diff --git a/Android.mk b/Android.mk
index 2d19e3d..88aad2b 100644
--- a/Android.mk
+++ b/Android.mk
@@ -36,27 +36,25 @@
 
 MY_LIBTEXTCLASSIFIER_CFLAGS := \
     $(MY_LIBTEXTCLASSIFIER_WARNING_CFLAGS) \
-    -fvisibility=hidden
+    -fvisibility=hidden \
+    -DLIBTEXTCLASSIFIER_UNILIB_ICU
 
 # Only enable debug logging in userdebug/eng builds.
 ifneq (,$(filter userdebug eng, $(TARGET_BUILD_VARIANT)))
   MY_LIBTEXTCLASSIFIER_CFLAGS += -DTC_DEBUG_LOGGING=1
 endif
 
-# ------------------------
-# libtextclassifier_protos
-# ------------------------
+# -----------------
+# flatbuffers
+# -----------------
 
+# Empty static library so that other projects can include just the basic
+# FlatBuffers headers as a module.
 include $(CLEAR_VARS)
-
-LOCAL_MODULE := libtextclassifier_protos
-
-LOCAL_STRIP_MODULE := $(LIBTEXTCLASSIFIER_STRIP_OPTS)
-
-LOCAL_SRC_FILES := $(call all-proto-files-under, .)
-LOCAL_SHARED_LIBRARIES := libprotobuf-cpp-lite
-
-LOCAL_CFLAGS := $(MY_LIBTEXTCLASSIFIER_WARNING_CFLAGS)
+LOCAL_MODULE := flatbuffers
+LOCAL_EXPORT_C_INCLUDES := $(LOCAL_PATH)/include
+LOCAL_EXPORT_CPPFLAGS := -std=c++11 -fexceptions -Wall \
+    -DFLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE
 
 include $(BUILD_STATIC_LIBRARY)
 
@@ -67,20 +65,18 @@
 include $(CLEAR_VARS)
 LOCAL_MODULE := libtextclassifier
 
-proto_sources_dir := $(generated_sources_dir)
-
 LOCAL_CPP_EXTENSION := .cc
 LOCAL_CFLAGS += $(MY_LIBTEXTCLASSIFIER_CFLAGS)
 LOCAL_STRIP_MODULE := $(LIBTEXTCLASSIFIER_STRIP_OPTS)
 
 LOCAL_SRC_FILES := $(filter-out tests/% %_test.cc,$(call all-subdir-cpp-files))
-LOCAL_C_INCLUDES += $(proto_sources_dir)/proto/external/libtextclassifier
+LOCAL_C_INCLUDES := $(TOP)/external/tensorflow $(TOP)/external/flatbuffers/include
 
-LOCAL_STATIC_LIBRARIES += libtextclassifier_protos
-LOCAL_SHARED_LIBRARIES += libprotobuf-cpp-lite
 LOCAL_SHARED_LIBRARIES += liblog
 LOCAL_SHARED_LIBRARIES += libicuuc libicui18n
-LOCAL_REQUIRED_MODULES := textclassifier.smartselection.en.model
+LOCAL_SHARED_LIBRARIES += libtflite
+LOCAL_STATIC_LIBRARIES += flatbuffers
+LOCAL_REQUIRED_MODULES := textclassifier.en.model
 
 LOCAL_ADDITIONAL_DEPENDENCIES += $(LOCAL_PATH)/jni.lds
 LOCAL_LDFLAGS += -Wl,-version-script=$(LOCAL_PATH)/jni.lds
@@ -101,162 +97,30 @@
 LOCAL_CFLAGS += $(MY_LIBTEXTCLASSIFIER_CFLAGS)
 LOCAL_STRIP_MODULE := $(LIBTEXTCLASSIFIER_STRIP_OPTS)
 
-LOCAL_TEST_DATA := $(call find-test-data-in-subdirs, $(LOCAL_PATH), *, tests/testdata)
+LOCAL_TEST_DATA := $(call find-test-data-in-subdirs, $(LOCAL_PATH), *, test_data)
 
-LOCAL_CPPFLAGS_32 += -DTEST_DATA_DIR="\"/data/nativetest/libtextclassifier_tests/tests/testdata/\""
-LOCAL_CPPFLAGS_64 += -DTEST_DATA_DIR="\"/data/nativetest64/libtextclassifier_tests/tests/testdata/\""
+LOCAL_CPPFLAGS_32 += -DLIBTEXTCLASSIFIER_TEST_DATA_DIR="\"/data/nativetest/libtextclassifier_tests/test_data/\""
+LOCAL_CPPFLAGS_64 += -DLIBTEXTCLASSIFIER_TEST_DATA_DIR="\"/data/nativetest64/libtextclassifier_tests/test_data/\""
 
 LOCAL_SRC_FILES := $(call all-subdir-cpp-files)
-LOCAL_C_INCLUDES += $(proto_sources_dir)/proto/external/libtextclassifier
+LOCAL_C_INCLUDES := $(TOP)/external/tensorflow $(TOP)/external/flatbuffers/include
 
-LOCAL_STATIC_LIBRARIES += libtextclassifier_protos libgmock
-LOCAL_SHARED_LIBRARIES += libprotobuf-cpp-lite
+LOCAL_STATIC_LIBRARIES += libgmock
 LOCAL_SHARED_LIBRARIES += liblog
 LOCAL_SHARED_LIBRARIES += libicuuc libicui18n
+LOCAL_SHARED_LIBRARIES += libtflite
 
 include $(BUILD_NATIVE_TEST)
 
-# ------------
-# LangId model
-# ------------
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.langid.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER := google
-LOCAL_SRC_FILES     := ./models/textclassifier.langid.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
 # ----------------------
 # Smart Selection models
 # ----------------------
 
 include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.ar.model
+LOCAL_MODULE        := textclassifier.en.model
 LOCAL_MODULE_CLASS  := ETC
 LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.ar.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.de.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.de.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.en.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.en.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.es.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.es.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.fr.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.fr.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.it.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.it.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.ja.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.ja.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.ko.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.ko.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.nl.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.nl.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.pl.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.pl.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.pt.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.pt.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.ru.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.ru.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.th.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.th.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.tr.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.tr.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.zh-Hant.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.zh-Hant.model
-LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
-include $(BUILD_PREBUILT)
-
-include $(CLEAR_VARS)
-LOCAL_MODULE        := textclassifier.smartselection.zh.model
-LOCAL_MODULE_CLASS  := ETC
-LOCAL_MODULE_OWNER  := google
-LOCAL_SRC_FILES     := ./models/textclassifier.smartselection.zh.model
+LOCAL_SRC_FILES     := ./models/textclassifier.en.model
 LOCAL_MODULE_PATH   := $(TARGET_OUT_ETC)/textclassifier
 include $(BUILD_PREBUILT)
 
@@ -265,10 +129,7 @@
 # -----------------------
 
 include $(CLEAR_VARS)
-LOCAL_MODULE           := textclassifier.smartselection.bundle1
-LOCAL_REQUIRED_MODULES := textclassifier.smartselection.en.model
-LOCAL_REQUIRED_MODULES += textclassifier.smartselection.es.model
-LOCAL_REQUIRED_MODULES += textclassifier.smartselection.de.model
-LOCAL_REQUIRED_MODULES += textclassifier.smartselection.fr.model
+LOCAL_MODULE           := textclassifier.bundle1
+LOCAL_REQUIRED_MODULES := textclassifier.en.model
 LOCAL_CFLAGS := $(MY_LIBTEXTCLASSIFIER_WARNING_CFLAGS)
 include $(BUILD_STATIC_LIBRARY)
diff --git a/cached-features.cc b/cached-features.cc
new file mode 100644
index 0000000..0b22d6d
--- /dev/null
+++ b/cached-features.cc
@@ -0,0 +1,177 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cached-features.h"
+
+#include "tensor-view.h"
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+
+namespace {
+
+// Populates the features for one token into the target vector at an offset
+// corresponding to the given token index. It builds the features to populate by
+// embedding the sparse features and combining them with the dense featues.
+// Embeds sparse features and the features of one token into the features
+// vector.
+bool PopulateTokenFeatures(int target_feature_index,
+                           const std::vector<int>& sparse_features,
+                           const std::vector<float>& dense_features,
+                           int feature_vector_size,
+                           EmbeddingExecutor* embedding_executor,
+                           std::vector<float>* target_features) {
+  const int sparse_embedding_size = feature_vector_size - dense_features.size();
+  float* dest =
+      target_features->data() + target_feature_index * feature_vector_size;
+
+  // Embed sparse features.
+  if (!embedding_executor->AddEmbedding(
+          TensorView<int>(sparse_features.data(),
+                          {static_cast<int>(sparse_features.size())}),
+          dest, sparse_embedding_size)) {
+    return false;
+  }
+
+  // Copy dense features.
+  for (int j = 0; j < dense_features.size(); ++j) {
+    dest[sparse_embedding_size + j] = dense_features[j];
+  }
+
+  return true;
+}
+
+}  // namespace
+
+CachedFeatures::CachedFeatures(
+    const TokenSpan& extraction_span,
+    const std::vector<std::vector<int>>& sparse_features,
+    const std::vector<std::vector<float>>& dense_features,
+    const std::vector<int>& padding_sparse_features,
+    const std::vector<float>& padding_dense_features,
+    const FeatureProcessorOptions_::BoundsSensitiveFeatures* config,
+    EmbeddingExecutor* embedding_executor, int feature_vector_size)
+    : extraction_span_(extraction_span), config_(config) {
+  int num_extracted_tokens = 0;
+  num_extracted_tokens += config->num_tokens_before();
+  num_extracted_tokens += config->num_tokens_inside_left();
+  num_extracted_tokens += config->num_tokens_inside_right();
+  num_extracted_tokens += config->num_tokens_after();
+  if (config->include_inside_bag()) {
+    ++num_extracted_tokens;
+  }
+  output_features_size_ = num_extracted_tokens * feature_vector_size;
+  if (config->include_inside_length()) {
+    ++output_features_size_;
+  }
+
+  features_.resize(feature_vector_size * TokenSpanSize(extraction_span));
+  for (int i = 0; i < TokenSpanSize(extraction_span); ++i) {
+    if (!PopulateTokenFeatures(/*target_feature_index=*/i, sparse_features[i],
+                               dense_features[i], feature_vector_size,
+                               embedding_executor, &features_)) {
+      TC_LOG(ERROR) << "Could not embed sparse token features.";
+      return;
+    }
+  }
+
+  padding_features_.resize(feature_vector_size);
+  if (!PopulateTokenFeatures(/*target_feature_index=*/0,
+                             padding_sparse_features, padding_dense_features,
+                             feature_vector_size, embedding_executor,
+                             &padding_features_)) {
+    TC_LOG(ERROR) << "Could not embed sparse padding token features.";
+    return;
+  }
+}
+
+std::vector<float> CachedFeatures::Get(TokenSpan selected_span) const {
+  selected_span.first -= extraction_span_.first;
+  selected_span.second -= extraction_span_.first;
+
+  std::vector<float> output_features;
+  output_features.reserve(output_features_size_);
+
+  // Append the features for tokens around the left bound. Masks out tokens
+  // after the right bound, so that if num_tokens_inside_left goes past it,
+  // padding tokens will be used.
+  AppendFeatures(
+      /*intended_span=*/{selected_span.first - config_->num_tokens_before(),
+                         selected_span.first +
+                             config_->num_tokens_inside_left()},
+      /*read_mask_span=*/{0, selected_span.second}, &output_features);
+
+  // Append the features for tokens around the right bound. Masks out tokens
+  // before the left bound, so that if num_tokens_inside_right goes past it,
+  // padding tokens will be used.
+  AppendFeatures(
+      /*intended_span=*/{selected_span.second -
+                             config_->num_tokens_inside_right(),
+                         selected_span.second + config_->num_tokens_after()},
+      /*read_mask_span=*/{selected_span.first, TokenSpanSize(extraction_span_)},
+      &output_features);
+
+  if (config_->include_inside_bag()) {
+    AppendSummedFeatures(selected_span, &output_features);
+  }
+
+  if (config_->include_inside_length()) {
+    output_features.push_back(static_cast<float>(TokenSpanSize(selected_span)));
+  }
+
+  return output_features;
+}
+
+void CachedFeatures::AppendFeatures(const TokenSpan& intended_span,
+                                    const TokenSpan& read_mask_span,
+                                    std::vector<float>* output_features) const {
+  const TokenSpan copy_span =
+      IntersectTokenSpans(intended_span, read_mask_span);
+  for (int i = intended_span.first; i < copy_span.first; ++i) {
+    AppendPaddingFeatures(output_features);
+  }
+  output_features->insert(
+      output_features->end(),
+      features_.begin() + copy_span.first * NumFeaturesPerToken(),
+      features_.begin() + copy_span.second * NumFeaturesPerToken());
+  for (int i = copy_span.second; i < intended_span.second; ++i) {
+    AppendPaddingFeatures(output_features);
+  }
+}
+
+void CachedFeatures::AppendPaddingFeatures(
+    std::vector<float>* output_features) const {
+  output_features->insert(output_features->end(), padding_features_.begin(),
+                          padding_features_.end());
+}
+
+void CachedFeatures::AppendSummedFeatures(
+    const TokenSpan& summing_span, std::vector<float>* output_features) const {
+  const int offset = output_features->size();
+  output_features->resize(output_features->size() + NumFeaturesPerToken());
+  for (int i = summing_span.first; i < summing_span.second; ++i) {
+    for (int j = 0; j < NumFeaturesPerToken(); ++j) {
+      (*output_features)[offset + j] +=
+          features_[i * NumFeaturesPerToken() + j];
+    }
+  }
+}
+
+int CachedFeatures::NumFeaturesPerToken() const {
+  return padding_features_.size();
+}
+
+}  // namespace libtextclassifier2
diff --git a/cached-features.h b/cached-features.h
new file mode 100644
index 0000000..5ffb9a9
--- /dev/null
+++ b/cached-features.h
@@ -0,0 +1,74 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_CACHED_FEATURES_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_CACHED_FEATURES_H_
+
+#include <memory>
+#include <vector>
+
+#include "model-executor.h"
+#include "model_generated.h"
+#include "types.h"
+
+namespace libtextclassifier2 {
+
+// Holds state for extracting features across multiple calls and reusing them.
+// Assumes that features for each Token are independent.
+class CachedFeatures {
+ public:
+  CachedFeatures(
+      const TokenSpan& extraction_span,
+      const std::vector<std::vector<int>>& sparse_features,
+      const std::vector<std::vector<float>>& dense_features,
+      const std::vector<int>& padding_sparse_features,
+      const std::vector<float>& padding_dense_features,
+      const FeatureProcessorOptions_::BoundsSensitiveFeatures* config,
+      EmbeddingExecutor* embedding_executor, int feature_vector_size);
+
+  // Gets a vector of features for the given token span.
+  std::vector<float> Get(TokenSpan selected_span) const;
+
+ private:
+  // Appends token features to the output. The intended_span specifies which
+  // tokens' features should be used in principle. The read_mask_span restricts
+  // which tokens are actually read. For tokens outside of the read_mask_span,
+  // padding tokens are used instead.
+  void AppendFeatures(const TokenSpan& intended_span,
+                      const TokenSpan& read_mask_span,
+                      std::vector<float>* output_features) const;
+
+  // Appends features of one padding token to the output.
+  void AppendPaddingFeatures(std::vector<float>* output_features) const;
+
+  // Appends the features of tokens from the given span to the output. The
+  // features are summed so that the appended features have the size
+  // corresponding to one token.
+  void AppendSummedFeatures(const TokenSpan& summing_span,
+                            std::vector<float>* output_features) const;
+
+  int NumFeaturesPerToken() const;
+
+  const TokenSpan extraction_span_;
+  const FeatureProcessorOptions_::BoundsSensitiveFeatures* config_;
+  int output_features_size_;
+  std::vector<float> features_;
+  std::vector<float> padding_features_;
+};
+
+}  // namespace libtextclassifier2
+
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_CACHED_FEATURES_H_
diff --git a/cached-features_test.cc b/cached-features_test.cc
new file mode 100644
index 0000000..2412ff3
--- /dev/null
+++ b/cached-features_test.cc
@@ -0,0 +1,121 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cached-features.h"
+
+#include "model-executor.h"
+#include "tensor-view.h"
+
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+
+using testing::ElementsAreArray;
+using testing::FloatEq;
+using testing::Matcher;
+
+namespace libtextclassifier2 {
+namespace {
+
+Matcher<std::vector<float>> ElementsAreFloat(const std::vector<float>& values) {
+  std::vector<Matcher<float>> matchers;
+  for (const float value : values) {
+    matchers.push_back(FloatEq(value));
+  }
+  return ElementsAreArray(matchers);
+}
+
+// EmbeddingExecutor that always returns features based on
+class FakeEmbeddingExecutor : public EmbeddingExecutor {
+ public:
+  bool AddEmbedding(const TensorView<int>& sparse_features, float* dest,
+                    int dest_size) override {
+    TC_CHECK_GE(dest_size, 2);
+    EXPECT_EQ(sparse_features.size(), 1);
+
+    dest[0] = sparse_features.data()[0] * 11.0f;
+    dest[1] = -sparse_features.data()[0] * 11.0f;
+    return true;
+  }
+
+ private:
+  std::vector<float> storage_;
+};
+
+TEST(CachedFeaturesTest, Simple) {
+  FeatureProcessorOptions_::BoundsSensitiveFeaturesT config;
+  config.enabled = true;
+  config.num_tokens_before = 2;
+  config.num_tokens_inside_left = 2;
+  config.num_tokens_inside_right = 2;
+  config.num_tokens_after = 2;
+  config.include_inside_bag = true;
+  config.include_inside_length = true;
+  flatbuffers::FlatBufferBuilder builder;
+  builder.Finish(CreateBoundsSensitiveFeatures(builder, &config));
+  flatbuffers::DetachedBuffer config_fb = builder.Release();
+
+  std::vector<std::vector<int>> sparse_features(9);
+  for (int i = 0; i < sparse_features.size(); ++i) {
+    sparse_features[i].push_back(i + 1);
+  }
+  std::vector<std::vector<float>> dense_features(9);
+  for (int i = 0; i < dense_features.size(); ++i) {
+    dense_features[i].push_back((i + 1) * 0.1);
+  }
+
+  std::vector<int> padding_sparse_features = {10203};
+  std::vector<float> padding_dense_features = {321.0};
+
+  FakeEmbeddingExecutor executor;
+  const CachedFeatures cached_features(
+      {3, 9}, sparse_features, dense_features, padding_sparse_features,
+      padding_dense_features,
+      flatbuffers::GetRoot<FeatureProcessorOptions_::BoundsSensitiveFeatures>(
+          config_fb.data()),
+      &executor, /*feature_vector_size=*/3);
+
+  EXPECT_THAT(cached_features.Get({5, 8}),
+              ElementsAreFloat({11.0,  -11.0,  0.1, 22.0,     -22.0,     0.2,
+                                33.0,  -33.0,  0.3, 44.0,     -44.0,     0.4,
+                                44.0,  -44.0,  0.4, 55.0,     -55.0,     0.5,
+                                66.0,  -66.0,  0.6, 112233.0, -112233.0, 321.0,
+                                132.0, -132.0, 1.2, 3.0}));
+
+  EXPECT_THAT(
+      cached_features.Get({5, 7}),
+      ElementsAreFloat({11.0,  -11.0, 0.1,   22.0,  -22.0, 0.2,   33.0,
+                        -33.0, 0.3,   44.0,  -44.0, 0.4,   33.0,  -33.0,
+                        0.3,   44.0,  -44.0, 0.4,   55.0,  -55.0, 0.5,
+                        66.0,  -66.0, 0.6,   77.0,  -77.0, 0.7,   2.0}));
+
+  EXPECT_THAT(
+      cached_features.Get({6, 8}),
+      ElementsAreFloat({22.0,     -22.0,     0.2,   33.0,  -33.0, 0.3,   44.0,
+                        -44.0,    0.4,       55.0,  -55.0, 0.5,   44.0,  -44.0,
+                        0.4,      55.0,      -55.0, 0.5,   66.0,  -66.0, 0.6,
+                        112233.0, -112233.0, 321.0, 99.0,  -99.0, 0.9,   2.0}));
+
+  EXPECT_THAT(
+      cached_features.Get({6, 7}),
+      ElementsAreFloat({22.0,     -22.0,     0.2,   33.0,     -33.0,     0.3,
+                        44.0,     -44.0,     0.4,   112233.0, -112233.0, 321.0,
+                        112233.0, -112233.0, 321.0, 44.0,     -44.0,     0.4,
+                        55.0,     -55.0,     0.5,   66.0,     -66.0,     0.6,
+                        44.0,     -44.0,     0.4,   1.0}));
+}
+
+}  // namespace
+}  // namespace libtextclassifier2
diff --git a/common/algorithm.h b/common/algorithm.h
deleted file mode 100644
index 365eec9..0000000
--- a/common/algorithm.h
+++ /dev/null
@@ -1,49 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Generic utils similar to those from the C++ header <algorithm>.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_ALGORITHM_H_
-#define LIBTEXTCLASSIFIER_COMMON_ALGORITHM_H_
-
-#include <algorithm>
-#include <vector>
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// Returns index of max element from the vector |elements|.  Returns 0 if
-// |elements| is empty.  T should be a type that can be compared by operator<.
-template<typename T>
-inline int GetArgMax(const std::vector<T> &elements) {
-  return std::distance(
-      elements.begin(),
-      std::max_element(elements.begin(), elements.end()));
-}
-
-// Returns index of min element from the vector |elements|.  Returns 0 if
-// |elements| is empty.  T should be a type that can be compared by operator<.
-template<typename T>
-inline int GetArgMin(const std::vector<T> &elements) {
-  return std::distance(
-      elements.begin(),
-      std::min_element(elements.begin(), elements.end()));
-}
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_ALGORITHM_H_
diff --git a/common/embedding-feature-extractor.cc b/common/embedding-feature-extractor.cc
deleted file mode 100644
index 254af45..0000000
--- a/common/embedding-feature-extractor.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/embedding-feature-extractor.h"
-
-#include <stddef.h>
-
-#include <vector>
-
-#include "common/feature-extractor.h"
-#include "common/feature-types.h"
-#include "common/task-context.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/strings/numbers.h"
-#include "util/strings/split.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-bool GenericEmbeddingFeatureExtractor::Init(TaskContext *context) {
-  // Don't use version to determine how to get feature FML.
-  const std::string features = context->Get(GetParamName("features"), "");
-  TC_LOG(INFO) << "Features: " << features;
-
-  const std::string embedding_names =
-      context->Get(GetParamName("embedding_names"), "");
-  TC_LOG(INFO) << "Embedding names: " << embedding_names;
-
-  const std::string embedding_dims =
-      context->Get(GetParamName("embedding_dims"), "");
-  TC_LOG(INFO) << "Embedding dims: " << embedding_dims;
-
-  embedding_fml_ = strings::Split(features, ';');
-  embedding_names_ = strings::Split(embedding_names, ';');
-  for (const std::string &dim : strings::Split(embedding_dims, ';')) {
-    int32 parsed_dim = 0;
-    if (!ParseInt32(dim.c_str(), &parsed_dim)) {
-      TC_LOG(ERROR) << "Unable to parse dim " << dim;
-      return false;
-    }
-    embedding_dims_.push_back(parsed_dim);
-  }
-  if ((embedding_fml_.size() != embedding_names_.size()) ||
-      (embedding_fml_.size() != embedding_dims_.size())) {
-    TC_LOG(ERROR) << "Mismatch: #fml specs = " << embedding_fml_.size()
-                  << "; #names = " << embedding_names_.size()
-                  << "; #dims = " << embedding_dims_.size();
-    return false;
-  }
-  return true;
-}
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/embedding-feature-extractor.h b/common/embedding-feature-extractor.h
deleted file mode 100644
index 0efd0d2..0000000
--- a/common/embedding-feature-extractor.h
+++ /dev/null
@@ -1,197 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_EMBEDDING_FEATURE_EXTRACTOR_H_
-#define LIBTEXTCLASSIFIER_COMMON_EMBEDDING_FEATURE_EXTRACTOR_H_
-
-#include <memory>
-#include <string>
-#include <vector>
-
-#include "common/feature-extractor.h"
-#include "common/task-context.h"
-#include "common/workspace.h"
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// An EmbeddingFeatureExtractor manages the extraction of features for
-// embedding-based models. It wraps a sequence of underlying classes of feature
-// extractors, along with associated predicate maps. Each class of feature
-// extractors is associated with a name, e.g., "words", "labels", "tags".
-//
-// The class is split between a generic abstract version,
-// GenericEmbeddingFeatureExtractor (that can be initialized without knowing the
-// signature of the ExtractFeatures method) and a typed version.
-//
-// The predicate maps must be initialized before use: they can be loaded using
-// Read() or updated via UpdateMapsForExample.
-class GenericEmbeddingFeatureExtractor {
- public:
-  GenericEmbeddingFeatureExtractor() {}
-  virtual ~GenericEmbeddingFeatureExtractor() {}
-
-  // Get the prefix std::string to put in front of all arguments, so they don't
-  // conflict with other embedding models.
-  virtual const std::string ArgPrefix() const = 0;
-
-  // Initializes predicate maps and embedding space names that are common for
-  // all embedding-based feature extractors.
-  virtual bool Init(TaskContext *context);
-
-  // Requests workspace for the underlying feature extractors. This is
-  // implemented in the typed class.
-  virtual void RequestWorkspaces(WorkspaceRegistry *registry) = 0;
-
-  // Returns number of embedding spaces.
-  int NumEmbeddings() const { return embedding_dims_.size(); }
-
-  // Number of predicates for the embedding at a given index (vocabulary size).
-  // Returns -1 if index is out of bounds.
-  int EmbeddingSize(int index) const {
-    const GenericFeatureExtractor *extractor = generic_feature_extractor(index);
-    return (extractor == nullptr) ? -1 : extractor->GetDomainSize();
-  }
-
-  // Returns the dimensionality of the embedding space.
-  int EmbeddingDims(int index) const { return embedding_dims_[index]; }
-
-  // Accessor for embedding dims (dimensions of the embedding spaces).
-  const std::vector<int> &embedding_dims() const { return embedding_dims_; }
-
-  const std::vector<std::string> &embedding_fml() const {
-    return embedding_fml_;
-  }
-
-  // Get parameter name by concatenating the prefix and the original name.
-  std::string GetParamName(const std::string &param_name) const {
-    std::string full_name = ArgPrefix();
-    full_name.push_back('_');
-    full_name.append(param_name);
-    return full_name;
-  }
-
- protected:
-  // Provides the generic class with access to the templated extractors. This is
-  // used to get the type information out of the feature extractor without
-  // knowing the specific calling arguments of the extractor itself.
-  // Returns nullptr for an out-of-bounds idx.
-  virtual const GenericFeatureExtractor *generic_feature_extractor(
-      int idx) const = 0;
-
- private:
-  // Embedding space names for parameter sharing.
-  std::vector<std::string> embedding_names_;
-
-  // FML strings for each feature extractor.
-  std::vector<std::string> embedding_fml_;
-
-  // Size of each of the embedding spaces (maximum predicate id).
-  std::vector<int> embedding_sizes_;
-
-  // Embedding dimensions of the embedding spaces (i.e. 32, 64 etc.)
-  std::vector<int> embedding_dims_;
-
-  TC_DISALLOW_COPY_AND_ASSIGN(GenericEmbeddingFeatureExtractor);
-};
-
-// Templated, object-specific implementation of the
-// EmbeddingFeatureExtractor. EXTRACTOR should be a FeatureExtractor<OBJ,
-// ARGS...> class that has the appropriate FeatureTraits() to ensure that
-// locator type features work.
-//
-// Note: for backwards compatibility purposes, this always reads the FML spec
-// from "<prefix>_features".
-template <class EXTRACTOR, class OBJ, class... ARGS>
-class EmbeddingFeatureExtractor : public GenericEmbeddingFeatureExtractor {
- public:
-  // Initializes all predicate maps, feature extractors, etc.
-  bool Init(TaskContext *context) override {
-    if (!GenericEmbeddingFeatureExtractor::Init(context)) {
-      return false;
-    }
-    feature_extractors_.resize(embedding_fml().size());
-    for (int i = 0; i < embedding_fml().size(); ++i) {
-      feature_extractors_[i].reset(new EXTRACTOR());
-      if (!feature_extractors_[i]->Parse(embedding_fml()[i])) {
-        return false;
-      }
-      if (!feature_extractors_[i]->Setup(context)) {
-        return false;
-      }
-    }
-    for (auto &feature_extractor : feature_extractors_) {
-      if (!feature_extractor->Init(context)) {
-        return false;
-      }
-    }
-    return true;
-  }
-
-  // Requests workspaces from the registry. Must be called after Init(), and
-  // before Preprocess().
-  void RequestWorkspaces(WorkspaceRegistry *registry) override {
-    for (auto &feature_extractor : feature_extractors_) {
-      feature_extractor->RequestWorkspaces(registry);
-    }
-  }
-
-  // Must be called on the object one state for each sentence, before any
-  // feature extraction (e.g., UpdateMapsForExample, ExtractFeatures).
-  void Preprocess(WorkspaceSet *workspaces, OBJ *obj) const {
-    for (auto &feature_extractor : feature_extractors_) {
-      feature_extractor->Preprocess(workspaces, obj);
-    }
-  }
-
-  // Extracts features using the extractors. Note that features must already
-  // be initialized to the correct number of feature extractors. No predicate
-  // mapping is applied.
-  void ExtractFeatures(const WorkspaceSet &workspaces, const OBJ &obj,
-                       ARGS... args,
-                       std::vector<FeatureVector> *features) const {
-    TC_DCHECK(features != nullptr);
-    TC_DCHECK_EQ(features->size(), feature_extractors_.size());
-    for (int i = 0; i < feature_extractors_.size(); ++i) {
-      (*features)[i].clear();
-      feature_extractors_[i]->ExtractFeatures(workspaces, obj, args...,
-                                              &(*features)[i]);
-    }
-  }
-
- protected:
-  // Provides generic access to the feature extractors.
-  const GenericFeatureExtractor *generic_feature_extractor(
-      int idx) const override {
-    if ((idx < 0) || (idx >= feature_extractors_.size())) {
-      TC_LOG(ERROR) << "Out of bounds index " << idx;
-      TC_DCHECK(false);  // Crash in debug mode.
-      return nullptr;
-    }
-    return feature_extractors_[idx].get();
-  }
-
- private:
-  // Templated feature extractor class.
-  std::vector<std::unique_ptr<EXTRACTOR>> feature_extractors_;
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_EMBEDDING_FEATURE_EXTRACTOR_H_
diff --git a/common/embedding-feature-extractor_test.cc b/common/embedding-feature-extractor_test.cc
deleted file mode 100644
index c5ed627..0000000
--- a/common/embedding-feature-extractor_test.cc
+++ /dev/null
@@ -1,142 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/embedding-feature-extractor.h"
-
-#include "lang_id/language-identifier-features.h"
-#include "lang_id/light-sentence-features.h"
-#include "lang_id/light-sentence.h"
-#include "lang_id/relevant-script-feature.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-class EmbeddingFeatureExtractorTest : public ::testing::Test {
- public:
-  void SetUp() override {
-    // Make sure all relevant features are registered:
-    lang_id::ContinuousBagOfNgramsFunction::RegisterClass();
-    lang_id::RelevantScriptFeature::RegisterClass();
-  }
-};
-
-// Specialization of EmbeddingFeatureExtractor that extracts from LightSentence.
-class TestEmbeddingFeatureExtractor
-    : public EmbeddingFeatureExtractor<lang_id::LightSentenceExtractor,
-                                       lang_id::LightSentence> {
- public:
-  const std::string ArgPrefix() const override { return "test"; }
-};
-
-TEST_F(EmbeddingFeatureExtractorTest, NoEmbeddingSpaces) {
-  TaskContext context;
-  context.SetParameter("test_features", "");
-  context.SetParameter("test_embedding_names", "");
-  context.SetParameter("test_embedding_dims", "");
-  TestEmbeddingFeatureExtractor tefe;
-  ASSERT_TRUE(tefe.Init(&context));
-  EXPECT_EQ(tefe.NumEmbeddings(), 0);
-}
-
-TEST_F(EmbeddingFeatureExtractorTest, GoodSpec) {
-  TaskContext context;
-  const std::string spec =
-      "continuous-bag-of-ngrams(id_dim=5000,size=3);"
-      "continuous-bag-of-ngrams(id_dim=7000,size=4)";
-  context.SetParameter("test_features", spec);
-  context.SetParameter("test_embedding_names", "trigram;quadgram");
-  context.SetParameter("test_embedding_dims", "16;24");
-  TestEmbeddingFeatureExtractor tefe;
-  ASSERT_TRUE(tefe.Init(&context));
-  EXPECT_EQ(tefe.NumEmbeddings(), 2);
-  EXPECT_EQ(tefe.EmbeddingSize(0), 5000);
-  EXPECT_EQ(tefe.EmbeddingDims(0), 16);
-  EXPECT_EQ(tefe.EmbeddingSize(1), 7000);
-  EXPECT_EQ(tefe.EmbeddingDims(1), 24);
-}
-
-TEST_F(EmbeddingFeatureExtractorTest, MissmatchFmlVsNames) {
-  TaskContext context;
-  const std::string spec =
-      "continuous-bag-of-ngrams(id_dim=5000,size=3);"
-      "continuous-bag-of-ngrams(id_dim=7000,size=4)";
-  context.SetParameter("test_features", spec);
-  context.SetParameter("test_embedding_names", "trigram");
-  context.SetParameter("test_embedding_dims", "16;16");
-  TestEmbeddingFeatureExtractor tefe;
-  ASSERT_FALSE(tefe.Init(&context));
-}
-
-TEST_F(EmbeddingFeatureExtractorTest, MissmatchFmlVsDims) {
-  TaskContext context;
-  const std::string spec =
-      "continuous-bag-of-ngrams(id_dim=5000,size=3);"
-      "continuous-bag-of-ngrams(id_dim=7000,size=4)";
-  context.SetParameter("test_features", spec);
-  context.SetParameter("test_embedding_names", "trigram;quadgram");
-  context.SetParameter("test_embedding_dims", "16;16;32");
-  TestEmbeddingFeatureExtractor tefe;
-  ASSERT_FALSE(tefe.Init(&context));
-}
-
-TEST_F(EmbeddingFeatureExtractorTest, BrokenSpec) {
-  TaskContext context;
-  const std::string spec =
-      "continuous-bag-of-ngrams(id_dim=5000;"
-      "continuous-bag-of-ngrams(id_dim=7000,size=4)";
-  context.SetParameter("test_features", spec);
-  context.SetParameter("test_embedding_names", "trigram;quadgram");
-  context.SetParameter("test_embedding_dims", "16;16");
-  TestEmbeddingFeatureExtractor tefe;
-  ASSERT_FALSE(tefe.Init(&context));
-}
-
-TEST_F(EmbeddingFeatureExtractorTest, MissingFeature) {
-  TaskContext context;
-  const std::string spec =
-      "continuous-bag-of-ngrams(id_dim=5000,size=3);"
-      "no-such-feature";
-  context.SetParameter("test_features", spec);
-  context.SetParameter("test_embedding_names", "trigram;foo");
-  context.SetParameter("test_embedding_dims", "16;16");
-  TestEmbeddingFeatureExtractor tefe;
-  ASSERT_FALSE(tefe.Init(&context));
-}
-
-TEST_F(EmbeddingFeatureExtractorTest, MultipleFeatures) {
-  TaskContext context;
-  const std::string spec =
-      "continuous-bag-of-ngrams(id_dim=1000,size=3);"
-      "continuous-bag-of-relevant-scripts";
-  context.SetParameter("test_features", spec);
-  context.SetParameter("test_embedding_names", "trigram;script");
-  context.SetParameter("test_embedding_dims", "8;16");
-  TestEmbeddingFeatureExtractor tefe;
-  ASSERT_TRUE(tefe.Init(&context));
-  EXPECT_EQ(tefe.NumEmbeddings(), 2);
-  EXPECT_EQ(tefe.EmbeddingSize(0), 1000);
-  EXPECT_EQ(tefe.EmbeddingDims(0), 8);
-
-  // continuous-bag-of-relevant-scripts has its own hard-wired vocabulary size.
-  // We don't want this test to depend on that value; we just check it's bigger
-  // than 0.
-  EXPECT_GT(tefe.EmbeddingSize(1), 0);
-  EXPECT_EQ(tefe.EmbeddingDims(1), 16);
-}
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/embedding-network-package.proto b/common/embedding-network-package.proto
deleted file mode 100644
index 54d47e6..0000000
--- a/common/embedding-network-package.proto
+++ /dev/null
@@ -1,29 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// This file defines TaskSpec as an extension to EmbeddingNetworkProto. The
-// definition is done here rather than directly in the imported protos to keep
-// the different messages as independent as possible.
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-import "external/libtextclassifier/common/task-spec.proto";
-import "external/libtextclassifier/common/embedding-network.proto";
-
-package libtextclassifier.nlp_core;
-
-extend EmbeddingNetworkProto {
-  optional TaskSpec task_spec_in_embedding_network_proto = 129692954;
-}
diff --git a/common/embedding-network-params-from-proto.h b/common/embedding-network-params-from-proto.h
deleted file mode 100644
index 2f2c429..0000000
--- a/common/embedding-network-params-from-proto.h
+++ /dev/null
@@ -1,245 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_PARAMS_FROM_PROTO_H_
-#define LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_PARAMS_FROM_PROTO_H_
-
-#include <algorithm>
-#include <memory>
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "common/embedding-network-package.pb.h"
-#include "common/embedding-network-params.h"
-#include "common/embedding-network.pb.h"
-#include "common/float16.h"
-#include "common/little-endian-data.h"
-#include "common/task-context.h"
-#include "common/task-spec.pb.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// A wrapper class that owns and exposes an EmbeddingNetworkProto message via
-// the EmbeddingNetworkParams interface.
-//
-// The EmbeddingNetworkParams interface encapsulates the weight matrices of the
-// embeddings, hidden and softmax layers as transposed versions of their
-// counterparts in the original EmbeddingNetworkProto. The matrices in the proto
-// passed to this class' constructor must likewise already have been transposed.
-// See embedding-network-params.h for details.
-class EmbeddingNetworkParamsFromProto : public EmbeddingNetworkParams {
- public:
-  // Constructor that takes ownership of the provided proto. See class-comment
-  // for the requirements that certain weight matrices must satisfy.
-  explicit EmbeddingNetworkParamsFromProto(
-      std::unique_ptr<EmbeddingNetworkProto> proto)
-      : proto_(std::move(proto)) {
-    valid_ = true;
-
-    // Initialize these vectors to have the required number of elements
-    // regardless of quantization status. This is to support the unlikely case
-    // where only some embeddings are quantized, along with the fact that
-    // EmbeddingNetworkParams interface accesses them by index.
-    embeddings_quant_scales_.resize(proto_->embeddings_size());
-    embeddings_quant_weights_.resize(proto_->embeddings_size());
-    for (int i = 0; i < proto_->embeddings_size(); ++i) {
-      MatrixParams *embedding = proto_->mutable_embeddings()->Mutable(i);
-      if (!embedding->is_quantized()) {
-        continue;
-      }
-
-      bool success = FillVectorFromDataBytesInLittleEndian(
-          embedding->bytes_for_quantized_values(),
-          embedding->rows() * embedding->cols(),
-          &(embeddings_quant_weights_[i]));
-      if (!success) {
-        TC_LOG(ERROR) << "Problem decoding quant_weights for embeddings #" << i;
-        valid_ = false;
-      }
-
-      // The repeated field bytes_for_quantized_values uses a lot of memory.
-      // Since it's no longer necessary (and we own the proto), we clear it.
-      embedding->clear_bytes_for_quantized_values();
-
-      success = FillVectorFromDataBytesInLittleEndian(
-          embedding->bytes_for_col_scales(),
-          embedding->rows(),
-          &(embeddings_quant_scales_[i]));
-      if (!success) {
-        TC_LOG(ERROR) << "Problem decoding col_scales for embeddings #" << i;
-        valid_ = false;
-      }
-
-      // See comments for clear_bytes_for_quantized_values().
-      embedding->clear_bytes_for_col_scales();
-    }
-  }
-
-  const TaskSpec *GetTaskSpec() override {
-    if (!proto_) {
-      return nullptr;
-    }
-    auto extension_id = task_spec_in_embedding_network_proto;
-    if (proto_->HasExtension(extension_id)) {
-      return &(proto_->GetExtension(extension_id));
-    } else {
-      TC_LOG(ERROR) << "Unable to get TaskSpec from EmbeddingNetworkProto";
-      return nullptr;
-    }
-  }
-
-  // Returns true if these params are valid.  False otherwise (e.g., if the
-  // original proto data was corrupted).
-  bool is_valid() { return valid_; }
-
- protected:
-  int embeddings_size() const override { return proto_->embeddings_size(); }
-
-  int embeddings_num_rows(int i) const override {
-    TC_DCHECK(InRange(i, embeddings_size()));
-    return proto_->embeddings(i).rows();
-  }
-
-  int embeddings_num_cols(int i) const override {
-    TC_DCHECK(InRange(i, embeddings_size()));
-    return proto_->embeddings(i).cols();
-  }
-
-  const void *embeddings_weights(int i) const override {
-    TC_DCHECK(InRange(i, embeddings_size()));
-    if (proto_->embeddings(i).is_quantized()) {
-      return static_cast<const void *>(embeddings_quant_weights_.at(i).data());
-    } else {
-      return static_cast<const void *>(proto_->embeddings(i).value().data());
-    }
-  }
-
-  QuantizationType embeddings_quant_type(int i) const override {
-    TC_DCHECK(InRange(i, embeddings_size()));
-    return proto_->embeddings(i).is_quantized() ? QuantizationType::UINT8
-                                                : QuantizationType::NONE;
-  }
-
-  const float16 *embeddings_quant_scales(int i) const override {
-    TC_DCHECK(InRange(i, embeddings_size()));
-    return proto_->embeddings(i).is_quantized()
-               ? embeddings_quant_scales_.at(i).data()
-               : nullptr;
-  }
-
-  int hidden_size() const override { return proto_->hidden_size(); }
-
-  int hidden_num_rows(int i) const override {
-    TC_DCHECK(InRange(i, hidden_size()));
-    return proto_->hidden(i).rows();
-  }
-
-  int hidden_num_cols(int i) const override {
-    TC_DCHECK(InRange(i, hidden_size()));
-    return proto_->hidden(i).cols();
-  }
-
-  const void *hidden_weights(int i) const override {
-    TC_DCHECK(InRange(i, hidden_size()));
-    return proto_->hidden(i).value().data();
-  }
-
-  int hidden_bias_size() const override { return proto_->hidden_bias_size(); }
-
-  int hidden_bias_num_rows(int i) const override {
-    TC_DCHECK(InRange(i, hidden_bias_size()));
-    return proto_->hidden_bias(i).rows();
-  }
-
-  int hidden_bias_num_cols(int i) const override {
-    TC_DCHECK(InRange(i, hidden_bias_size()));
-    return proto_->hidden_bias(i).cols();
-  }
-
-  const void *hidden_bias_weights(int i) const override {
-    TC_DCHECK(InRange(i, hidden_bias_size()));
-    return proto_->hidden_bias(i).value().data();
-  }
-
-  int softmax_size() const override { return proto_->has_softmax() ? 1 : 0; }
-
-  int softmax_num_rows(int i) const override {
-    TC_DCHECK(InRange(i, softmax_size()));
-    return proto_->has_softmax() ? proto_->softmax().rows() : 0;
-  }
-
-  int softmax_num_cols(int i) const override {
-    TC_DCHECK(InRange(i, softmax_size()));
-    return proto_->has_softmax() ? proto_->softmax().cols() : 0;
-  }
-
-  const void *softmax_weights(int i) const override {
-    TC_DCHECK(InRange(i, softmax_size()));
-    return proto_->has_softmax() ? proto_->softmax().value().data() : nullptr;
-  }
-
-  int softmax_bias_size() const override {
-    return proto_->has_softmax_bias() ? 1 : 0;
-  }
-
-  int softmax_bias_num_rows(int i) const override {
-    TC_DCHECK(InRange(i, softmax_bias_size()));
-    return proto_->has_softmax_bias() ? proto_->softmax_bias().rows() : 0;
-  }
-
-  int softmax_bias_num_cols(int i) const override {
-    TC_DCHECK(InRange(i, softmax_bias_size()));
-    return proto_->has_softmax_bias() ? proto_->softmax_bias().cols() : 0;
-  }
-
-  const void *softmax_bias_weights(int i) const override {
-    TC_DCHECK(InRange(i, softmax_bias_size()));
-    return proto_->has_softmax_bias() ? proto_->softmax_bias().value().data()
-                                      : nullptr;
-  }
-
-  int embedding_num_features_size() const override {
-    return proto_->embedding_num_features_size();
-  }
-
-  int embedding_num_features(int i) const override {
-    TC_DCHECK(InRange(i, embedding_num_features_size()));
-    return proto_->embedding_num_features(i);
-  }
-
- private:
-  std::unique_ptr<EmbeddingNetworkProto> proto_;
-
-  // True if these params are valid.  May be false if the original proto was
-  // corrupted.  We prefer to set this to false to CHECK-failing.
-  bool valid_;
-
-  // When the embeddings are quantized, these members are used to store their
-  // numeric values using the types expected by the rest of the class. Due to
-  // technical reasons, the proto stores this info using larger types (i.e.,
-  // more bits).
-  std::vector<std::vector<float16>> embeddings_quant_scales_;
-  std::vector<std::vector<uint8>> embeddings_quant_weights_;
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_PARAMS_FROM_PROTO_H_
diff --git a/common/embedding-network-params.h b/common/embedding-network-params.h
deleted file mode 100755
index ee2d9dc..0000000
--- a/common/embedding-network-params.h
+++ /dev/null
@@ -1,325 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_PARAMS_H_
-#define LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_PARAMS_H_
-
-#include <algorithm>
-#include <string>
-
-#include "common/float16.h"
-#include "common/task-context.h"
-#include "common/task-spec.pb.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-enum class QuantizationType { NONE = 0, UINT8 };
-
-// API for accessing parameters for a feed-forward neural network with
-// embeddings.
-//
-// Note: this API is closely related to embedding-network.proto.  The reason we
-// have a separate API is that the proto may not be the only way of packaging
-// these parameters.
-class EmbeddingNetworkParams {
- public:
-  virtual ~EmbeddingNetworkParams() {}
-
-  // **** High-level API.
-
-  // Simple representation of a matrix.  This small struct that doesn't own any
-  // resource intentionally supports copy / assign, to simplify our APIs.
-  struct Matrix {
-    // Number of rows.
-    int rows;
-
-    // Number of columns.
-    int cols;
-
-    QuantizationType quant_type;
-
-    // Pointer to matrix elements, in row-major order
-    // (https://en.wikipedia.org/wiki/Row-major_order) Not owned.
-    const void *elements;
-
-    // Quantization scales: one scale for each row.
-    const float16 *quant_scales;
-  };
-
-  // Returns number of embedding spaces.
-  int GetNumEmbeddingSpaces() const {
-    if (embeddings_size() != embedding_num_features_size()) {
-      TC_LOG(ERROR) << "Embedding spaces mismatch " << embeddings_size()
-                    << " != " << embedding_num_features_size();
-    }
-    return std::max(0,
-                    std::min(embeddings_size(), embedding_num_features_size()));
-  }
-
-  // Returns embedding matrix for the i-th embedding space.
-  //
-  // NOTE: i must be in [0, GetNumEmbeddingSpaces()).  Undefined behavior
-  // otherwise.
-  Matrix GetEmbeddingMatrix(int i) const {
-    TC_DCHECK(InRange(i, embeddings_size()));
-    Matrix matrix;
-    matrix.rows = embeddings_num_rows(i);
-    matrix.cols = embeddings_num_cols(i);
-    matrix.elements = embeddings_weights(i);
-    matrix.quant_type = embeddings_quant_type(i);
-    matrix.quant_scales = embeddings_quant_scales(i);
-    return matrix;
-  }
-
-  // Returns number of features in i-th embedding space.
-  //
-  // NOTE: i must be in [0, GetNumEmbeddingSpaces()).  Undefined behavior
-  // otherwise.
-  int GetNumFeaturesInEmbeddingSpace(int i) const {
-    TC_DCHECK(InRange(i, embedding_num_features_size()));
-    return std::max(0, embedding_num_features(i));
-  }
-
-  // Returns number of hidden layers in the neural network.  Each such layer has
-  // weight matrix and a bias vector (a matrix with one column).
-  int GetNumHiddenLayers() const {
-    if (hidden_size() != hidden_bias_size()) {
-      TC_LOG(ERROR) << "Hidden layer mismatch " << hidden_size()
-                    << " != " << hidden_bias_size();
-    }
-    return std::max(0, std::min(hidden_size(), hidden_bias_size()));
-  }
-
-  // Returns weight matrix for i-th hidden layer.
-  //
-  // NOTE: i must be in [0, GetNumHiddenLayers()).  Undefined behavior
-  // otherwise.
-  Matrix GetHiddenLayerMatrix(int i) const {
-    TC_DCHECK(InRange(i, hidden_size()));
-    Matrix matrix;
-    matrix.rows = hidden_num_rows(i);
-    matrix.cols = hidden_num_cols(i);
-
-    // Quantization not supported here.
-    matrix.quant_type = QuantizationType::NONE;
-    matrix.elements = hidden_weights(i);
-    return matrix;
-  }
-
-  // Returns bias matrix for i-th hidden layer.  Technically a Matrix, but we
-  // expect it to be a vector (i.e., num cols is 1).
-  //
-  // NOTE: i must be in [0, GetNumHiddenLayers()).  Undefined behavior
-  // otherwise.
-  Matrix GetHiddenLayerBias(int i) const {
-    TC_DCHECK(InRange(i, hidden_bias_size()));
-    Matrix matrix;
-    matrix.rows = hidden_bias_num_rows(i);
-    matrix.cols = hidden_bias_num_cols(i);
-
-    // Quantization not supported here.
-    matrix.quant_type = QuantizationType::NONE;
-    matrix.elements = hidden_bias_weights(i);
-    return matrix;
-  }
-
-  // Returns true if a softmax layer exists.
-  bool HasSoftmaxLayer() const {
-    if (softmax_size() != softmax_bias_size()) {
-      TC_LOG(ERROR) << "Softmax layer mismatch " << softmax_size()
-                    << " != " << softmax_bias_size();
-    }
-    return (softmax_size() == 1) && (softmax_bias_size() == 1);
-  }
-
-  // Returns weight matrix for the softmax layer.
-  //
-  // NOTE: Should be called only if HasSoftmaxLayer() is true.  Undefined
-  // behavior otherwise.
-  Matrix GetSoftmaxMatrix() const {
-    TC_DCHECK(softmax_size() == 1);
-    Matrix matrix;
-    matrix.rows = softmax_num_rows(0);
-    matrix.cols = softmax_num_cols(0);
-
-    // Quantization not supported here.
-    matrix.quant_type = QuantizationType::NONE;
-    matrix.elements = softmax_weights(0);
-    return matrix;
-  }
-
-  // Returns bias for the softmax layer.  Technically a Matrix, but we expect it
-  // to be a row/column vector (i.e., num cols is 1).
-  //
-  // NOTE: Should be called only if HasSoftmaxLayer() is true.  Undefined
-  // behavior otherwise.
-  Matrix GetSoftmaxBias() const {
-    TC_DCHECK(softmax_bias_size() == 1);
-    Matrix matrix;
-    matrix.rows = softmax_bias_num_rows(0);
-    matrix.cols = softmax_bias_num_cols(0);
-
-    // Quantization not supported here.
-    matrix.quant_type = QuantizationType::NONE;
-    matrix.elements = softmax_bias_weights(0);
-    return matrix;
-  }
-
-  // Updates the EmbeddingNetwork-related parameters from task_context.  Returns
-  // true on success, false on error.
-  virtual bool UpdateTaskContextParameters(TaskContext *task_context) {
-    const TaskSpec *task_spec = GetTaskSpec();
-    if (task_spec == nullptr) {
-      TC_LOG(ERROR) << "Unable to get TaskSpec";
-      return false;
-    }
-    for (const TaskSpec::Parameter &parameter : task_spec->parameter()) {
-      task_context->SetParameter(parameter.name(), parameter.value());
-    }
-    return true;
-  }
-
-  // Returns a pointer to a TaskSpec with the EmbeddingNetwork-related
-  // parameters.  Returns nullptr in case of problems.  Ownership with the
-  // returned pointer is *not* transfered to the caller.
-  virtual const TaskSpec *GetTaskSpec() {
-    TC_LOG(ERROR) << "Not implemented";
-    return nullptr;
-  }
-
- protected:
-  // **** Low-level API.
-  //
-  // * Most low-level API methods are documented by giving an equivalent
-  //   function call on proto, the original proto (of type
-  //   EmbeddingNetworkProto) which was used to generate the C++ code.
-  //
-  // * To simplify our generation code, optional proto fields of message type
-  //   are treated as repeated fields with 0 or 1 instances.  As such, we have
-  //   *_size() methods for such optional fields: they return 0 or 1.
-  //
-  // * "transpose(M)" denotes the transpose of a matrix M.
-  //
-  // * Behavior is undefined when trying to retrieve a piece of data that does
-  //   not exist: e.g., embeddings_num_rows(5) if embeddings_size() == 2.
-
-  // ** Access methods for repeated MatrixParams embeddings.
-  //
-  // Returns proto.embeddings_size().
-  virtual int embeddings_size() const = 0;
-
-  // Returns number of rows of transpose(proto.embeddings(i)).
-  virtual int embeddings_num_rows(int i) const = 0;
-
-  // Returns number of columns of transpose(proto.embeddings(i)).
-  virtual int embeddings_num_cols(int i) const = 0;
-
-  // Returns pointer to elements of transpose(proto.embeddings(i)), in row-major
-  // order.  NOTE: for unquantized embeddings, this returns a pointer to float;
-  // for quantized embeddings, this returns a pointer to uint8.
-  virtual const void *embeddings_weights(int i) const = 0;
-
-  virtual QuantizationType embeddings_quant_type(int i) const {
-    return QuantizationType::NONE;
-  }
-
-  virtual const float16 *embeddings_quant_scales(int i) const {
-    return nullptr;
-  }
-
-  // ** Access methods for repeated MatrixParams hidden.
-  //
-  // Returns embedding_network_proto.hidden_size().
-  virtual int hidden_size() const = 0;
-
-  // Returns embedding_network_proto.hidden(i).rows().
-  virtual int hidden_num_rows(int i) const = 0;
-
-  // Returns embedding_network_proto.hidden(i).rows().
-  virtual int hidden_num_cols(int i) const = 0;
-
-  // Returns pointer to beginning of array of floats with all values from
-  // embedding_network_proto.hidden(i).
-  virtual const void *hidden_weights(int i) const = 0;
-
-  // ** Access methods for repeated MatrixParams hidden_bias.
-  //
-  // Returns proto.hidden_bias_size().
-  virtual int hidden_bias_size() const = 0;
-
-  // Returns number of rows of proto.hidden_bias(i).
-  virtual int hidden_bias_num_rows(int i) const = 0;
-
-  // Returns number of columns of proto.hidden_bias(i).
-  virtual int hidden_bias_num_cols(int i) const = 0;
-
-  // Returns pointer to elements of proto.hidden_bias(i), in row-major order.
-  virtual const void *hidden_bias_weights(int i) const = 0;
-
-  // ** Access methods for optional MatrixParams softmax.
-  //
-  // Returns 1 if proto has optional field softmax, 0 otherwise.
-  virtual int softmax_size() const = 0;
-
-  // Returns number of rows of transpose(proto.softmax()).
-  virtual int softmax_num_rows(int i) const = 0;
-
-  // Returns number of columns of transpose(proto.softmax()).
-  virtual int softmax_num_cols(int i) const = 0;
-
-  // Returns pointer to elements of transpose(proto.softmax()), in row-major
-  // order.
-  virtual const void *softmax_weights(int i) const = 0;
-
-  // ** Access methods for optional MatrixParams softmax_bias.
-  //
-  // Returns 1 if proto has optional field softmax_bias, 0 otherwise.
-  virtual int softmax_bias_size() const = 0;
-
-  // Returns number of rows of proto.softmax_bias().
-  virtual int softmax_bias_num_rows(int i) const = 0;
-
-  // Returns number of columns of proto.softmax_bias().
-  virtual int softmax_bias_num_cols(int i) const = 0;
-
-  // Returns pointer to elements of proto.softmax_bias(), in row-major order.
-  virtual const void *softmax_bias_weights(int i) const = 0;
-
-  // ** Access methods for repeated int32 embedding_num_features.
-  //
-  // Returns proto.embedding_num_features_size().
-  virtual int embedding_num_features_size() const = 0;
-
-  // Returns proto.embedding_num_features(i).
-  virtual int embedding_num_features(int i) const = 0;
-
-  // Returns true if and only if index is in range [0, size).  Log an error
-  // message otherwise.
-  static bool InRange(int index, int size) {
-    if ((index < 0) || (index >= size)) {
-      TC_LOG(ERROR) << "Index " << index << " outside [0, " << size << ")";
-      return false;
-    }
-    return true;
-  }
-};  // class EmbeddingNetworkParams
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_PARAMS_H_
diff --git a/common/embedding-network.cc b/common/embedding-network.cc
deleted file mode 100644
index b27cda3..0000000
--- a/common/embedding-network.cc
+++ /dev/null
@@ -1,380 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/embedding-network.h"
-
-#include <math.h>
-
-#include "common/simple-adder.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace {
-
-// Returns true if and only if matrix does not use any quantization.
-bool CheckNoQuantization(const EmbeddingNetworkParams::Matrix &matrix) {
-  if (matrix.quant_type != QuantizationType::NONE) {
-    TC_LOG(ERROR) << "Unsupported quantization";
-    TC_DCHECK(false);  // Crash in debug mode.
-    return false;
-  }
-  return true;
-}
-
-// Initializes a Matrix object with the parameters from the MatrixParams
-// source_matrix.  source_matrix should not use quantization.
-//
-// Returns true on success, false on error.
-bool InitNonQuantizedMatrix(const EmbeddingNetworkParams::Matrix &source_matrix,
-                            EmbeddingNetwork::Matrix *mat) {
-  mat->resize(source_matrix.rows);
-
-  // Before we access the weights as floats, we need to check that they are
-  // really floats, i.e., no quantization is used.
-  if (!CheckNoQuantization(source_matrix)) return false;
-  const float *weights =
-      reinterpret_cast<const float *>(source_matrix.elements);
-  for (int r = 0; r < source_matrix.rows; ++r) {
-    (*mat)[r] = EmbeddingNetwork::VectorWrapper(weights, source_matrix.cols);
-    weights += source_matrix.cols;
-  }
-  return true;
-}
-
-// Initializes a VectorWrapper object with the parameters from the MatrixParams
-// source_matrix.  source_matrix should have exactly one column and should not
-// use quantization.
-//
-// Returns true on success, false on error.
-bool InitNonQuantizedVector(const EmbeddingNetworkParams::Matrix &source_matrix,
-                            EmbeddingNetwork::VectorWrapper *vector) {
-  if (source_matrix.cols != 1) {
-    TC_LOG(ERROR) << "wrong #cols " << source_matrix.cols;
-    return false;
-  }
-  if (!CheckNoQuantization(source_matrix)) {
-    TC_LOG(ERROR) << "unsupported quantization";
-    return false;
-  }
-  // Before we access the weights as floats, we need to check that they are
-  // really floats, i.e., no quantization is used.
-  if (!CheckNoQuantization(source_matrix)) return false;
-  const float *weights =
-      reinterpret_cast<const float *>(source_matrix.elements);
-  *vector = EmbeddingNetwork::VectorWrapper(weights, source_matrix.rows);
-  return true;
-}
-
-// Computes y = weights * Relu(x) + b where Relu is optionally applied.
-template <typename ScaleAdderClass>
-bool SparseReluProductPlusBias(bool apply_relu,
-                               const EmbeddingNetwork::Matrix &weights,
-                               const EmbeddingNetwork::VectorWrapper &b,
-                               const VectorSpan<float> &x,
-                               EmbeddingNetwork::Vector *y) {
-  // Check that dimensions match.
-  if ((x.size() != weights.size()) || weights.empty()) {
-    TC_LOG(ERROR) << x.size() << " != " << weights.size();
-    return false;
-  }
-  if (weights[0].size() != b.size()) {
-    TC_LOG(ERROR) << weights[0].size() << " != " << b.size();
-    return false;
-  }
-
-  y->assign(b.data(), b.data() + b.size());
-  ScaleAdderClass adder(y->data(), y->size());
-
-  const int x_size = x.size();
-  for (int i = 0; i < x_size; ++i) {
-    const float &scale = x[i];
-    if (apply_relu) {
-      if (scale > 0) {
-        adder.LazyScaleAdd(weights[i].data(), scale);
-      }
-    } else {
-      adder.LazyScaleAdd(weights[i].data(), scale);
-    }
-  }
-  return true;
-}
-}  // namespace
-
-bool EmbeddingNetwork::ConcatEmbeddings(
-    const std::vector<FeatureVector> &feature_vectors, Vector *concat) const {
-  concat->resize(concat_layer_size_);
-
-  // Invariant 1: feature_vectors contains exactly one element for each
-  // embedding space.  That element is itself a FeatureVector, which may be
-  // empty, but it should be there.
-  if (feature_vectors.size() != embedding_matrices_.size()) {
-    TC_LOG(ERROR) << feature_vectors.size()
-                  << " != " << embedding_matrices_.size();
-    return false;
-  }
-
-  // "es_index" stands for "embedding space index".
-  for (int es_index = 0; es_index < feature_vectors.size(); ++es_index) {
-    // Access is safe by es_index loop bounds and Invariant 1.
-    EmbeddingMatrix *const embedding_matrix =
-        embedding_matrices_[es_index].get();
-    if (embedding_matrix == nullptr) {
-      // Should not happen, hence our terse log error message.
-      TC_LOG(ERROR) << es_index;
-      return false;
-    }
-
-    // Access is safe due to es_index loop bounds.
-    const FeatureVector &feature_vector = feature_vectors[es_index];
-
-    // Access is safe by es_index loop bounds, Invariant 1, and Invariant 2.
-    const int concat_offset = concat_offset_[es_index];
-
-    if (!GetEmbeddingInternal(feature_vector, embedding_matrix, concat_offset,
-                              concat->data(), concat->size())) {
-      TC_LOG(ERROR) << es_index;
-      return false;
-    }
-  }
-  return true;
-}
-
-bool EmbeddingNetwork::GetEmbedding(const FeatureVector &feature_vector,
-                                    int es_index, float *embedding) const {
-  EmbeddingMatrix *const embedding_matrix = embedding_matrices_[es_index].get();
-  if (embedding_matrix == nullptr) {
-    // Should not happen, hence our terse log error message.
-    TC_LOG(ERROR) << es_index;
-    return false;
-  }
-  return GetEmbeddingInternal(feature_vector, embedding_matrix, 0, embedding,
-                              embedding_matrices_[es_index]->dim());
-}
-
-bool EmbeddingNetwork::GetEmbeddingInternal(
-    const FeatureVector &feature_vector,
-    EmbeddingMatrix *const embedding_matrix, const int concat_offset,
-    float *concat, int concat_size) const {
-  const int embedding_dim = embedding_matrix->dim();
-  const bool is_quantized =
-      embedding_matrix->quant_type() != QuantizationType::NONE;
-  const int num_features = feature_vector.size();
-  for (int fi = 0; fi < num_features; ++fi) {
-    // Both accesses below are safe due to loop bounds for fi.
-    const FeatureType *feature_type = feature_vector.type(fi);
-    const FeatureValue feature_value = feature_vector.value(fi);
-    const int feature_offset =
-        concat_offset + feature_type->base() * embedding_dim;
-
-    // Code below updates max(0, embedding_dim) elements from concat, starting
-    // with index feature_offset.  Check below ensures these updates are safe.
-    if ((feature_offset < 0) ||
-        (feature_offset + embedding_dim > concat_size)) {
-      TC_LOG(ERROR) << fi << ": " << feature_offset << " " << embedding_dim
-                    << " " << concat_size;
-      return false;
-    }
-
-    // Pointer to float / uint8 weights for relevant embedding.
-    const void *embedding_data;
-
-    // Multiplier for each embedding weight.
-    float multiplier;
-
-    if (feature_type->is_continuous()) {
-      // Continuous features (encoded as FloatFeatureValue).
-      FloatFeatureValue float_feature_value(feature_value);
-      const int id = float_feature_value.id;
-      embedding_matrix->get_embedding(id, &embedding_data, &multiplier);
-      multiplier *= float_feature_value.weight;
-    } else {
-      // Discrete features: every present feature has implicit value 1.0.
-      // Hence, after we grab the multiplier below, we don't multiply it by
-      // any weight.
-      embedding_matrix->get_embedding(feature_value, &embedding_data,
-                                      &multiplier);
-    }
-
-    // Weighted embeddings will be added starting from this address.
-    float *concat_ptr = concat + feature_offset;
-
-    if (is_quantized) {
-      const uint8 *quant_weights =
-          reinterpret_cast<const uint8 *>(embedding_data);
-      for (int i = 0; i < embedding_dim; ++i, ++quant_weights, ++concat_ptr) {
-        // 128 is bias for UINT8 quantization, only one we currently support.
-        *concat_ptr += (static_cast<int>(*quant_weights) - 128) * multiplier;
-      }
-    } else {
-      const float *weights = reinterpret_cast<const float *>(embedding_data);
-      for (int i = 0; i < embedding_dim; ++i, ++weights, ++concat_ptr) {
-        *concat_ptr += *weights * multiplier;
-      }
-    }
-  }
-  return true;
-}
-
-bool EmbeddingNetwork::ComputeLogits(const VectorSpan<float> &input,
-                                     Vector *scores) const {
-  return EmbeddingNetwork::ComputeLogitsInternal(input, scores);
-}
-
-bool EmbeddingNetwork::ComputeLogits(const Vector &input,
-                                     Vector *scores) const {
-  return EmbeddingNetwork::ComputeLogitsInternal(input, scores);
-}
-
-bool EmbeddingNetwork::ComputeLogitsInternal(const VectorSpan<float> &input,
-                                             Vector *scores) const {
-  return FinishComputeFinalScoresInternal<SimpleAdder>(input, scores);
-}
-
-template <typename ScaleAdderClass>
-bool EmbeddingNetwork::FinishComputeFinalScoresInternal(
-    const VectorSpan<float> &input, Vector *scores) const {
-  // This vector serves as an alternating storage for activations of the
-  // different layers. We can't use just one vector here because all of the
-  // activations of  the previous layer are needed for computation of
-  // activations of the next one.
-  std::vector<Vector> h_storage(2);
-
-  // Compute pre-logits activations.
-  VectorSpan<float> h_in(input);
-  Vector *h_out;
-  for (int i = 0; i < hidden_weights_.size(); ++i) {
-    const bool apply_relu = i > 0;
-    h_out = &(h_storage[i % 2]);
-    h_out->resize(hidden_bias_[i].size());
-    if (!SparseReluProductPlusBias<ScaleAdderClass>(
-            apply_relu, hidden_weights_[i], hidden_bias_[i], h_in, h_out)) {
-      return false;
-    }
-    h_in = VectorSpan<float>(*h_out);
-  }
-
-  // Compute logit scores.
-  if (!SparseReluProductPlusBias<ScaleAdderClass>(
-          true, softmax_weights_, softmax_bias_, h_in, scores)) {
-    return false;
-  }
-
-  return true;
-}
-
-bool EmbeddingNetwork::ComputeFinalScores(
-    const std::vector<FeatureVector> &features, Vector *scores) const {
-  return ComputeFinalScores(features, {}, scores);
-}
-
-bool EmbeddingNetwork::ComputeFinalScores(
-    const std::vector<FeatureVector> &features,
-    const std::vector<float> extra_inputs, Vector *scores) const {
-  // If we haven't successfully initialized, return without doing anything.
-  if (!is_valid()) return false;
-
-  Vector concat;
-  if (!ConcatEmbeddings(features, &concat)) return false;
-
-  if (!extra_inputs.empty()) {
-    concat.reserve(concat.size() + extra_inputs.size());
-    for (int i = 0; i < extra_inputs.size(); i++) {
-      concat.push_back(extra_inputs[i]);
-    }
-  }
-
-  scores->resize(softmax_bias_.size());
-  return ComputeLogits(concat, scores);
-}
-
-EmbeddingNetwork::EmbeddingNetwork(const EmbeddingNetworkParams *model) {
-  // We'll set valid_ to true only if construction is successful.  If we detect
-  // an error along the way, we log an informative message and return early, but
-  // we do not crash.
-  valid_ = false;
-
-  // Fill embedding_matrices_, concat_offset_, and concat_layer_size_.
-  const int num_embedding_spaces = model->GetNumEmbeddingSpaces();
-  int offset_sum = 0;
-  for (int i = 0; i < num_embedding_spaces; ++i) {
-    concat_offset_.push_back(offset_sum);
-    const EmbeddingNetworkParams::Matrix matrix = model->GetEmbeddingMatrix(i);
-    if (matrix.quant_type != QuantizationType::UINT8) {
-      TC_LOG(ERROR) << "Unsupported quantization for embedding #" << i << ": "
-                    << static_cast<int>(matrix.quant_type);
-      return;
-    }
-
-    // There is no way to accomodate an empty embedding matrix.  E.g., there is
-    // no way for get_embedding to return something that can be read safely.
-    // Hence, we catch that error here and return early.
-    if (matrix.rows == 0) {
-      TC_LOG(ERROR) << "Empty embedding matrix #" << i;
-      return;
-    }
-    embedding_matrices_.emplace_back(new EmbeddingMatrix(matrix));
-    const int embedding_dim = embedding_matrices_.back()->dim();
-    offset_sum += embedding_dim * model->GetNumFeaturesInEmbeddingSpace(i);
-  }
-  concat_layer_size_ = offset_sum;
-
-  // Invariant 2 (trivial by the code above).
-  TC_DCHECK_EQ(concat_offset_.size(), embedding_matrices_.size());
-
-  const int num_hidden_layers = model->GetNumHiddenLayers();
-  if (num_hidden_layers < 1) {
-    TC_LOG(ERROR) << "Wrong number of hidden layers: " << num_hidden_layers;
-    return;
-  }
-  hidden_weights_.resize(num_hidden_layers);
-  hidden_bias_.resize(num_hidden_layers);
-
-  for (int i = 0; i < num_hidden_layers; ++i) {
-    const EmbeddingNetworkParams::Matrix matrix =
-        model->GetHiddenLayerMatrix(i);
-    const EmbeddingNetworkParams::Matrix bias = model->GetHiddenLayerBias(i);
-    if (!InitNonQuantizedMatrix(matrix, &hidden_weights_[i]) ||
-        !InitNonQuantizedVector(bias, &hidden_bias_[i])) {
-      TC_LOG(ERROR) << "Bad hidden layer #" << i;
-      return;
-    }
-  }
-
-  if (!model->HasSoftmaxLayer()) {
-    TC_LOG(ERROR) << "Missing softmax layer";
-    return;
-  }
-  const EmbeddingNetworkParams::Matrix softmax = model->GetSoftmaxMatrix();
-  const EmbeddingNetworkParams::Matrix softmax_bias = model->GetSoftmaxBias();
-  if (!InitNonQuantizedMatrix(softmax, &softmax_weights_) ||
-      !InitNonQuantizedVector(softmax_bias, &softmax_bias_)) {
-    TC_LOG(ERROR) << "Bad softmax layer";
-    return;
-  }
-
-  // Everything looks good.
-  valid_ = true;
-}
-
-int EmbeddingNetwork::EmbeddingSize(int es_index) const {
-  return embedding_matrices_[es_index]->dim();
-}
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/embedding-network.h b/common/embedding-network.h
deleted file mode 100644
index a02c6ea..0000000
--- a/common/embedding-network.h
+++ /dev/null
@@ -1,246 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_H_
-#define LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_H_
-
-#include <memory>
-#include <vector>
-
-#include "common/embedding-network-params.h"
-#include "common/feature-extractor.h"
-#include "common/vector-span.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// Classifier using a hand-coded feed-forward neural network.
-//
-// No gradient computation, just inference.
-//
-// Classification works as follows:
-//
-// Discrete features -> Embeddings -> Concatenation -> Hidden+ -> Softmax
-//
-// In words: given some discrete features, this class extracts the embeddings
-// for these features, concatenates them, passes them through one or two hidden
-// layers (each layer uses Relu) and next through a softmax layer that computes
-// an unnormalized score for each possible class.  Note: there is always a
-// softmax layer.
-class EmbeddingNetwork {
- public:
-  // Class used to represent an embedding matrix.  Each row is the embedding on
-  // a vocabulary element.  Number of columns = number of embedding dimensions.
-  class EmbeddingMatrix {
-   public:
-    explicit EmbeddingMatrix(const EmbeddingNetworkParams::Matrix source_matrix)
-        : rows_(source_matrix.rows),
-          cols_(source_matrix.cols),
-          quant_type_(source_matrix.quant_type),
-          data_(source_matrix.elements),
-          row_size_in_bytes_(GetRowSizeInBytes(cols_, quant_type_)),
-          quant_scales_(source_matrix.quant_scales) {}
-
-    // Returns vocabulary size; one embedding for each vocabulary element.
-    int size() const { return rows_; }
-
-    // Returns number of weights in embedding of each vocabulary element.
-    int dim() const { return cols_; }
-
-    // Returns quantization type for this embedding matrix.
-    QuantizationType quant_type() const { return quant_type_; }
-
-    // Gets embedding for k-th vocabulary element: on return, sets *data to
-    // point to the embedding weights and *scale to the quantization scale (1.0
-    // if no quantization).
-    void get_embedding(int k, const void **data, float *scale) const {
-      if ((k < 0) || (k >= size())) {
-        TC_LOG(ERROR) << "Index outside [0, " << size() << "): " << k;
-
-        // In debug mode, crash.  In prod, pretend that k is 0.
-        TC_DCHECK(false);
-        k = 0;
-      }
-      *data = reinterpret_cast<const char *>(data_) + k * row_size_in_bytes_;
-      if (quant_type_ == QuantizationType::NONE) {
-        *scale = 1.0;
-      } else {
-        *scale = Float16To32(quant_scales_[k]);
-      }
-    }
-
-   private:
-    static int GetRowSizeInBytes(int cols, QuantizationType quant_type) {
-      switch (quant_type) {
-        case QuantizationType::NONE:
-          return cols * sizeof(float);
-        case QuantizationType::UINT8:
-          return cols * sizeof(uint8);
-        default:
-          TC_LOG(ERROR) << "Unknown quant type: "
-                        << static_cast<int>(quant_type);
-          return 0;
-      }
-    }
-
-    // Vocabulary size.
-    const int rows_;
-
-    // Number of elements in each embedding.
-    const int cols_;
-
-    const QuantizationType quant_type_;
-
-    // Pointer to the embedding weights, in row-major order.  This is a pointer
-    // to an array of floats / uint8, depending on the quantization type.
-    // Not owned.
-    const void *const data_;
-
-    // Number of bytes for one row.  Used to jump to next row in data_.
-    const int row_size_in_bytes_;
-
-    // Pointer to quantization scales.  nullptr if no quantization.  Otherwise,
-    // quant_scales_[i] is scale for embedding of i-th vocabulary element.
-    const float16 *const quant_scales_;
-
-    TC_DISALLOW_COPY_AND_ASSIGN(EmbeddingMatrix);
-  };
-
-  // An immutable vector that doesn't own the memory that stores the underlying
-  // floats.  Can be used e.g., as a wrapper around model weights stored in the
-  // static memory.
-  class VectorWrapper {
-   public:
-    VectorWrapper() : VectorWrapper(nullptr, 0) {}
-
-    // Constructs a vector wrapper around the size consecutive floats that start
-    // at address data.  Note: the underlying data should be alive for at least
-    // the lifetime of this VectorWrapper object.  That's trivially true if data
-    // points to statically allocated data :)
-    VectorWrapper(const float *data, int size) : data_(data), size_(size) {}
-
-    int size() const { return size_; }
-
-    const float *data() const { return data_; }
-
-   private:
-    const float *data_;  // Not owned.
-    int size_;
-
-    // Doesn't own anything, so it can be copied and assigned at will :)
-  };
-
-  typedef std::vector<VectorWrapper> Matrix;
-  typedef std::vector<float> Vector;
-
-  // Constructs an embedding network using the parameters from model.
-  //
-  // Note: model should stay alive for at least the lifetime of this
-  // EmbeddingNetwork object.
-  explicit EmbeddingNetwork(const EmbeddingNetworkParams *model);
-
-  virtual ~EmbeddingNetwork() {}
-
-  // Returns true if this EmbeddingNetwork object has been correctly constructed
-  // and is ready to use.  Idea: in case of errors, mark this EmbeddingNetwork
-  // object as invalid, but do not crash.
-  bool is_valid() const { return valid_; }
-
-  // Runs forward computation to fill scores with unnormalized output unit
-  // scores. This is useful for making predictions.
-  //
-  // Returns true on success, false on error (e.g., if !is_valid()).
-  bool ComputeFinalScores(const std::vector<FeatureVector> &features,
-                          Vector *scores) const;
-
-  // Same as above, but allows specification of extra neural network inputs that
-  // will be appended to the embedding vector build from features.
-  bool ComputeFinalScores(const std::vector<FeatureVector> &features,
-                          const std::vector<float> extra_inputs,
-                          Vector *scores) const;
-
-  // Constructs the concatenated input embedding vector in place in output
-  // vector concat.  Returns true on success, false on error.
-  bool ConcatEmbeddings(const std::vector<FeatureVector> &features,
-                        Vector *concat) const;
-
-  // Sums embeddings for all features from |feature_vector| and adds result
-  // to values from the array pointed-to by |output|.  Embeddings for continuous
-  // features are weighted by the feature weight.
-  //
-  // NOTE: output should point to an array of EmbeddingSize(es_index) floats.
-  bool GetEmbedding(const FeatureVector &feature_vector, int es_index,
-                    float *embedding) const;
-
-  // Runs the feed-forward neural network for |input| and computes logits for
-  // softmax layer.
-  bool ComputeLogits(const Vector &input, Vector *scores) const;
-
-  // Same as above but uses a view of the feature vector.
-  bool ComputeLogits(const VectorSpan<float> &input, Vector *scores) const;
-
-  // Returns the size (the number of columns) of the embedding space es_index.
-  int EmbeddingSize(int es_index) const;
-
- protected:
-  // Builds an embedding for given feature vector, and places it from
-  // concat_offset to the concat vector.
-  bool GetEmbeddingInternal(const FeatureVector &feature_vector,
-                            EmbeddingMatrix *embedding_matrix,
-                            int concat_offset, float *concat,
-                            int embedding_size) const;
-
-  // Templated function that computes the logit scores given the concatenated
-  // input embeddings.
-  bool ComputeLogitsInternal(const VectorSpan<float> &concat,
-                             Vector *scores) const;
-
-  // Computes the softmax scores (prior to normalization) from the concatenated
-  // representation.  Returns true on success, false on error.
-  template <typename ScaleAdderClass>
-  bool FinishComputeFinalScoresInternal(const VectorSpan<float> &concat,
-                                        Vector *scores) const;
-
-  // Set to true on successful construction, false otherwise.
-  bool valid_ = false;
-
-  // Network parameters.
-
-  // One weight matrix for each embedding space.
-  std::vector<std::unique_ptr<EmbeddingMatrix>> embedding_matrices_;
-
-  // concat_offset_[i] is the input layer offset for i-th embedding space.
-  std::vector<int> concat_offset_;
-
-  // Size of the input ("concatenation") layer.
-  int concat_layer_size_;
-
-  // One weight matrix and one vector of bias weights for each hiden layer.
-  std::vector<Matrix> hidden_weights_;
-  std::vector<VectorWrapper> hidden_bias_;
-
-  // Weight matrix and bias vector for the softmax layer.
-  Matrix softmax_weights_;
-  VectorWrapper softmax_bias_;
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_EMBEDDING_NETWORK_H_
diff --git a/common/embedding-network.proto b/common/embedding-network.proto
deleted file mode 100644
index ce30b11..0000000
--- a/common/embedding-network.proto
+++ /dev/null
@@ -1,90 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// Protos for performing inference with an EmbeddingNetwork.
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-package libtextclassifier.nlp_core;
-
-// Wrapper for storing a matrix of parameters. These are stored in row-major
-// order.
-message MatrixParams {
-  optional int32 rows = 1;  // # of rows in the matrix
-  optional int32 cols = 2;  // # of columns in the matrix
-
-  // Non-quantized matrix entries.
-  repeated float value = 3 [packed = true];
-
-  // Whether the matrix is quantized.
-  optional bool is_quantized = 4 [default = false];
-
-  // Bytes for all quantized values.  Each value (see "repeated float value"
-  // field) is quantized to an uint8 (1 byte) value, and all these bytes are
-  // concatenated into the string from this field.
-  optional bytes bytes_for_quantized_values = 7;
-
-  // Bytes for all scale factors for dequantizing the values.  The quantization
-  // process generates a float16 scale factor for each column.  The 2 bytes for
-  // each such float16 are put in little-endian order (least significant byte
-  // first) and next all these pairs of bytes are concatenated into the string
-  // from this field.
-  optional bytes bytes_for_col_scales = 8;
-
-  reserved 5, 6;
-}
-
-// Stores all parameters for a given EmbeddingNetwork. This can either be a
-// EmbeddingNetwork or a PrecomputedEmbeddingNetwork: for precomputed networks,
-// the embedding weights are actually the activations of the first hidden layer
-// *before* the bias is added and the non-linear transform is applied.
-//
-// Thus, for PrecomputedEmbeddingNetwork storage, hidden layers are stored
-// starting from the second hidden layer, while biases are stored for every
-// hidden layer.
-message EmbeddingNetworkProto {
-  // Embeddings and hidden layers. Note that if is_precomputed == true, then the
-  // embeddings should store the activations of the first hidden layer, so we
-  // must have hidden_bias_size() == hidden_size() + 1 (we store weights for
-  // first hidden layer bias, but no the layer itself.)
-  repeated MatrixParams embeddings = 1;
-  repeated MatrixParams hidden = 2;
-  repeated MatrixParams hidden_bias = 3;
-
-  // Final layer of the network.
-  optional MatrixParams softmax = 4;
-  optional MatrixParams softmax_bias = 5;
-
-  // Element i of the repeated field below indicates number of features that use
-  // the i-th embedding space.
-  repeated int32 embedding_num_features = 7;
-
-  // Whether or not this is intended to store a precomputed network.
-  optional bool is_precomputed = 11 [default = false];
-
-  // True if this EmbeddingNetworkProto can be used for inference with no
-  // additional matrix transposition.
-  //
-  // Given an EmbeddingNetworkProto produced by a Neurosis training pipeline, we
-  // have to transpose a few matrices (e.g., the embedding matrices) before we
-  // can perform inference.  When we do so, we negate this flag.  Note: we don't
-  // simply set this to true: transposing twice takes us to the original state.
-  optional bool is_transposed = 12 [default = false];
-
-  // Allow extensions.
-  extensions 100 to max;
-
-  reserved 6, 8, 9, 10;
-}
diff --git a/common/embedding-network_test.cc b/common/embedding-network_test.cc
deleted file mode 100644
index 026ec17..0000000
--- a/common/embedding-network_test.cc
+++ /dev/null
@@ -1,87 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/embedding-network.h"
-#include "common/embedding-network-params-from-proto.h"
-#include "common/embedding-network.pb.h"
-#include "common/simple-adder.h"
-
-#include "gmock/gmock.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace {
-
-using testing::ElementsAreArray;
-
-class TestingEmbeddingNetwork : public EmbeddingNetwork {
- public:
-  using EmbeddingNetwork::EmbeddingNetwork;
-  using EmbeddingNetwork::FinishComputeFinalScoresInternal;
-};
-
-void DiagonalAndBias3x3(int diagonal_value, int bias_value,
-                        MatrixParams* weights, MatrixParams* bias) {
-  weights->set_rows(3);
-  weights->set_cols(3);
-  weights->add_value(diagonal_value);
-  weights->add_value(0);
-  weights->add_value(0);
-  weights->add_value(0);
-  weights->add_value(diagonal_value);
-  weights->add_value(0);
-  weights->add_value(0);
-  weights->add_value(0);
-  weights->add_value(diagonal_value);
-
-  bias->set_rows(3);
-  bias->set_cols(1);
-  bias->add_value(bias_value);
-  bias->add_value(bias_value);
-  bias->add_value(bias_value);
-}
-
-TEST(EmbeddingNetworkTest, IdentityThroughMultipleLayers) {
-  std::unique_ptr<EmbeddingNetworkProto> proto;
-  proto.reset(new EmbeddingNetworkProto);
-
-  // These layers should be an identity with bias.
-  DiagonalAndBias3x3(/*diagonal_value=*/1, /*bias_value=*/1,
-                     proto->add_hidden(), proto->add_hidden_bias());
-  DiagonalAndBias3x3(/*diagonal_value=*/1, /*bias_value=*/2,
-                     proto->add_hidden(), proto->add_hidden_bias());
-  DiagonalAndBias3x3(/*diagonal_value=*/1, /*bias_value=*/3,
-                     proto->add_hidden(), proto->add_hidden_bias());
-  DiagonalAndBias3x3(/*diagonal_value=*/1, /*bias_value=*/4,
-                     proto->add_hidden(), proto->add_hidden_bias());
-  DiagonalAndBias3x3(/*diagonal_value=*/1, /*bias_value=*/5,
-                     proto->mutable_softmax(), proto->mutable_softmax_bias());
-
-  EmbeddingNetworkParamsFromProto params(std::move(proto));
-  TestingEmbeddingNetwork network(&params);
-
-  std::vector<float> input({-2, -1, 0});
-  std::vector<float> output;
-  network.FinishComputeFinalScoresInternal<SimpleAdder>(
-      VectorSpan<float>(input), &output);
-
-  EXPECT_THAT(output, ElementsAreArray({14, 14, 15}));
-}
-
-}  // namespace
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/feature-descriptors.h b/common/feature-descriptors.h
deleted file mode 100644
index 9aa6527..0000000
--- a/common/feature-descriptors.h
+++ /dev/null
@@ -1,154 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_FEATURE_DESCRIPTORS_H_
-#define LIBTEXTCLASSIFIER_COMMON_FEATURE_DESCRIPTORS_H_
-
-#include <memory>
-#include <string>
-#include <vector>
-
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// Named feature parameter.
-class Parameter {
- public:
-  Parameter() {}
-
-  void set_name(const std::string &name) { name_ = name; }
-  const std::string &name() const { return name_; }
-
-  void set_value(const std::string &value) { value_ = value; }
-  const std::string &value() const { return value_; }
-
- private:
-  std::string name_;
-  std::string value_;
-};
-
-// Descriptor for a feature function.  Used to store the results of parsing one
-// feature function.
-class FeatureFunctionDescriptor {
- public:
-  FeatureFunctionDescriptor() {}
-
-  // Accessors for the feature function type.  The function type is the string
-  // that the feature extractor code is registered under.
-  void set_type(const std::string &type) { type_ = type; }
-  bool has_type() const { return !type_.empty(); }
-  const std::string &type() const { return type_; }
-
-  // Accessors for the feature function name.  The function name (if available)
-  // is used for some log messages.  Otherwise, a more precise, but also more
-  // verbose name based on the feature specification is used.
-  void set_name(const std::string &name) { name_ = name; }
-  bool has_name() const { return !name_.empty(); }
-  const std::string &name() { return name_; }
-
-  // Accessors for the default (name-less) parameter.
-  void set_argument(int32 argument) { argument_ = argument; }
-  bool has_argument() const {
-    // If argument has not been specified, clients should treat it as 0.  This
-    // makes the test below correct, without having a separate has_argument_
-    // bool field.
-    return argument_ != 0;
-  }
-  int32 argument() const { return argument_; }
-
-  // Accessors for the named parameters.
-  Parameter *add_parameter() {
-    parameters_.emplace_back();
-    return &(parameters_.back());
-  }
-  int parameter_size() const { return parameters_.size(); }
-  const Parameter &parameter(int i) const {
-    TC_DCHECK((i >= 0) && (i < parameter_size()));
-    return parameters_[i];
-  }
-
-  // Accessors for the sub (i.e., nested) features.  Nested features: as in
-  // offset(1).label.
-  FeatureFunctionDescriptor *add_feature() {
-    sub_features_.emplace_back(new FeatureFunctionDescriptor());
-    return sub_features_.back().get();
-  }
-  int feature_size() const { return sub_features_.size(); }
-  const FeatureFunctionDescriptor &feature(int i) const {
-    TC_DCHECK((i >= 0) && (i < feature_size()));
-    return *(sub_features_[i].get());
-  }
-  FeatureFunctionDescriptor *mutable_feature(int i) {
-    TC_DCHECK((i >= 0) && (i < feature_size()));
-    return sub_features_[i].get();
-  }
-
- private:
-  // See comments for set_type().
-  std::string type_;
-
-  // See comments for set_name().
-  std::string name_;
-
-  // See comments for set_argument().
-  int32 argument_ = 0;
-
-  // See comemnts for add_parameter().
-  std::vector<Parameter> parameters_;
-
-  // See comments for add_feature().
-  std::vector<std::unique_ptr<FeatureFunctionDescriptor>> sub_features_;
-
-  TC_DISALLOW_COPY_AND_ASSIGN(FeatureFunctionDescriptor);
-};
-
-// List of FeatureFunctionDescriptors.  Used to store the result of parsing the
-// spec for several feature functions.
-class FeatureExtractorDescriptor {
- public:
-  FeatureExtractorDescriptor() {}
-
-  int feature_size() const { return features_.size(); }
-
-  FeatureFunctionDescriptor *add_feature() {
-    features_.emplace_back(new FeatureFunctionDescriptor());
-    return features_.back().get();
-  }
-
-  const FeatureFunctionDescriptor &feature(int i) const {
-    TC_DCHECK((i >= 0) && (i < feature_size()));
-    return *(features_[i].get());
-  }
-
-  FeatureFunctionDescriptor *mutable_feature(int i) {
-    TC_DCHECK((i >= 0) && (i < feature_size()));
-    return features_[i].get();
-  }
-
- private:
-  std::vector<std::unique_ptr<FeatureFunctionDescriptor>> features_;
-
-  TC_DISALLOW_COPY_AND_ASSIGN(FeatureExtractorDescriptor);
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_FEATURE_DESCRIPTORS_H_
diff --git a/common/feature-extractor.cc b/common/feature-extractor.cc
deleted file mode 100644
index 12de46d..0000000
--- a/common/feature-extractor.cc
+++ /dev/null
@@ -1,152 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/feature-extractor.h"
-
-#include "common/feature-types.h"
-#include "common/fml-parser.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/gtl/stl_util.h"
-#include "util/strings/numbers.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-constexpr FeatureValue GenericFeatureFunction::kNone;
-
-GenericFeatureExtractor::GenericFeatureExtractor() {}
-
-GenericFeatureExtractor::~GenericFeatureExtractor() {}
-
-bool GenericFeatureExtractor::Parse(const std::string &source) {
-  // Parse feature specification into descriptor.
-  FMLParser parser;
-  if (!parser.Parse(source, mutable_descriptor())) return false;
-
-  // Initialize feature extractor from descriptor.
-  if (!InitializeFeatureFunctions()) return false;
-  return true;
-}
-
-bool GenericFeatureExtractor::InitializeFeatureTypes() {
-  // Register all feature types.
-  GetFeatureTypes(&feature_types_);
-  for (size_t i = 0; i < feature_types_.size(); ++i) {
-    FeatureType *ft = feature_types_[i];
-    ft->set_base(i);
-
-    // Check for feature space overflow.
-    double domain_size = ft->GetDomainSize();
-    if (domain_size < 0) {
-      TC_LOG(ERROR) << "Illegal domain size for feature " << ft->name() << ": "
-                    << domain_size;
-      return false;
-    }
-  }
-  return true;
-}
-
-FeatureValue GenericFeatureExtractor::GetDomainSize() const {
-  // Domain size of the set of features is equal to:
-  //   [largest domain size of any feature types] * [number of feature types]
-  FeatureValue max_feature_type_dsize = 0;
-  for (size_t i = 0; i < feature_types_.size(); ++i) {
-    FeatureType *ft = feature_types_[i];
-    const FeatureValue feature_type_dsize = ft->GetDomainSize();
-    if (feature_type_dsize > max_feature_type_dsize) {
-      max_feature_type_dsize = feature_type_dsize;
-    }
-  }
-
-  return max_feature_type_dsize * feature_types_.size();
-}
-
-std::string GenericFeatureFunction::GetParameter(
-    const std::string &name) const {
-  // Find named parameter in feature descriptor.
-  for (int i = 0; i < descriptor_->parameter_size(); ++i) {
-    if (name == descriptor_->parameter(i).name()) {
-      return descriptor_->parameter(i).value();
-    }
-  }
-  return "";
-}
-
-GenericFeatureFunction::GenericFeatureFunction() {}
-
-GenericFeatureFunction::~GenericFeatureFunction() { delete feature_type_; }
-
-int GenericFeatureFunction::GetIntParameter(const std::string &name,
-                                            int default_value) const {
-  int32 parsed_value = default_value;
-  std::string value = GetParameter(name);
-  if (!value.empty()) {
-    if (!ParseInt32(value.c_str(), &parsed_value)) {
-      // A parameter value has been specified, but it can't be parsed as an int.
-      // We don't crash: instead, we long an error and return the default value.
-      TC_LOG(ERROR) << "Value of param " << name << " is not an int: " << value;
-    }
-  }
-  return parsed_value;
-}
-
-bool GenericFeatureFunction::GetBoolParameter(const std::string &name,
-                                              bool default_value) const {
-  std::string value = GetParameter(name);
-  if (value.empty()) return default_value;
-  if (value == "true") return true;
-  if (value == "false") return false;
-  TC_LOG(ERROR) << "Illegal value '" << value << "' for bool parameter '"
-                << name << "'"
-                << " will assume default " << default_value;
-  return default_value;
-}
-
-void GenericFeatureFunction::GetFeatureTypes(
-    std::vector<FeatureType *> *types) const {
-  if (feature_type_ != nullptr) types->push_back(feature_type_);
-}
-
-FeatureType *GenericFeatureFunction::GetFeatureType() const {
-  // If a single feature type has been registered return it.
-  if (feature_type_ != nullptr) return feature_type_;
-
-  // Get feature types for function.
-  std::vector<FeatureType *> types;
-  GetFeatureTypes(&types);
-
-  // If there is exactly one feature type return this, else return null.
-  if (types.size() == 1) return types[0];
-  return nullptr;
-}
-
-std::string GenericFeatureFunction::name() const {
-  std::string output;
-  if (descriptor_->name().empty()) {
-    if (!prefix_.empty()) {
-      output.append(prefix_);
-      output.append(".");
-    }
-    ToFML(*descriptor_, &output);
-  } else {
-    output = descriptor_->name();
-  }
-  return output;
-}
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/feature-extractor.h b/common/feature-extractor.h
deleted file mode 100644
index bdba609..0000000
--- a/common/feature-extractor.h
+++ /dev/null
@@ -1,665 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Generic feature extractor for extracting features from objects. The feature
-// extractor can be used for extracting features from any object. The feature
-// extractor and feature function classes are template classes that have to
-// be instantiated for extracting feature from a specific object type.
-//
-// A feature extractor consists of a hierarchy of feature functions. Each
-// feature function extracts one or more feature type and value pairs from the
-// object.
-//
-// The feature extractor has a modular design where new feature functions can be
-// registered as components. The feature extractor is initialized from a
-// descriptor represented by a protocol buffer. The feature extractor can also
-// be initialized from a text-based source specification of the feature
-// extractor. Feature specification parsers can be added as components. By
-// default the feature extractor can be read from an ASCII protocol buffer or in
-// a simple feature modeling language (fml).
-
-// A feature function is invoked with a focus. Nested feature function can be
-// invoked with another focus determined by the parent feature function.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_FEATURE_EXTRACTOR_H_
-#define LIBTEXTCLASSIFIER_COMMON_FEATURE_EXTRACTOR_H_
-
-#include <stddef.h>
-
-#include <string>
-#include <vector>
-
-#include "common/feature-descriptors.h"
-#include "common/feature-types.h"
-#include "common/fml-parser.h"
-#include "common/registry.h"
-#include "common/task-context.h"
-#include "common/workspace.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-#include "util/gtl/stl_util.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-typedef int64 Predicate;
-typedef Predicate FeatureValue;
-
-// A union used to represent discrete and continuous feature values.
-union FloatFeatureValue {
- public:
-  explicit FloatFeatureValue(FeatureValue v) : discrete_value(v) {}
-  FloatFeatureValue(uint32 i, float w) : id(i), weight(w) {}
-  FeatureValue discrete_value;
-  struct {
-    uint32 id;
-    float weight;
-  };
-};
-
-// A feature vector contains feature type and value pairs.
-class FeatureVector {
- public:
-  FeatureVector() {}
-
-  // Adds feature type and value pair to feature vector.
-  void add(FeatureType *type, FeatureValue value) {
-    features_.emplace_back(type, value);
-  }
-
-  // Removes all elements from the feature vector.
-  void clear() { features_.clear(); }
-
-  // Returns the number of elements in the feature vector.
-  int size() const { return features_.size(); }
-
-  // Reserves space in the underlying feature vector.
-  void reserve(int n) { features_.reserve(n); }
-
-  // Returns feature type for an element in the feature vector.
-  FeatureType *type(int index) const { return features_[index].type; }
-
-  // Returns feature value for an element in the feature vector.
-  FeatureValue value(int index) const { return features_[index].value; }
-
- private:
-  // Structure for holding feature type and value pairs.
-  struct Element {
-    Element() : type(nullptr), value(-1) {}
-    Element(FeatureType *t, FeatureValue v) : type(t), value(v) {}
-
-    FeatureType *type;
-    FeatureValue value;
-  };
-
-  // Array for storing feature vector elements.
-  std::vector<Element> features_;
-
-  TC_DISALLOW_COPY_AND_ASSIGN(FeatureVector);
-};
-
-// The generic feature extractor is the type-independent part of a feature
-// extractor. This holds the descriptor for the feature extractor and the
-// collection of feature types used in the feature extractor.  The feature
-// types are not available until FeatureExtractor<>::Init() has been called.
-class GenericFeatureExtractor {
- public:
-  GenericFeatureExtractor();
-  virtual ~GenericFeatureExtractor();
-
-  // Initializes the feature extractor from an FML string specification.  For
-  // the FML specification grammar, see fml-parser.h.
-  //
-  // Returns true on success, false on syntax error.
-  bool Parse(const std::string &source);
-
-  // Returns the feature extractor descriptor.
-  const FeatureExtractorDescriptor &descriptor() const { return descriptor_; }
-  FeatureExtractorDescriptor *mutable_descriptor() { return &descriptor_; }
-
-  // Returns the number of feature types in the feature extractor.  Invalid
-  // before Init() has been called.
-  int feature_types() const { return feature_types_.size(); }
-
-  // Returns a feature type used in the extractor.  Invalid before Init() has
-  // been called.
-  const FeatureType *feature_type(int index) const {
-    return feature_types_[index];
-  }
-
-  // Returns the feature domain size of this feature extractor.
-  // NOTE: The way that domain size is calculated is, for some, unintuitive. It
-  // is the largest domain size of any feature type.
-  FeatureValue GetDomainSize() const;
-
- protected:
-  // Initializes the feature types used by the extractor.  Called from
-  // FeatureExtractor<>::Init().
-  //
-  // Returns true on success, false on error.
-  bool InitializeFeatureTypes();
-
- private:
-  // Initializes the top-level feature functions.
-  virtual bool InitializeFeatureFunctions() = 0;
-
-  // Returns all feature types used by the extractor. The feature types are
-  // added to the result array.
-  virtual void GetFeatureTypes(std::vector<FeatureType *> *types) const = 0;
-
-  // Descriptor for the feature extractor. This is a protocol buffer that
-  // contains all the information about the feature extractor. The feature
-  // functions are initialized from the information in the descriptor.
-  FeatureExtractorDescriptor descriptor_;
-
-  // All feature types used by the feature extractor. The collection of all the
-  // feature types describes the feature space of the feature set produced by
-  // the feature extractor.  Not owned.
-  std::vector<FeatureType *> feature_types_;
-
-  TC_DISALLOW_COPY_AND_ASSIGN(GenericFeatureExtractor);
-};
-
-// The generic feature function is the type-independent part of a feature
-// function. Each feature function is associated with the descriptor that it is
-// instantiated from.  The feature types associated with this feature function
-// will be established by the time FeatureExtractor<>::Init() completes.
-class GenericFeatureFunction {
- public:
-  // A feature value that represents the absence of a value.
-  static constexpr FeatureValue kNone = -1;
-
-  GenericFeatureFunction();
-  virtual ~GenericFeatureFunction();
-
-  // Sets up the feature function. NB: FeatureTypes of nested functions are not
-  // guaranteed to be available until Init().
-  //
-  // Returns true on success, false on error.
-  virtual bool Setup(TaskContext *context) { return true; }
-
-  // Initializes the feature function. NB: The FeatureType of this function must
-  // be established when this method completes.
-  //
-  // Returns true on success, false on error.
-  virtual bool Init(TaskContext *context) { return true; }
-
-  // Requests workspaces from a registry to obtain indices into a WorkspaceSet
-  // for any Workspace objects used by this feature function. NB: This will be
-  // called after Init(), so it can depend on resources and arguments.
-  virtual void RequestWorkspaces(WorkspaceRegistry *registry) {}
-
-  // Appends the feature types produced by the feature function to types.  The
-  // default implementation appends feature_type(), if non-null.  Invalid
-  // before Init() has been called.
-  virtual void GetFeatureTypes(std::vector<FeatureType *> *types) const;
-
-  // Returns the feature type for feature produced by this feature function. If
-  // the feature function produces features of different types this returns
-  // null.  Invalid before Init() has been called.
-  virtual FeatureType *GetFeatureType() const;
-
-  // Returns the name of the registry used for creating the feature function.
-  // This can be used for checking if two feature functions are of the same
-  // kind.
-  virtual const char *RegistryName() const = 0;
-
-  // Returns the value of a named parameter from the feature function
-  // descriptor.  Returns empty string ("") if parameter is not found.
-  std::string GetParameter(const std::string &name) const;
-
-  // Returns the int value of a named parameter from the feature function
-  // descriptor.  Returns default_value if the parameter is not found or if its
-  // value can't be parsed as an int.
-  int GetIntParameter(const std::string &name, int default_value) const;
-
-  // Returns the bool value of a named parameter from the feature function
-  // descriptor.  Returns default_value if the parameter is not found or if its
-  // value is not "true" or "false".
-  bool GetBoolParameter(const std::string &name, bool default_value) const;
-
-  // Returns the FML function description for the feature function, i.e. the
-  // name and parameters without the nested features.
-  std::string FunctionName() const {
-    std::string output;
-    ToFMLFunction(*descriptor_, &output);
-    return output;
-  }
-
-  // Returns the prefix for nested feature functions. This is the prefix of this
-  // feature function concatenated with the feature function name.
-  std::string SubPrefix() const {
-    return prefix_.empty() ? FunctionName() : prefix_ + "." + FunctionName();
-  }
-
-  // Returns/sets the feature extractor this function belongs to.
-  GenericFeatureExtractor *extractor() const { return extractor_; }
-  void set_extractor(GenericFeatureExtractor *extractor) {
-    extractor_ = extractor;
-  }
-
-  // Returns/sets the feature function descriptor.
-  FeatureFunctionDescriptor *descriptor() const { return descriptor_; }
-  void set_descriptor(FeatureFunctionDescriptor *descriptor) {
-    descriptor_ = descriptor;
-  }
-
-  // Returns a descriptive name for the feature function. The name is taken from
-  // the descriptor for the feature function. If the name is empty or the
-  // feature function is a variable the name is the FML representation of the
-  // feature, including the prefix.
-  std::string name() const;
-
-  // Returns the argument from the feature function descriptor. It defaults to
-  // 0 if the argument has not been specified.
-  int argument() const {
-    return descriptor_->has_argument() ? descriptor_->argument() : 0;
-  }
-
-  // Returns/sets/clears function name prefix.
-  const std::string &prefix() const { return prefix_; }
-  void set_prefix(const std::string &prefix) { prefix_ = prefix; }
-
- protected:
-  // Returns the feature type for single-type feature functions.
-  FeatureType *feature_type() const { return feature_type_; }
-
-  // Sets the feature type for single-type feature functions.  This takes
-  // ownership of feature_type.  Can only be called once with a non-null
-  // pointer.
-  void set_feature_type(FeatureType *feature_type) {
-    TC_DCHECK_NE(feature_type, nullptr);
-    feature_type_ = feature_type;
-  }
-
- private:
-  // Feature extractor this feature function belongs to.  Not owned.
-  GenericFeatureExtractor *extractor_ = nullptr;
-
-  // Descriptor for feature function.  Not owned.
-  FeatureFunctionDescriptor *descriptor_ = nullptr;
-
-  // Feature type for features produced by this feature function. If the
-  // feature function produces features of multiple feature types this is null
-  // and the feature function must return it's feature types in
-  // GetFeatureTypes().  Owned.
-  FeatureType *feature_type_ = nullptr;
-
-  // Prefix used for sub-feature types of this function.
-  std::string prefix_;
-};
-
-// Feature function that can extract features from an object.  Templated on
-// two type arguments:
-//
-// OBJ:  The "object" from which features are extracted; e.g., a sentence.  This
-//       should be a plain type, rather than a reference or pointer.
-//
-// ARGS: A set of 0 or more types that are used to "index" into some part of the
-//       object that should be extracted, e.g. an int token index for a sentence
-//       object.  This should not be a reference type.
-template <class OBJ, class... ARGS>
-class FeatureFunction
-    : public GenericFeatureFunction,
-      public RegisterableClass<FeatureFunction<OBJ, ARGS...> > {
- public:
-  using Self = FeatureFunction<OBJ, ARGS...>;
-
-  // Preprocesses the object.  This will be called prior to calling Evaluate()
-  // or Compute() on that object.
-  virtual void Preprocess(WorkspaceSet *workspaces, OBJ *object) const {}
-
-  // Appends features computed from the object and focus to the result.  The
-  // default implementation delegates to Compute(), adding a single value if
-  // available.  Multi-valued feature functions must override this method.
-  virtual void Evaluate(const WorkspaceSet &workspaces, const OBJ &object,
-                        ARGS... args, FeatureVector *result) const {
-    FeatureValue value = Compute(workspaces, object, args..., result);
-    if (value != kNone) result->add(feature_type(), value);
-  }
-
-  // Returns a feature value computed from the object and focus, or kNone if no
-  // value is computed.  Single-valued feature functions only need to override
-  // this method.
-  virtual FeatureValue Compute(const WorkspaceSet &workspaces,
-                               const OBJ &object, ARGS... args,
-                               const FeatureVector *fv) const {
-    return kNone;
-  }
-
-  // Instantiates a new feature function in a feature extractor from a feature
-  // descriptor.
-  static Self *Instantiate(GenericFeatureExtractor *extractor,
-                           FeatureFunctionDescriptor *fd,
-                           const std::string &prefix) {
-    Self *f = Self::Create(fd->type());
-    if (f != nullptr) {
-      f->set_extractor(extractor);
-      f->set_descriptor(fd);
-      f->set_prefix(prefix);
-    }
-    return f;
-  }
-
-  // Returns the name of the registry for the feature function.
-  const char *RegistryName() const override { return Self::registry()->name(); }
-
- private:
-  // Special feature function class for resolving variable references. The type
-  // of the feature function is used for resolving the variable reference. When
-  // evaluated it will either get the feature value(s) from the variable portion
-  // of the feature vector, if present, or otherwise it will call the referenced
-  // feature extractor function directly to extract the feature(s).
-  class Reference;
-};
-
-// Base class for features with nested feature functions. The nested functions
-// are of type NES, which may be different from the type of the parent function.
-// NB: NestedFeatureFunction will ensure that all initialization of nested
-// functions takes place during Setup() and Init() -- after the nested features
-// are initialized, the parent feature is initialized via SetupNested() and
-// InitNested(). Alternatively, a derived classes that overrides Setup() and
-// Init() directly should call Parent::Setup(), Parent::Init(), etc. first.
-//
-// Note: NestedFeatureFunction cannot know how to call Preprocess, Evaluate, or
-// Compute, since the nested functions may be of a different type.
-template <class NES, class OBJ, class... ARGS>
-class NestedFeatureFunction : public FeatureFunction<OBJ, ARGS...> {
- public:
-  using Parent = NestedFeatureFunction<NES, OBJ, ARGS...>;
-
-  // Clean up nested functions.
-  ~NestedFeatureFunction() override {
-    // Fully qualified class name, to avoid an ambiguity error when building for
-    // Android.
-    ::libtextclassifier::STLDeleteElements(&nested_);
-  }
-
-  // By default, just appends the nested feature types.
-  void GetFeatureTypes(std::vector<FeatureType *> *types) const override {
-    // It's odd if a NestedFeatureFunction does not have anything nested inside
-    // it, so we crash in debug mode.  Still, nothing should crash in prod mode.
-    TC_DCHECK(!this->nested().empty())
-        << "Nested features require nested features to be defined.";
-    for (auto *function : nested_) function->GetFeatureTypes(types);
-  }
-
-  // Sets up the nested features.
-  bool Setup(TaskContext *context) override {
-    bool success = CreateNested(this->extractor(), this->descriptor(), &nested_,
-                                this->SubPrefix());
-    if (!success) {
-      return false;
-    }
-    for (auto *function : nested_) {
-      if (!function->Setup(context)) return false;
-    }
-    if (!SetupNested(context)) {
-      return false;
-    }
-    return true;
-  }
-
-  // Sets up this NestedFeatureFunction specifically.
-  virtual bool SetupNested(TaskContext *context) { return true; }
-
-  // Initializes the nested features.
-  bool Init(TaskContext *context) override {
-    for (auto *function : nested_) {
-      if (!function->Init(context)) return false;
-    }
-    if (!InitNested(context)) return false;
-    return true;
-  }
-
-  // Initializes this NestedFeatureFunction specifically.
-  virtual bool InitNested(TaskContext *context) { return true; }
-
-  // Gets all the workspaces needed for the nested functions.
-  void RequestWorkspaces(WorkspaceRegistry *registry) override {
-    for (auto *function : nested_) function->RequestWorkspaces(registry);
-  }
-
-  // Returns the list of nested feature functions.
-  const std::vector<NES *> &nested() const { return nested_; }
-
-  // Instantiates nested feature functions for a feature function. Creates and
-  // initializes one feature function for each sub-descriptor in the feature
-  // descriptor.
-  static bool CreateNested(GenericFeatureExtractor *extractor,
-                           FeatureFunctionDescriptor *fd,
-                           std::vector<NES *> *functions,
-                           const std::string &prefix) {
-    for (int i = 0; i < fd->feature_size(); ++i) {
-      FeatureFunctionDescriptor *sub = fd->mutable_feature(i);
-      NES *f = NES::Instantiate(extractor, sub, prefix);
-      if (f == nullptr) {
-        return false;
-      }
-      functions->push_back(f);
-    }
-    return true;
-  }
-
- protected:
-  // The nested feature functions, if any, in order of declaration in the
-  // feature descriptor.  Owned.
-  std::vector<NES *> nested_;
-};
-
-// Base class for a nested feature function that takes nested features with the
-// same signature as these features, i.e. a meta feature. For this class, we can
-// provide preprocessing of the nested features.
-template <class OBJ, class... ARGS>
-class MetaFeatureFunction
-    : public NestedFeatureFunction<FeatureFunction<OBJ, ARGS...>, OBJ,
-                                   ARGS...> {
- public:
-  // Preprocesses using the nested features.
-  void Preprocess(WorkspaceSet *workspaces, OBJ *object) const override {
-    for (auto *function : this->nested_) {
-      function->Preprocess(workspaces, object);
-    }
-  }
-};
-
-// Template for a special type of locator: The locator of type
-// FeatureFunction<OBJ, ARGS...> calls nested functions of type
-// FeatureFunction<OBJ, IDX, ARGS...>, where the derived class DER is
-// responsible for translating by providing the following:
-//
-// // Gets the new additional focus.
-// IDX GetFocus(const WorkspaceSet &workspaces, const OBJ &object);
-//
-// This is useful to e.g. add a token focus to a parser state based on some
-// desired property of that state.
-template <class DER, class OBJ, class IDX, class... ARGS>
-class FeatureAddFocusLocator
-    : public NestedFeatureFunction<FeatureFunction<OBJ, IDX, ARGS...>, OBJ,
-                                   ARGS...> {
- public:
-  void Preprocess(WorkspaceSet *workspaces, OBJ *object) const override {
-    for (auto *function : this->nested_) {
-      function->Preprocess(workspaces, object);
-    }
-  }
-
-  void Evaluate(const WorkspaceSet &workspaces, const OBJ &object, ARGS... args,
-                FeatureVector *result) const override {
-    IDX focus =
-        static_cast<const DER *>(this)->GetFocus(workspaces, object, args...);
-    for (auto *function : this->nested()) {
-      function->Evaluate(workspaces, object, focus, args..., result);
-    }
-  }
-
-  // Returns the first nested feature's computed value.
-  FeatureValue Compute(const WorkspaceSet &workspaces, const OBJ &object,
-                       ARGS... args,
-                       const FeatureVector *result) const override {
-    IDX focus =
-        static_cast<const DER *>(this)->GetFocus(workspaces, object, args...);
-    return this->nested()[0]->Compute(workspaces, object, focus, args...,
-                                      result);
-  }
-};
-
-// CRTP feature locator class. This is a meta feature that modifies ARGS and
-// then calls the nested feature functions with the modified ARGS. Note that in
-// order for this template to work correctly, all of ARGS must be types for
-// which the reference operator & can be interpreted as a pointer to the
-// argument. The derived class DER must implement the UpdateFocus method which
-// takes pointers to the ARGS arguments:
-//
-// // Updates the current arguments.
-// void UpdateArgs(const OBJ &object, ARGS *...args) const;
-template <class DER, class OBJ, class... ARGS>
-class FeatureLocator : public MetaFeatureFunction<OBJ, ARGS...> {
- public:
-  // Feature locators have an additional check that there is no intrinsic type,
-  // but only in debug mode: having an intrinsic type here is odd, but not
-  // enough to motive a crash in prod.
-  void GetFeatureTypes(std::vector<FeatureType *> *types) const override {
-    TC_DCHECK_EQ(this->feature_type(), nullptr)
-        << "FeatureLocators should not have an intrinsic type.";
-    MetaFeatureFunction<OBJ, ARGS...>::GetFeatureTypes(types);
-  }
-
-  // Evaluates the locator.
-  void Evaluate(const WorkspaceSet &workspaces, const OBJ &object, ARGS... args,
-                FeatureVector *result) const override {
-    static_cast<const DER *>(this)->UpdateArgs(workspaces, object, &args...);
-    for (auto *function : this->nested()) {
-      function->Evaluate(workspaces, object, args..., result);
-    }
-  }
-
-  // Returns the first nested feature's computed value.
-  FeatureValue Compute(const WorkspaceSet &workspaces, const OBJ &object,
-                       ARGS... args,
-                       const FeatureVector *result) const override {
-    static_cast<const DER *>(this)->UpdateArgs(workspaces, object, &args...);
-    return this->nested()[0]->Compute(workspaces, object, args..., result);
-  }
-};
-
-// Feature extractor for extracting features from objects of a certain class.
-// Template type parameters are as defined for FeatureFunction.
-template <class OBJ, class... ARGS>
-class FeatureExtractor : public GenericFeatureExtractor {
- public:
-  // Feature function type for top-level functions in the feature extractor.
-  typedef FeatureFunction<OBJ, ARGS...> Function;
-  typedef FeatureExtractor<OBJ, ARGS...> Self;
-
-  // Feature locator type for the feature extractor.
-  template <class DER>
-  using Locator = FeatureLocator<DER, OBJ, ARGS...>;
-
-  // Initializes feature extractor.
-  FeatureExtractor() {}
-
-  ~FeatureExtractor() override {
-    // Fully qualified class name, to avoid an ambiguity error when building for
-    // Android.
-    ::libtextclassifier::STLDeleteElements(&functions_);
-  }
-
-  // Sets up the feature extractor. Note that only top-level functions exist
-  // until Setup() is called. This does not take ownership over the context,
-  // which must outlive this.
-  bool Setup(TaskContext *context) {
-    for (Function *function : functions_) {
-      if (!function->Setup(context)) return false;
-    }
-    return true;
-  }
-
-  // Initializes the feature extractor.  Must be called after Setup().  This
-  // does not take ownership over the context, which must outlive this.
-  bool Init(TaskContext *context) {
-    for (Function *function : functions_) {
-      if (!function->Init(context)) return false;
-    }
-    if (!this->InitializeFeatureTypes()) {
-      return false;
-    }
-    return true;
-  }
-
-  // Requests workspaces from the registry. Must be called after Init(), and
-  // before Preprocess(). Does not take ownership over registry. This should be
-  // the same registry used to initialize the WorkspaceSet used in Preprocess()
-  // and ExtractFeatures(). NB: This is a different ordering from that used in
-  // SentenceFeatureRepresentation style feature computation.
-  void RequestWorkspaces(WorkspaceRegistry *registry) {
-    for (auto *function : functions_) function->RequestWorkspaces(registry);
-  }
-
-  // Preprocesses the object using feature functions for the phase.  Must be
-  // called before any calls to ExtractFeatures() on that object and phase.
-  void Preprocess(WorkspaceSet *workspaces, OBJ *object) const {
-    for (Function *function : functions_) {
-      function->Preprocess(workspaces, object);
-    }
-  }
-
-  // Extracts features from an object with a focus. This invokes all the
-  // top-level feature functions in the feature extractor. Only feature
-  // functions belonging to the specified phase are invoked.
-  void ExtractFeatures(const WorkspaceSet &workspaces, const OBJ &object,
-                       ARGS... args, FeatureVector *result) const {
-    result->reserve(this->feature_types());
-
-    // Extract features.
-    for (int i = 0; i < functions_.size(); ++i) {
-      functions_[i]->Evaluate(workspaces, object, args..., result);
-    }
-  }
-
- private:
-  // Creates and initializes all feature functions in the feature extractor.
-  bool InitializeFeatureFunctions() override {
-    // Create all top-level feature functions.
-    for (int i = 0; i < descriptor().feature_size(); ++i) {
-      FeatureFunctionDescriptor *fd = mutable_descriptor()->mutable_feature(i);
-      Function *function = Function::Instantiate(this, fd, "");
-      if (function == nullptr) return false;
-      functions_.push_back(function);
-    }
-    return true;
-  }
-
-  // Collect all feature types used in the feature extractor.
-  void GetFeatureTypes(std::vector<FeatureType *> *types) const override {
-    for (Function *function : functions_) {
-      function->GetFeatureTypes(types);
-    }
-  }
-
-  // Top-level feature functions (and variables) in the feature extractor.
-  // Owned.  INVARIANT: contains only non-null pointers.
-  std::vector<Function *> functions_;
-};
-
-#define REGISTER_FEATURE_FUNCTION(base, name, component) \
-  REGISTER_CLASS_COMPONENT(base, name, component)
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_FEATURE_EXTRACTOR_H_
diff --git a/common/feature-types.h b/common/feature-types.h
deleted file mode 100644
index 92814d9..0000000
--- a/common/feature-types.h
+++ /dev/null
@@ -1,189 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Common feature types for parser components.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_FEATURE_TYPES_H_
-#define LIBTEXTCLASSIFIER_COMMON_FEATURE_TYPES_H_
-
-#include <algorithm>
-#include <map>
-#include <string>
-#include <utility>
-
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/strings/numbers.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// TODO(djweiss) Clean this up as well.
-// Use the same type for feature values as is used for predicated.
-typedef int64 Predicate;
-typedef Predicate FeatureValue;
-
-// Each feature value in a feature vector has a feature type. The feature type
-// is used for converting feature type and value pairs to predicate values. The
-// feature type can also return names for feature values and calculate the size
-// of the feature value domain. The FeatureType class is abstract and must be
-// specialized for the concrete feature types.
-class FeatureType {
- public:
-  // Initializes a feature type.
-  explicit FeatureType(const std::string &name)
-      : name_(name), base_(0),
-        is_continuous_(name.find("continuous") != std::string::npos) {
-  }
-
-  virtual ~FeatureType() {}
-
-  // Converts a feature value to a name.
-  virtual std::string GetFeatureValueName(FeatureValue value) const = 0;
-
-  // Returns the size of the feature values domain.
-  virtual int64 GetDomainSize() const = 0;
-
-  // Returns the feature type name.
-  const std::string &name() const { return name_; }
-
-  Predicate base() const { return base_; }
-  void set_base(Predicate base) { base_ = base; }
-
-  // Returns true iff this feature is continuous; see FloatFeatureValue.
-  bool is_continuous() const { return is_continuous_; }
-
- private:
-  // Feature type name.
-  std::string name_;
-
-  // "Base" feature value: i.e. a "slot" in a global ordering of features.
-  Predicate base_;
-
-  // See doc for is_continuous().
-  bool is_continuous_;
-};
-
-// Feature type that is defined using an explicit map from FeatureValue to
-// std::string values.  This can reduce some of the boilerplate when defining
-// features that generate enum values.  Example usage:
-//
-//   class BeverageSizeFeature : public FeatureFunction<Beverage>
-//     enum FeatureValue { SMALL, MEDIUM, LARGE };  // values for this feature
-//     void Init(TaskContext *context) override {
-//       set_feature_type(new EnumFeatureType("beverage_size",
-//           {{SMALL, "SMALL"}, {MEDIUM, "MEDIUM"}, {LARGE, "LARGE"}});
-//     }
-//     [...]
-//   };
-class EnumFeatureType : public FeatureType {
- public:
-  EnumFeatureType(const std::string &name,
-                  const std::map<FeatureValue, std::string> &value_names)
-      : FeatureType(name), value_names_(value_names) {
-    for (const auto &pair : value_names) {
-      TC_DCHECK_GE(pair.first, 0)
-          << "Invalid feature value: " << pair.first << ", " << pair.second;
-      domain_size_ = std::max(domain_size_, pair.first + 1);
-    }
-  }
-
-  // Returns the feature name for a given feature value.
-  std::string GetFeatureValueName(FeatureValue value) const override {
-    auto it = value_names_.find(value);
-    if (it == value_names_.end()) {
-      TC_LOG(ERROR) << "Invalid feature value " << value << " for " << name();
-      return "<INVALID>";
-    }
-    return it->second;
-  }
-
-  // Returns the number of possible values for this feature type. This is one
-  // greater than the largest value in the value_names map.
-  FeatureValue GetDomainSize() const override { return domain_size_; }
-
- protected:
-  // Maximum possible value this feature could take.
-  FeatureValue domain_size_ = 0;
-
-  // Names of feature values.
-  std::map<FeatureValue, std::string> value_names_;
-};
-
-// Feature type for binary features.
-class BinaryFeatureType : public FeatureType {
- public:
-  BinaryFeatureType(const std::string &name, const std::string &off,
-                    const std::string &on)
-      : FeatureType(name), off_(off), on_(on) {}
-
-  // Returns the feature name for a given feature value.
-  std::string GetFeatureValueName(FeatureValue value) const override {
-    if (value == 0) return off_;
-    if (value == 1) return on_;
-    return "";
-  }
-
-  // Binary features always have two feature values.
-  FeatureValue GetDomainSize() const override { return 2; }
-
- private:
-  // Feature value names for on and off.
-  std::string off_;
-  std::string on_;
-};
-
-// Feature type for numeric features.
-class NumericFeatureType : public FeatureType {
- public:
-  // Initializes numeric feature.
-  NumericFeatureType(const std::string &name, FeatureValue size)
-      : FeatureType(name), size_(size) {}
-
-  // Returns numeric feature value.
-  std::string GetFeatureValueName(FeatureValue value) const override {
-    if (value < 0) return "";
-    return IntToString(value);
-  }
-
-  // Returns the number of feature values.
-  FeatureValue GetDomainSize() const override { return size_; }
-
- private:
-  // The underlying size of the numeric feature.
-  FeatureValue size_;
-};
-
-// Feature type for byte features, including an "outside" value.
-class ByteFeatureType : public NumericFeatureType {
- public:
-  explicit ByteFeatureType(const std::string &name)
-      : NumericFeatureType(name, 257) {}
-
-  std::string GetFeatureValueName(FeatureValue value) const override {
-    if (value == 256) {
-      return "<NULL>";
-    }
-    std::string result;
-    result += static_cast<char>(value);
-    return result;
-  }
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_FEATURE_TYPES_H_
diff --git a/common/file-utils.cc b/common/file-utils.cc
deleted file mode 100644
index 6ae4442..0000000
--- a/common/file-utils.cc
+++ /dev/null
@@ -1,76 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/file-utils.h"
-
-#include <fcntl.h>
-#include <stdio.h>
-#include <sys/stat.h>
-#include <sys/types.h>
-
-#include <fstream>
-#include <memory>
-#include <string>
-
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace file_utils {
-
-bool GetFileContent(const std::string &filename, std::string *content) {
-  std::ifstream input_stream(filename, std::ifstream::binary);
-  if (input_stream.fail()) {
-    TC_LOG(INFO) << "Error opening " << filename;
-    return false;
-  }
-
-  content->assign(
-      std::istreambuf_iterator<char>(input_stream),
-      std::istreambuf_iterator<char>());
-
-  if (input_stream.fail()) {
-    TC_LOG(ERROR) << "Error reading " << filename;
-    return false;
-  }
-
-  TC_LOG(INFO) << "Successfully read " << filename;
-  return true;
-}
-
-bool FileExists(const std::string &filename) {
-  struct stat s = {0};
-  if (!stat(filename.c_str(), &s)) {
-    return s.st_mode & S_IFREG;
-  } else {
-    return false;
-  }
-}
-
-bool DirectoryExists(const std::string &dirpath) {
-  struct stat s = {0};
-  if (!stat(dirpath.c_str(), &s)) {
-    return s.st_mode & S_IFDIR;
-  } else {
-    return false;
-  }
-}
-
-}  // namespace file_utils
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/file-utils.h b/common/file-utils.h
deleted file mode 100644
index e2a60f2..0000000
--- a/common/file-utils.h
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_FILE_UTILS_H_
-#define LIBTEXTCLASSIFIER_COMMON_FILE_UTILS_H_
-
-#include <cstddef>
-#include <memory>
-#include <string>
-
-#include "common/config.h"
-
-#if PORTABLE_SAFT_MOBILE
-#include <google/protobuf/io/zero_copy_stream_impl_lite.h>
-#endif
-
-#include "common/mmap.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace file_utils {
-
-// Reads the entire content of a file into a string.  Returns true on success,
-// false on error.
-bool GetFileContent(const std::string &filename, std::string *content);
-
-// Parses a proto from its serialized representation in memory.  That
-// representation starts at address sp.data() and contains exactly sp.size()
-// bytes.  Returns true on success, false otherwise.
-template<class Proto>
-bool ParseProtoFromMemory(StringPiece sp, Proto *proto) {
-  if (!sp.data()) {
-    // Avoid passing a nullptr to ArrayInputStream below.
-    return false;
-  }
-#if PORTABLE_SAFT_MOBILE
-  ::google::protobuf::io::ArrayInputStream stream(sp.data(), sp.size());
-  return proto->ParseFromZeroCopyStream(&stream);
-#else
-
-  std::string data(sp.data(), sp.size());
-  return proto->ParseFromString(data);
-#endif
-}
-
-// Parses a proto from a file.  Returns true on success, false otherwise.
-//
-// Note: the entire content of the file should be the binary (not
-// human-readable) serialization of a protocol buffer.
-//
-// Note: when we compile for Android, the proto parsing methods need to know the
-// type of the message they are parsing.  We use template polymorphism for that.
-template<class Proto>
-bool ReadProtoFromFile(const std::string &filename, Proto *proto) {
-  ScopedMmap scoped_mmap(filename);
-  const MmapHandle &handle = scoped_mmap.handle();
-  if (!handle.ok()) {
-    return false;
-  }
-  return ParseProtoFromMemory(handle.to_stringpiece(), proto);
-}
-
-// Returns true if filename is the name of an existing file, and false
-// otherwise.
-bool FileExists(const std::string &filename);
-
-// Returns true if dirpath is the path to an existing directory, and false
-// otherwise.
-bool DirectoryExists(const std::string &dirpath);
-
-}  // namespace file_utils
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_FILE_UTILS_H_
diff --git a/common/float16.h b/common/float16.h
deleted file mode 100644
index 8b52be3..0000000
--- a/common/float16.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_FLOAT16_H_
-#define LIBTEXTCLASSIFIER_COMMON_FLOAT16_H_
-
-#include "util/base/casts.h"
-#include "util/base/integral_types.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// 16 bit encoding of a float.  NOTE: can't be used directly for computation:
-// one first needs to convert it to a normal float, using Float16To32.
-//
-// Documentation copied from original file:
-//
-// Compact 16-bit encoding of floating point numbers. This
-// representation uses 1 bit for the sign, 8 bits for the exponent and
-// 7 bits for the mantissa.  It is assumed that floats are in IEEE 754
-// format so a float16 is just bits 16-31 of a single precision float.
-//
-// NOTE: The IEEE floating point standard defines a float16 format that
-// is different than this format (it has fewer bits of exponent and more
-// bits of mantissa).  We don't use that format here because conversion
-// to/from 32-bit floats is more complex for that format, and the
-// conversion for this format is very simple.
-//
-// <---------float16------------>
-// s e e e e e e e e f f f f f f f f f f f f f f f f f f f f f f f
-// <------------------------------float-------------------------->
-// 3 3             2 2             1 1                           0
-// 1 0             3 2             5 4                           0
-
-typedef uint16 float16;
-
-static inline float16 Float32To16(float f) {
-  // Note that we just truncate the mantissa bits: we make no effort to
-  // do any smarter rounding.
-  return (bit_cast<uint32>(f) >> 16) & 0xffff;
-}
-
-static inline float Float16To32(float16 f) {
-  // We fill in the new mantissa bits with 0, and don't do anything smarter.
-  return bit_cast<float>(f << 16);
-}
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_FLOAT16_H_
diff --git a/common/fml-parser.cc b/common/fml-parser.cc
deleted file mode 100644
index 2964671..0000000
--- a/common/fml-parser.cc
+++ /dev/null
@@ -1,329 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/fml-parser.h"
-
-#include <ctype.h>
-#include <string>
-
-#include "util/base/logging.h"
-#include "util/strings/numbers.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace {
-inline bool IsValidCharAtStartOfIdentifier(char c) {
-  return isalpha(c) || (c == '_') || (c == '/');
-}
-
-// Returns true iff character c can appear inside an identifier.
-inline bool IsValidCharInsideIdentifier(char c) {
-  return isalnum(c) || (c == '_') || (c == '-') || (c == '/');
-}
-
-// Returns true iff character c can appear at the beginning of a number.
-inline bool IsValidCharAtStartOfNumber(char c) {
-  return isdigit(c) || (c == '+') || (c == '-');
-}
-
-// Returns true iff character c can appear inside a number.
-inline bool IsValidCharInsideNumber(char c) {
-  return isdigit(c) || (c == '.');
-}
-}  // namespace
-
-bool FMLParser::Initialize(const std::string &source) {
-  // Initialize parser state.
-  source_ = source;
-  current_ = source_.begin();
-  item_start_ = line_start_ = current_;
-  line_number_ = item_line_number_ = 1;
-
-  // Read first input item.
-  return NextItem();
-}
-
-void FMLParser::ReportError(const std::string &error_message) {
-  const int position = item_start_ - line_start_ + 1;
-  const std::string line(line_start_, current_);
-
-  TC_LOG(ERROR) << "Error in feature model, line " << item_line_number_
-                << ", position " << position << ": " << error_message
-                << "\n    " << line << " <--HERE";
-}
-
-void FMLParser::Next() {
-  // Move to the next input character. If we are at a line break update line
-  // number and line start position.
-  if (CurrentChar() == '\n') {
-    ++line_number_;
-    ++current_;
-    line_start_ = current_;
-  } else {
-    ++current_;
-  }
-}
-
-bool FMLParser::NextItem() {
-  // Skip white space and comments.
-  while (!eos()) {
-    if (CurrentChar() == '#') {
-      // Skip comment.
-      while (!eos() && CurrentChar() != '\n') Next();
-    } else if (isspace(CurrentChar())) {
-      // Skip whitespace.
-      while (!eos() && isspace(CurrentChar())) Next();
-    } else {
-      break;
-    }
-  }
-
-  // Record start position for next item.
-  item_start_ = current_;
-  item_line_number_ = line_number_;
-
-  // Check for end of input.
-  if (eos()) {
-    item_type_ = END;
-    return true;
-  }
-
-  // Parse number.
-  if (IsValidCharAtStartOfNumber(CurrentChar())) {
-    std::string::iterator start = current_;
-    Next();
-    while (!eos() && IsValidCharInsideNumber(CurrentChar())) Next();
-    item_text_.assign(start, current_);
-    item_type_ = NUMBER;
-    return true;
-  }
-
-  // Parse std::string.
-  if (CurrentChar() == '"') {
-    Next();
-    std::string::iterator start = current_;
-    while (CurrentChar() != '"') {
-      if (eos()) {
-        ReportError("Unterminated string");
-        return false;
-      }
-      Next();
-    }
-    item_text_.assign(start, current_);
-    item_type_ = STRING;
-    Next();
-    return true;
-  }
-
-  // Parse identifier name.
-  if (IsValidCharAtStartOfIdentifier(CurrentChar())) {
-    std::string::iterator start = current_;
-    while (!eos() && IsValidCharInsideIdentifier(CurrentChar())) {
-      Next();
-    }
-    item_text_.assign(start, current_);
-    item_type_ = NAME;
-    return true;
-  }
-
-  // Single character item.
-  item_type_ = CurrentChar();
-  Next();
-  return true;
-}
-
-bool FMLParser::Parse(const std::string &source,
-                      FeatureExtractorDescriptor *result) {
-  // Initialize parser.
-  if (!Initialize(source)) {
-    return false;
-  }
-
-  while (item_type_ != END) {
-    // Current item should be a feature name.
-    if (item_type_ != NAME) {
-      ReportError("Feature type name expected");
-      return false;
-    }
-    std::string name = item_text_;
-    if (!NextItem()) {
-      return false;
-    }
-
-    // Parse feature.
-    FeatureFunctionDescriptor *descriptor = result->add_feature();
-    descriptor->set_type(name);
-    if (!ParseFeature(descriptor)) {
-      return false;
-    }
-  }
-
-  return true;
-}
-
-bool FMLParser::ParseFeature(FeatureFunctionDescriptor *result) {
-  // Parse argument and parameters.
-  if (item_type_ == '(') {
-    if (!NextItem()) return false;
-    if (!ParseParameter(result)) return false;
-    while (item_type_ == ',') {
-      if (!NextItem()) return false;
-      if (!ParseParameter(result)) return false;
-    }
-
-    if (item_type_ != ')') {
-      ReportError(") expected");
-      return false;
-    }
-    if (!NextItem()) return false;
-  }
-
-  // Parse feature name.
-  if (item_type_ == ':') {
-    if (!NextItem()) return false;
-    if (item_type_ != NAME && item_type_ != STRING) {
-      ReportError("Feature name expected");
-      return false;
-    }
-    std::string name = item_text_;
-    if (!NextItem()) return false;
-
-    // Set feature name.
-    result->set_name(name);
-  }
-
-  // Parse sub-features.
-  if (item_type_ == '.') {
-    // Parse dotted sub-feature.
-    if (!NextItem()) return false;
-    if (item_type_ != NAME) {
-      ReportError("Feature type name expected");
-      return false;
-    }
-    std::string type = item_text_;
-    if (!NextItem()) return false;
-
-    // Parse sub-feature.
-    FeatureFunctionDescriptor *subfeature = result->add_feature();
-    subfeature->set_type(type);
-    if (!ParseFeature(subfeature)) return false;
-  } else if (item_type_ == '{') {
-    // Parse sub-feature block.
-    if (!NextItem()) return false;
-    while (item_type_ != '}') {
-      if (item_type_ != NAME) {
-        ReportError("Feature type name expected");
-        return false;
-      }
-      std::string type = item_text_;
-      if (!NextItem()) return false;
-
-      // Parse sub-feature.
-      FeatureFunctionDescriptor *subfeature = result->add_feature();
-      subfeature->set_type(type);
-      if (!ParseFeature(subfeature)) return false;
-    }
-    if (!NextItem()) return false;
-  }
-  return true;
-}
-
-bool FMLParser::ParseParameter(FeatureFunctionDescriptor *result) {
-  if (item_type_ == NUMBER) {
-    int32 argument;
-    if (!ParseInt32(item_text_.c_str(), &argument)) {
-      ReportError("Unable to parse number");
-      return false;
-    }
-    if (!NextItem()) return false;
-
-    // Set default argument for feature.
-    result->set_argument(argument);
-  } else if (item_type_ == NAME) {
-    std::string name = item_text_;
-    if (!NextItem()) return false;
-    if (item_type_ != '=') {
-      ReportError("= expected");
-      return false;
-    }
-    if (!NextItem()) return false;
-    if (item_type_ >= END) {
-      ReportError("Parameter value expected");
-      return false;
-    }
-    std::string value = item_text_;
-    if (!NextItem()) return false;
-
-    // Add parameter to feature.
-    Parameter *parameter;
-    parameter = result->add_parameter();
-    parameter->set_name(name);
-    parameter->set_value(value);
-  } else {
-    ReportError("Syntax error in parameter list");
-    return false;
-  }
-  return true;
-}
-
-void ToFMLFunction(const FeatureFunctionDescriptor &function,
-                   std::string *output) {
-  output->append(function.type());
-  if (function.argument() != 0 || function.parameter_size() > 0) {
-    output->append("(");
-    bool first = true;
-    if (function.argument() != 0) {
-      output->append(IntToString(function.argument()));
-      first = false;
-    }
-    for (int i = 0; i < function.parameter_size(); ++i) {
-      if (!first) output->append(",");
-      output->append(function.parameter(i).name());
-      output->append("=");
-      output->append("\"");
-      output->append(function.parameter(i).value());
-      output->append("\"");
-      first = false;
-    }
-    output->append(")");
-  }
-}
-
-void ToFML(const FeatureFunctionDescriptor &function, std::string *output) {
-  ToFMLFunction(function, output);
-  if (function.feature_size() == 1) {
-    output->append(".");
-    ToFML(function.feature(0), output);
-  } else if (function.feature_size() > 1) {
-    output->append(" { ");
-    for (int i = 0; i < function.feature_size(); ++i) {
-      if (i > 0) output->append(" ");
-      ToFML(function.feature(i), output);
-    }
-    output->append(" } ");
-  }
-}
-
-void ToFML(const FeatureExtractorDescriptor &extractor, std::string *output) {
-  for (int i = 0; i < extractor.feature_size(); ++i) {
-    ToFML(extractor.feature(i), output);
-    output->append("\n");
-  }
-}
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/fml-parser.h b/common/fml-parser.h
deleted file mode 100644
index b6b9da2..0000000
--- a/common/fml-parser.h
+++ /dev/null
@@ -1,147 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Feature modeling language (fml) parser.
-//
-// BNF grammar for fml:
-//
-// <feature model> ::= { <feature extractor> }
-//
-// <feature extractor> ::= <extractor spec> |
-//                         <extractor spec> '.' <feature extractor> |
-//                         <extractor spec> '{' { <feature extractor> } '}'
-//
-// <extractor spec> ::= <extractor type>
-//                      [ '(' <parameter list> ')' ]
-//                      [ ':' <extractor name> ]
-//
-// <parameter list> = ( <parameter> | <argument> ) { ',' <parameter> }
-//
-// <parameter> ::= <parameter name> '=' <parameter value>
-//
-// <extractor type> ::= NAME
-// <extractor name> ::= NAME | STRING
-// <argument> ::= NUMBER
-// <parameter name> ::= NAME
-// <parameter value> ::= NUMBER | STRING | NAME
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_FML_PARSER_H_
-#define LIBTEXTCLASSIFIER_COMMON_FML_PARSER_H_
-
-#include <string>
-#include <vector>
-
-#include "common/feature-descriptors.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-class FMLParser {
- public:
-  // Parses fml specification into feature extractor descriptor.
-  // Returns true on success, false on error (e.g., syntax errors).
-  bool Parse(const std::string &source, FeatureExtractorDescriptor *result);
-
- private:
-  // Initializes the parser with the source text.
-  // Returns true on success, false on syntax error.
-  bool Initialize(const std::string &source);
-
-  // Outputs an error message, with context info, and sets error_ to true.
-  void ReportError(const std::string &error_message);
-
-  // Moves to the next input character.
-  void Next();
-
-  // Moves to the next input item.  Sets item_text_ and item_type_ accordingly.
-  // Returns true on success, false on syntax error.
-  bool NextItem();
-
-  // Parses a feature descriptor.
-  // Returns true on success, false on syntax error.
-  bool ParseFeature(FeatureFunctionDescriptor *result);
-
-  // Parses a parameter specification.
-  // Returns true on success, false on syntax error.
-  bool ParseParameter(FeatureFunctionDescriptor *result);
-
-  // Returns true if end of source input has been reached.
-  bool eos() const { return current_ >= source_.end(); }
-
-  // Returns current character.  Other methods should access the current
-  // character through this method (instead of using *current_ directly): this
-  // method performs extra safety checks.
-  //
-  // In case of an unsafe access, returns '\0'.
-  char CurrentChar() const {
-    if ((current_ >= source_.begin()) && (current_ < source_.end())) {
-      return *current_;
-    } else {
-      TC_LOG(ERROR) << "Unsafe char read";
-      return '\0';
-    }
-  }
-
-  // Item types.
-  enum ItemTypes {
-    END = 0,
-    NAME = -1,
-    NUMBER = -2,
-    STRING = -3,
-  };
-
-  // Source text.
-  std::string source_;
-
-  // Current input position.
-  std::string::iterator current_;
-
-  // Line number for current input position.
-  int line_number_;
-
-  // Start position for current item.
-  std::string::iterator item_start_;
-
-  // Start position for current line.
-  std::string::iterator line_start_;
-
-  // Line number for current item.
-  int item_line_number_;
-
-  // Item type for current item. If this is positive it is interpreted as a
-  // character. If it is negative it is interpreted as an item type.
-  int item_type_;
-
-  // Text for current item.
-  std::string item_text_;
-};
-
-// Converts a FeatureFunctionDescriptor into an FML spec (reverse of parsing).
-void ToFML(const FeatureFunctionDescriptor &function, std::string *output);
-
-// Like ToFML, but doesn't go into the nested functions.  Instead, it generates
-// a string that starts with the name of the feature extraction function and
-// next, in-between parentheses, the parameters, separated by comma.
-// Intuitively, the constructed string is the prefix of ToFML, before the "{"
-// that starts the nested features.
-void ToFMLFunction(const FeatureFunctionDescriptor &function,
-                   std::string *output);
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_FML_PARSER_H_
diff --git a/common/fml-parser_test.cc b/common/fml-parser_test.cc
deleted file mode 100644
index b46048f..0000000
--- a/common/fml-parser_test.cc
+++ /dev/null
@@ -1,157 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/fml-parser.h"
-
-#include "common/feature-descriptors.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-TEST(FMLParserTest, NoFeature) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  const std::string kFeatureName = "";
-  EXPECT_TRUE(fml_parser.Parse(kFeatureName, &descriptor));
-  EXPECT_EQ(0, descriptor.feature_size());
-}
-
-TEST(FMLParserTest, FeatureWithNoParams) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  const std::string kFeatureName = "continuous-bag-of-relevant-scripts";
-  EXPECT_TRUE(fml_parser.Parse(kFeatureName, &descriptor));
-  EXPECT_EQ(1, descriptor.feature_size());
-  EXPECT_EQ(kFeatureName, descriptor.feature(0).type());
-}
-
-TEST(FMLParserTest, FeatureWithOneKeywordParameter) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  EXPECT_TRUE(fml_parser.Parse("myfeature(start=2)", &descriptor));
-  EXPECT_EQ(1, descriptor.feature_size());
-  EXPECT_EQ("myfeature", descriptor.feature(0).type());
-  EXPECT_EQ(1, descriptor.feature(0).parameter_size());
-  EXPECT_EQ("start", descriptor.feature(0).parameter(0).name());
-  EXPECT_EQ("2", descriptor.feature(0).parameter(0).value());
-  EXPECT_FALSE(descriptor.feature(0).has_argument());
-}
-
-TEST(FMLParserTest, FeatureWithDefaultArgumentNegative) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  EXPECT_TRUE(fml_parser.Parse("offset(-3)", &descriptor));
-  EXPECT_EQ(1, descriptor.feature_size());
-  EXPECT_EQ("offset", descriptor.feature(0).type());
-  EXPECT_EQ(0, descriptor.feature(0).parameter_size());
-  EXPECT_EQ(-3, descriptor.feature(0).argument());
-}
-
-TEST(FMLParserTest, FeatureWithDefaultArgumentPositive) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  EXPECT_TRUE(fml_parser.Parse("delta(7)", &descriptor));
-  EXPECT_EQ(1, descriptor.feature_size());
-  EXPECT_EQ("delta", descriptor.feature(0).type());
-  EXPECT_EQ(0, descriptor.feature(0).parameter_size());
-  EXPECT_EQ(7, descriptor.feature(0).argument());
-}
-
-TEST(FMLParserTest, FeatureWithDefaultArgumentZero) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  EXPECT_TRUE(fml_parser.Parse("delta(0)", &descriptor));
-  EXPECT_EQ(1, descriptor.feature_size());
-  EXPECT_EQ("delta", descriptor.feature(0).type());
-  EXPECT_EQ(0, descriptor.feature(0).parameter_size());
-  EXPECT_EQ(0, descriptor.feature(0).argument());
-}
-
-TEST(FMLParserTest, FeatureWithManyKeywordParameters) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  EXPECT_TRUE(fml_parser.Parse("myfeature(ratio=0.316,start=2,name=\"foo\")",
-                               &descriptor));
-  EXPECT_EQ(1, descriptor.feature_size());
-  EXPECT_EQ("myfeature", descriptor.feature(0).type());
-  EXPECT_EQ(3, descriptor.feature(0).parameter_size());
-  EXPECT_EQ("ratio", descriptor.feature(0).parameter(0).name());
-  EXPECT_EQ("0.316", descriptor.feature(0).parameter(0).value());
-  EXPECT_EQ("start", descriptor.feature(0).parameter(1).name());
-  EXPECT_EQ("2", descriptor.feature(0).parameter(1).value());
-  EXPECT_EQ("name", descriptor.feature(0).parameter(2).name());
-  EXPECT_EQ("foo", descriptor.feature(0).parameter(2).value());
-  EXPECT_FALSE(descriptor.feature(0).has_argument());
-}
-
-TEST(FMLParserTest, FeatureWithAllKindsOfParameters) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  EXPECT_TRUE(
-      fml_parser.Parse("myfeature(17,ratio=0.316,start=2)", &descriptor));
-  EXPECT_EQ(1, descriptor.feature_size());
-  EXPECT_EQ("myfeature", descriptor.feature(0).type());
-  EXPECT_EQ(2, descriptor.feature(0).parameter_size());
-  EXPECT_EQ("ratio", descriptor.feature(0).parameter(0).name());
-  EXPECT_EQ("0.316", descriptor.feature(0).parameter(0).value());
-  EXPECT_EQ("start", descriptor.feature(0).parameter(1).name());
-  EXPECT_EQ("2", descriptor.feature(0).parameter(1).value());
-  EXPECT_EQ(17, descriptor.feature(0).argument());
-}
-
-TEST(FMLParserTest, FeatureWithWhitespaces) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  EXPECT_TRUE(fml_parser.Parse(
-      "  myfeature\t\t\t\n(17,\nratio=0.316  ,  start=2)  ", &descriptor));
-  EXPECT_EQ(1, descriptor.feature_size());
-  EXPECT_EQ("myfeature", descriptor.feature(0).type());
-  EXPECT_EQ(2, descriptor.feature(0).parameter_size());
-  EXPECT_EQ("ratio", descriptor.feature(0).parameter(0).name());
-  EXPECT_EQ("0.316", descriptor.feature(0).parameter(0).value());
-  EXPECT_EQ("start", descriptor.feature(0).parameter(1).name());
-  EXPECT_EQ("2", descriptor.feature(0).parameter(1).value());
-  EXPECT_EQ(17, descriptor.feature(0).argument());
-}
-
-TEST(FMLParserTest, Broken_ParamWithoutValue) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  EXPECT_FALSE(
-      fml_parser.Parse("myfeature(17,ratio=0.316,start)", &descriptor));
-}
-
-TEST(FMLParserTest, Broken_MissingCloseParen) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  EXPECT_FALSE(fml_parser.Parse("myfeature(17,ratio=0.316", &descriptor));
-}
-
-TEST(FMLParserTest, Broken_MissingOpenParen) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  EXPECT_FALSE(fml_parser.Parse("myfeature17,ratio=0.316)", &descriptor));
-}
-
-TEST(FMLParserTest, Broken_MissingQuote) {
-  FMLParser fml_parser;
-  FeatureExtractorDescriptor descriptor;
-  EXPECT_FALSE(fml_parser.Parse("count(17,name=\"foo)", &descriptor));
-}
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/list-of-strings.proto b/common/list-of-strings.proto
deleted file mode 100644
index 5ba45ed..0000000
--- a/common/list-of-strings.proto
+++ /dev/null
@@ -1,22 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-package libtextclassifier.nlp_core;
-
-message ListOfStrings {
-  repeated string element = 1;
-}
diff --git a/common/little-endian-data.h b/common/little-endian-data.h
deleted file mode 100644
index e3bc88f..0000000
--- a/common/little-endian-data.h
+++ /dev/null
@@ -1,124 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_LITTLE_ENDIAN_DATA_H_
-#define LIBTEXTCLASSIFIER_COMMON_LITTLE_ENDIAN_DATA_H_
-
-#include <algorithm>
-#include <string>
-#include <vector>
-
-#include "util/base/endian.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// Swaps the sizeof(T) bytes that start at addr.  E.g., if sizeof(T) == 2,
-// then (addr[0], addr[1]) -> (addr[1], addr[0]).  Useful for little endian
-// <-> big endian conversions.
-template <class T>
-void SwapBytes(T *addr) {
-  char *char_ptr = reinterpret_cast<char *>(addr);
-  std::reverse(char_ptr, char_ptr + sizeof(T));
-}
-
-// Assuming addr points to a piece of data of type T, with its bytes in the
-// little/big endian order specific to the machine this code runs on, this
-// method will re-arrange the bytes (in place) in little-endian order.
-template <class T>
-void HostToLittleEndian(T *addr) {
-  if (LittleEndian::IsLittleEndian()) {
-    // Do nothing: current machine is little-endian.
-  } else {
-    SwapBytes(addr);
-  }
-}
-
-// Reverse of HostToLittleEndian.
-template <class T>
-void LittleEndianToHost(T *addr) {
-  // It turns out it's the same function: on little-endian machines, do nothing
-  // (source and target formats are identical).  Otherwise, swap bytes.
-  HostToLittleEndian(addr);
-}
-
-// Returns string obtained by concatenating the bytes of the elements from a
-// vector (in order: v[0], v[1], etc).  If the type T requires more than one
-// byte, the byte for each element are first converted to little-endian format.
-template<typename T>
-std::string GetDataBytesInLittleEndianOrder(const std::vector<T> &v) {
-  std::string data_bytes;
-  for (const T element : v) {
-    T little_endian_element = element;
-    HostToLittleEndian(&little_endian_element);
-    data_bytes.append(
-        reinterpret_cast<const char *>(&little_endian_element),
-        sizeof(T));
-  }
-  return data_bytes;
-}
-
-// Performs reverse of GetDataBytesInLittleEndianOrder.
-//
-// I.e., decodes the data bytes from parameter bytes into num_elements Ts, and
-// places them in the vector v (previous content of that vector is erased).
-//
-// We expect bytes to contain the concatenation of the bytes for exactly
-// num_elements elements of type T.  If the type T requires more than one byte,
-// those bytes should be arranged in little-endian form.
-//
-// Returns true on success and false otherwise (e.g., bytes has the wrong size).
-// Note: we do not want to crash on corrupted data (some clients, e..g, GMSCore,
-// have asked us not to do so).  Instead, we report the error and let the client
-// decide what to do.  On error, we also fill the vector with zeros, such that
-// at least the dimension of v matches expectations.
-template<typename T>
-bool FillVectorFromDataBytesInLittleEndian(
-    const std::string &bytes, int num_elements, std::vector<T> *v) {
-  if (bytes.size() != num_elements * sizeof(T)) {
-    TC_LOG(ERROR) << "Wrong number of bytes: actual " << bytes.size()
-                  << " vs expected " << num_elements
-                  << " elements of sizeof(element) = " << sizeof(T)
-                  << " bytes each ; will fill vector with zeros";
-    v->assign(num_elements, static_cast<T>(0));
-    return false;
-  }
-  v->clear();
-  v->reserve(num_elements);
-  const T *start = reinterpret_cast<const T *>(bytes.data());
-  if (LittleEndian::IsLittleEndian() || (sizeof(T) == 1)) {
-    // Fast in the common case ([almost] all hardware today is little-endian):
-    // if same endianness (or type T requires a single byte and endianness
-    // irrelevant), just use the bytes.
-    v->assign(start, start + num_elements);
-  } else {
-    // Slower (but very rare case): this code runs on a big endian machine and
-    // the type T requires more than one byte.  Hence, some conversion is
-    // necessary.
-    for (int i = 0; i < num_elements; ++i) {
-      T temp = start[i];
-      SwapBytes(&temp);
-      v->push_back(temp);
-    }
-  }
-  return true;
-}
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_LITTLE_ENDIAN_DATA_H_
diff --git a/common/memory_image/data-store.cc b/common/memory_image/data-store.cc
deleted file mode 100644
index a5f500c..0000000
--- a/common/memory_image/data-store.cc
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/memory_image/data-store.h"
-
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace memory_image {
-
-DataStore::DataStore(StringPiece bytes)
-    : reader_(bytes.data(), bytes.size()) {
-  if (!reader_.success_status()) {
-    TC_LOG(ERROR) << "Unable to successfully initialize DataStore.";
-  }
-}
-
-StringPiece DataStore::GetData(const std::string &name) const {
-  if (!reader_.success_status()) {
-    TC_LOG(ERROR) << "DataStore::GetData(" << name << ") "
-                  << "called on invalid DataStore; will return empty data "
-                  << "chunk";
-    return StringPiece();
-  }
-
-  const auto &entries = reader_.trimmed_proto().entries();
-  const auto &it = entries.find(name);
-  if (it == entries.end()) {
-    TC_LOG(ERROR) << "Unknown key: " << name
-                  << "; will return empty data chunk";
-    return StringPiece();
-  }
-
-  const DataStoreEntryBytes &entry_bytes = it->second;
-  if (!entry_bytes.has_blob_index()) {
-    TC_LOG(ERROR) << "DataStoreEntryBytes with no blob_index; "
-                  << "will return empty data chunk.";
-    return StringPiece();
-  }
-
-  int blob_index = entry_bytes.blob_index();
-  return reader_.data_blob_view(blob_index);
-}
-
-}  // namespace memory_image
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/memory_image/data-store.h b/common/memory_image/data-store.h
deleted file mode 100644
index 56aa4fc..0000000
--- a/common/memory_image/data-store.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_DATA_STORE_H_
-#define LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_DATA_STORE_H_
-
-#include <string>
-
-#include "common/memory_image/data-store.pb.h"
-#include "common/memory_image/memory-image-reader.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace memory_image {
-
-// Class to access a data store.  See usage example in comments for
-// DataStoreBuilder.
-class DataStore {
- public:
-  // Constructs a DataStore using the indicated bytes, i.e., bytes.size() bytes
-  // starting at address bytes.data().  These bytes should contain the
-  // serialization of a data store, see DataStoreBuilder::SerializeAsString().
-  explicit DataStore(StringPiece bytes);
-
-  // Retrieves (start_addr, num_bytes) info for piece of memory that contains
-  // the data associated with the indicated name.  Note: this piece of memory is
-  // inside the [start, start + size) (see constructor).  This piece of memory
-  // starts at an offset from start which is a multiple of the alignment
-  // specified when the data store was built using DataStoreBuilder.
-  //
-  // If the alignment is a low power of 2 (e..g, 4, 8, or 16) and "start" passed
-  // to constructor corresponds to the beginning of a memory page or an address
-  // returned by new or malloc(), then start_addr is divisible with alignment.
-  StringPiece GetData(const std::string &name) const;
-
- private:
-  MemoryImageReader<DataStoreProto> reader_;
-};
-
-}  // namespace memory_image
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_DATA_STORE_H_
diff --git a/common/memory_image/data-store.proto b/common/memory_image/data-store.proto
deleted file mode 100644
index 68e914a..0000000
--- a/common/memory_image/data-store.proto
+++ /dev/null
@@ -1,44 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// Protos for a data store: a barebone in-memory file system.
-//
-// A DataStore maintains an association between names and chunks of bytes.  It
-// can be serialized into a string.  Of course, it can be deserialized from a
-// string, with minimal parsing; after deserialization, all chunks of bytes
-// start at aligned addresses (aligned = multiple of an address specified at
-// build time).
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-package libtextclassifier.nlp_core.memory_image;
-
-// Bytes for a data store entry.  They can be stored either directly in the
-// "data" field, or in the DataBlob with the 0-based index "blob_index".
-message DataStoreEntryBytes {
-  oneof data {
-    // Bytes for this data store entry, stored in this message.
-    string in_place_data = 1;
-
-    // 0-based index of the data blob with bytes for this data store entry.  In
-    // this case, the actual bytes are stored outside this message; the
-    // DataStore code handles the association.
-    int32 blob_index = 2 [default = -1];
-  }
-}
-
-message DataStoreProto {
-  map<string, DataStoreEntryBytes> entries = 1;
-}
diff --git a/common/memory_image/embedding-network-params-from-image.h b/common/memory_image/embedding-network-params-from-image.h
deleted file mode 100644
index e8c7d1e..0000000
--- a/common/memory_image/embedding-network-params-from-image.h
+++ /dev/null
@@ -1,225 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_EMBEDDING_NETWORK_PARAMS_FROM_IMAGE_H_
-#define LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_EMBEDDING_NETWORK_PARAMS_FROM_IMAGE_H_
-
-#include "common/embedding-network-package.pb.h"
-#include "common/embedding-network-params.h"
-#include "common/embedding-network.pb.h"
-#include "common/memory_image/memory-image-reader.h"
-#include "util/base/integral_types.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// EmbeddingNetworkParams backed by a memory image.
-//
-// In this context, a memory image is like an EmbeddingNetworkProto, but with
-// all repeated weights (>99% of the size) directly usable (with no parsing
-// required).
-class EmbeddingNetworkParamsFromImage : public EmbeddingNetworkParams {
- public:
-  // Constructs an EmbeddingNetworkParamsFromImage, using the memory image that
-  // starts at address start and contains num_bytes bytes.
-  EmbeddingNetworkParamsFromImage(const void *start, uint64 num_bytes)
-      : memory_reader_(start, num_bytes),
-        trimmed_proto_(memory_reader_.trimmed_proto()) {
-    embeddings_blob_offset_ = 0;
-
-    hidden_blob_offset_ = embeddings_blob_offset_ + embeddings_size();
-    if (trimmed_proto_.embeddings_size() &&
-        trimmed_proto_.embeddings(0).is_quantized()) {
-      // Adjust for quantization: each quantized matrix takes two blobs (instead
-      // of one): one for the quantized values and one for the scales.
-      hidden_blob_offset_ += embeddings_size();
-    }
-
-    hidden_bias_blob_offset_ = hidden_blob_offset_ + hidden_size();
-    softmax_blob_offset_ = hidden_bias_blob_offset_ + hidden_bias_size();
-    softmax_bias_blob_offset_ = softmax_blob_offset_ + softmax_size();
-  }
-
-  ~EmbeddingNetworkParamsFromImage() override {}
-
-  const TaskSpec *GetTaskSpec() override {
-    auto extension_id = task_spec_in_embedding_network_proto;
-    if (trimmed_proto_.HasExtension(extension_id)) {
-      return &(trimmed_proto_.GetExtension(extension_id));
-    } else {
-      return nullptr;
-    }
-  }
-
- protected:
-  int embeddings_size() const override {
-    return trimmed_proto_.embeddings_size();
-  }
-
-  int embeddings_num_rows(int i) const override {
-    TC_DCHECK(InRange(i, embeddings_size()));
-    return trimmed_proto_.embeddings(i).rows();
-  }
-
-  int embeddings_num_cols(int i) const override {
-    TC_DCHECK(InRange(i, embeddings_size()));
-    return trimmed_proto_.embeddings(i).cols();
-  }
-
-  const void *embeddings_weights(int i) const override {
-    TC_DCHECK(InRange(i, embeddings_size()));
-    const int blob_index = trimmed_proto_.embeddings(i).is_quantized()
-                               ? (embeddings_blob_offset_ + 2 * i)
-                               : (embeddings_blob_offset_ + i);
-    StringPiece data_blob_view = memory_reader_.data_blob_view(blob_index);
-    return data_blob_view.data();
-  }
-
-  QuantizationType embeddings_quant_type(int i) const override {
-    TC_DCHECK(InRange(i, embeddings_size()));
-    if (trimmed_proto_.embeddings(i).is_quantized()) {
-      return QuantizationType::UINT8;
-    } else {
-      return QuantizationType::NONE;
-    }
-  }
-
-  const float16 *embeddings_quant_scales(int i) const override {
-    TC_DCHECK(InRange(i, embeddings_size()));
-    if (trimmed_proto_.embeddings(i).is_quantized()) {
-      // Each embedding matrix has two atttached data blobs (hence the "2 * i"):
-      // one blob with the quantized values and (immediately after it, hence the
-      // "+ 1") one blob with the scales.
-      int blob_index = embeddings_blob_offset_ + 2 * i + 1;
-      StringPiece data_blob_view = memory_reader_.data_blob_view(blob_index);
-      return reinterpret_cast<const float16 *>(data_blob_view.data());
-    } else {
-      return nullptr;
-    }
-  }
-
-  int hidden_size() const override { return trimmed_proto_.hidden_size(); }
-
-  int hidden_num_rows(int i) const override {
-    TC_DCHECK(InRange(i, hidden_size()));
-    return trimmed_proto_.hidden(i).rows();
-  }
-
-  int hidden_num_cols(int i) const override {
-    TC_DCHECK(InRange(i, hidden_size()));
-    return trimmed_proto_.hidden(i).cols();
-  }
-
-  const void *hidden_weights(int i) const override {
-    TC_DCHECK(InRange(i, hidden_size()));
-    StringPiece data_blob_view =
-        memory_reader_.data_blob_view(hidden_blob_offset_ + i);
-    return data_blob_view.data();
-  }
-
-  int hidden_bias_size() const override {
-    return trimmed_proto_.hidden_bias_size();
-  }
-
-  int hidden_bias_num_rows(int i) const override {
-    TC_DCHECK(InRange(i, hidden_bias_size()));
-    return trimmed_proto_.hidden_bias(i).rows();
-  }
-
-  int hidden_bias_num_cols(int i) const override {
-    TC_DCHECK(InRange(i, hidden_bias_size()));
-    return trimmed_proto_.hidden_bias(i).cols();
-  }
-
-  const void *hidden_bias_weights(int i) const override {
-    TC_DCHECK(InRange(i, hidden_bias_size()));
-    StringPiece data_blob_view =
-        memory_reader_.data_blob_view(hidden_bias_blob_offset_ + i);
-    return data_blob_view.data();
-  }
-
-  int softmax_size() const override {
-    return trimmed_proto_.has_softmax() ? 1 : 0;
-  }
-
-  int softmax_num_rows(int i) const override {
-    TC_DCHECK(InRange(i, softmax_size()));
-    return trimmed_proto_.softmax().rows();
-  }
-
-  int softmax_num_cols(int i) const override {
-    TC_DCHECK(InRange(i, softmax_size()));
-    return trimmed_proto_.softmax().cols();
-  }
-
-  const void *softmax_weights(int i) const override {
-    TC_DCHECK(InRange(i, softmax_size()));
-    StringPiece data_blob_view =
-        memory_reader_.data_blob_view(softmax_blob_offset_ + i);
-    return data_blob_view.data();
-  }
-
-  int softmax_bias_size() const override {
-    return trimmed_proto_.has_softmax_bias() ? 1 : 0;
-  }
-
-  int softmax_bias_num_rows(int i) const override {
-    TC_DCHECK(InRange(i, softmax_bias_size()));
-    return trimmed_proto_.softmax_bias().rows();
-  }
-
-  int softmax_bias_num_cols(int i) const override {
-    TC_DCHECK(InRange(i, softmax_bias_size()));
-    return trimmed_proto_.softmax_bias().cols();
-  }
-
-  const void *softmax_bias_weights(int i) const override {
-    TC_DCHECK(InRange(i, softmax_bias_size()));
-    StringPiece data_blob_view =
-        memory_reader_.data_blob_view(softmax_bias_blob_offset_ + i);
-    return data_blob_view.data();
-  }
-
-  int embedding_num_features_size() const override {
-    return trimmed_proto_.embedding_num_features_size();
-  }
-
-  int embedding_num_features(int i) const override {
-    TC_DCHECK(InRange(i, embedding_num_features_size()));
-    return trimmed_proto_.embedding_num_features(i);
-  }
-
- private:
-  MemoryImageReader<EmbeddingNetworkProto> memory_reader_;
-
-  const EmbeddingNetworkProto &trimmed_proto_;
-
-  // 0-based offsets in the list of data blobs for the different MatrixParams
-  // fields.  E.g., the 1st hidden MatrixParams has its weights stored in the
-  // data blob number hidden_blob_offset_, the 2nd one in hidden_blob_offset_ +
-  // 1, and so on.
-  int embeddings_blob_offset_;
-  int hidden_blob_offset_;
-  int hidden_bias_blob_offset_;
-  int softmax_blob_offset_;
-  int softmax_bias_blob_offset_;
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_EMBEDDING_NETWORK_PARAMS_FROM_IMAGE_H_
diff --git a/common/memory_image/in-memory-model-data.cc b/common/memory_image/in-memory-model-data.cc
deleted file mode 100644
index acf3d86..0000000
--- a/common/memory_image/in-memory-model-data.cc
+++ /dev/null
@@ -1,45 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/memory_image/in-memory-model-data.h"
-
-#include "common/file-utils.h"
-#include "util/base/logging.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-const char InMemoryModelData::kTaskSpecDataStoreEntryName[] = "TASK-SPEC-#@";
-const char InMemoryModelData::kFilePatternPrefix[] = "in-mem-model::";
-
-bool InMemoryModelData::GetTaskSpec(TaskSpec *task_spec) const {
-  StringPiece blob = data_store_.GetData(kTaskSpecDataStoreEntryName);
-  if (blob.data() == nullptr) {
-    TC_LOG(ERROR) << "Can't find data blob for TaskSpec, i.e., entry "
-                  << kTaskSpecDataStoreEntryName;
-    return false;
-  }
-  bool parse_status = file_utils::ParseProtoFromMemory(blob, task_spec);
-  if (!parse_status) {
-    TC_LOG(ERROR) << "Error parsing TaskSpec";
-    return false;
-  }
-  return true;
-}
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/memory_image/in-memory-model-data.h b/common/memory_image/in-memory-model-data.h
deleted file mode 100644
index 91e4436..0000000
--- a/common/memory_image/in-memory-model-data.h
+++ /dev/null
@@ -1,76 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_IN_MEMORY_MODEL_DATA_H_
-#define LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_IN_MEMORY_MODEL_DATA_H_
-
-#include "common/memory_image/data-store.h"
-#include "common/task-spec.pb.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// In-memory representation of data for a Saft model.  Provides access to a
-// TaskSpec object (produced by the "spec" stage of the Saft training model) and
-// to the bytes of the TaskInputs mentioned in that spec (all these bytes are in
-// memory, no file I/O required).
-//
-// Technically, an InMemoryModelData is a DataStore that maps the special string
-// kTaskSpecDataStoreEntryName to the binary serialization of a TaskSpec.  For
-// each TaskInput (of the TaskSpec) with a file_pattern that starts with
-// kFilePatternPrefix (see below), the same DataStore maps file_pattern to some
-// content bytes.  This way, it is possible to have all TaskInputs in memory,
-// while still allowing classic, on-disk TaskInputs.
-class InMemoryModelData {
- public:
-  // Name for the DataStore entry that stores the serialized TaskSpec for the
-  // entire model.
-  static const char kTaskSpecDataStoreEntryName[];
-
-  // Returns prefix for TaskInput::Part::file_pattern, to distinguish those
-  // "files" from other files.
-  static const char kFilePatternPrefix[];
-
-  // Constructs an InMemoryModelData based on a chunk of bytes.  Those bytes
-  // should have been produced by a DataStoreBuilder.
-  explicit InMemoryModelData(StringPiece bytes) : data_store_(bytes) {}
-
-  // Fills *task_spec with a TaskSpec similar to the one used by
-  // DataStoreBuilder (when building the bytes used to construct this
-  // InMemoryModelData) except that each file name
-  // (TaskInput::Part::file_pattern) is replaced with a name that can be used to
-  // retrieve the corresponding file content bytes via GetBytesForInputFile().
-  //
-  // Returns true on success, false otherwise.
-  bool GetTaskSpec(TaskSpec *task_spec) const;
-
-  // Gets content bytes for a file.  The file_name argument should be the
-  // file_pattern for a TaskInput from the TaskSpec (see GetTaskSpec()).
-  // Returns a StringPiece indicating a memory area with the content bytes.  On
-  // error, returns StringPiece(nullptr, 0).
-  StringPiece GetBytesForInputFile(const std::string &file_name) const {
-    return data_store_.GetData(file_name);
-  }
-
- private:
-  const memory_image::DataStore data_store_;
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_IN_MEMORY_MODEL_DATA_H_
diff --git a/common/memory_image/low-level-memory-reader.h b/common/memory_image/low-level-memory-reader.h
deleted file mode 100644
index c87c772..0000000
--- a/common/memory_image/low-level-memory-reader.h
+++ /dev/null
@@ -1,142 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_LOW_LEVEL_MEMORY_READER_H_
-#define LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_LOW_LEVEL_MEMORY_READER_H_
-
-#include <string.h>
-
-#include <string>
-
-#include "util/base/endian.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-class LowLevelMemReader {
- public:
-  // Constructs a MemReader instance that reads at most num_available_bytes
-  // starting from address start.
-  LowLevelMemReader(const void *start, uint64 num_available_bytes)
-      : current_(reinterpret_cast<const char *>(start)),
-        // 0 bytes available if start == nullptr
-        num_available_bytes_(start ? num_available_bytes : 0),
-        num_loaded_bytes_(0) {
-  }
-
-  // Copies length bytes of data to address target.  Advances current position
-  // and returns true on success and false otherwise.
-  bool Read(void *target, uint64 length) {
-    if (length > num_available_bytes_) {
-      TC_LOG(WARNING) << "Not enough bytes: available " << num_available_bytes_
-                      << " < required " << length;
-      return false;
-    }
-    memcpy(target, current_, length);
-    Advance(length);
-    return true;
-  }
-
-  // Reads the string encoded at the current position.  The bytes starting at
-  // current position should contain (1) little-endian uint32 size (in bytes) of
-  // the actual string and next (2) the actual bytes of the string.  Advances
-  // the current position and returns true if successful, false otherwise.
-  //
-  // On success, sets *view to be a view of the relevant bytes: view.data()
-  // points to the beginning of the string bytes, and view.size() is the number
-  // of such bytes.
-  bool ReadString(StringPiece *view) {
-    uint32 size;
-    if (!Read(&size, sizeof(size))) {
-      TC_LOG(ERROR) << "Unable to read std::string size";
-      return false;
-    }
-    size = LittleEndian::ToHost32(size);
-    if (size > num_available_bytes_) {
-      TC_LOG(WARNING) << "Not enough bytes: " << num_available_bytes_
-                      << " available < " << size << " required ";
-      return false;
-    }
-    *view = StringPiece(current_, size);
-    Advance(size);
-    return true;
-  }
-
-  // Like ReadString(StringPiece *) but reads directly into a C++ string,
-  // instead of a StringPiece (StringPiece-like object).
-  bool ReadString(std::string *target) {
-    StringPiece view;
-    if (!ReadString(&view)) {
-      return false;
-    }
-    *target = view.ToString();
-    return true;
-  }
-
-  // Returns current position.
-  const char *GetCurrent() const { return current_; }
-
-  // Returns remaining number of available bytes.
-  uint64 GetNumAvailableBytes() const { return num_available_bytes_; }
-
-  // Returns number of bytes read ("loaded") so far.
-  uint64 GetNumLoadedBytes() const { return num_loaded_bytes_; }
-
-  // Advance the current read position by indicated number of bytes.  Returns
-  // true on success, false otherwise (e.g., if there are not enough available
-  // bytes to advance num_bytes).
-  bool Advance(uint64 num_bytes) {
-    if (num_bytes > num_available_bytes_) {
-      return false;
-    }
-
-    // Next line never results in an underflow of the unsigned
-    // num_available_bytes_, due to the previous if.
-    num_available_bytes_ -= num_bytes;
-    current_ += num_bytes;
-    num_loaded_bytes_ += num_bytes;
-    return true;
-  }
-
-  // Advance current position to nearest multiple of alignment.  Returns false
-  // if not enough bytes available to do that, true (success) otherwise.
-  bool SkipToAlign(int alignment) {
-    int num_extra_bytes = num_loaded_bytes_ % alignment;
-    if (num_extra_bytes == 0) {
-      return true;
-    }
-    return Advance(alignment - num_extra_bytes);
-  }
-
- private:
-  // Current position in the in-memory data.  Next call to Read() will read from
-  // this address.
-  const char *current_;
-
-  // Number of available bytes we can still read.
-  uint64 num_available_bytes_;
-
-  // Number of bytes read ("loaded") so far.
-  uint64 num_loaded_bytes_;
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_LOW_LEVEL_MEMORY_READER_H_
diff --git a/common/memory_image/memory-image-common.cc b/common/memory_image/memory-image-common.cc
deleted file mode 100644
index 6debf1d..0000000
--- a/common/memory_image/memory-image-common.cc
+++ /dev/null
@@ -1,31 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/memory_image/memory-image-common.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// IMPORTANT: this signature should never change.  If you change the protocol,
-// update kCurrentVersion, *not* this signature.
-const char MemoryImageConstants::kSignature[] = "Memory image $5%1#o3-1x32";
-
-const int MemoryImageConstants::kCurrentVersion = 1;
-
-const int MemoryImageConstants::kDefaultAlignment = 16;
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/memory_image/memory-image-common.h b/common/memory_image/memory-image-common.h
deleted file mode 100644
index 3a46f49..0000000
--- a/common/memory_image/memory-image-common.h
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Common utils for memory images.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_MEMORY_IMAGE_COMMON_H_
-#define LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_MEMORY_IMAGE_COMMON_H_
-
-#include <stddef.h>
-
-#include <string>
-
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-class MemoryImageConstants {
- public:
-  static const char kSignature[];
-  static const int kCurrentVersion;
-  static const int kDefaultAlignment;
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_MEMORY_IMAGE_COMMON_H_
diff --git a/common/memory_image/memory-image-reader.cc b/common/memory_image/memory-image-reader.cc
deleted file mode 100644
index 7e717d5..0000000
--- a/common/memory_image/memory-image-reader.cc
+++ /dev/null
@@ -1,125 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/memory_image/memory-image-reader.h"
-
-#include <string>
-
-#include "common/memory_image/low-level-memory-reader.h"
-#include "common/memory_image/memory-image-common.h"
-#include "common/memory_image/memory-image.pb.h"
-#include "util/base/endian.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace {
-
-// Checks that the memory area read by mem_reader starts with the expected
-// signature.  Advances mem_reader past the signature and returns success
-// status.
-bool ReadAndCheckSignature(LowLevelMemReader *mem_reader) {
-  const std::string expected_signature = MemoryImageConstants::kSignature;
-  const int signature_size = expected_signature.size();
-  if (mem_reader->GetNumAvailableBytes() < signature_size) {
-    TC_LOG(ERROR) << "Not enough bytes to check signature";
-    return false;
-  }
-  const std::string actual_signature(mem_reader->GetCurrent(), signature_size);
-  if (!mem_reader->Advance(signature_size)) {
-    TC_LOG(ERROR) << "Failed to advance past signature";
-    return false;
-  }
-  if (actual_signature != expected_signature) {
-    TC_LOG(ERROR) << "Different signature: actual \"" << actual_signature
-                  << "\" != expected \"" << expected_signature << "\"";
-    return false;
-  }
-  return true;
-}
-
-// Parses MemoryImageHeader from mem_reader.  Advances mem_reader past it.
-// Returns success status.
-bool ParseMemoryImageHeader(
-    LowLevelMemReader *mem_reader, MemoryImageHeader *header) {
-  std::string header_proto_str;
-  if (!mem_reader->ReadString(&header_proto_str)) {
-    TC_LOG(ERROR) << "Unable to read header_proto_str";
-    return false;
-  }
-  if (!header->ParseFromString(header_proto_str)) {
-    TC_LOG(ERROR) << "Unable to parse MemoryImageHeader";
-    return false;
-  }
-  return true;
-}
-
-}  // namespace
-
-bool GeneralMemoryImageReader::ReadMemoryImage() {
-  LowLevelMemReader mem_reader(start_, num_bytes_);
-
-  // Read and check signature.
-  if (!ReadAndCheckSignature(&mem_reader)) {
-    return false;
-  }
-
-  // Parse MemoryImageHeader header_.
-  if (!ParseMemoryImageHeader(&mem_reader, &header_)) {
-    return false;
-  }
-
-  // Check endianness.
-  if (header_.is_little_endian() != LittleEndian::IsLittleEndian()) {
-    // TODO(salcianu): implement conversion: it will take time, but it's better
-    // than crashing.  Not very urgent: [almost] all current Android phones are
-    // little-endian.
-    TC_LOG(ERROR) << "Memory image is "
-                  << (header_.is_little_endian() ? "little" : "big")
-                  << " endian. "
-                  << "Local system is different and we don't currently support "
-                  << "conversion between the two.";
-    return false;
-  }
-
-  // Read binary serialization of trimmed original proto.
-  if (!mem_reader.ReadString(&trimmed_proto_serialization_)) {
-    TC_LOG(ERROR) << "Unable to read trimmed proto binary serialization";
-    return false;
-  }
-
-  // Fill vector of pointers to beginning of each data blob.
-  for (int i = 0; i < header_.blob_info_size(); ++i) {
-    const MemoryImageDataBlobInfo &blob_info = header_.blob_info(i);
-    if (!mem_reader.SkipToAlign(header_.alignment())) {
-      TC_LOG(ERROR) << "Unable to align for blob #i" << i;
-      return false;
-    }
-    data_blob_views_.emplace_back(
-        mem_reader.GetCurrent(),
-        blob_info.num_bytes());
-    if (!mem_reader.Advance(blob_info.num_bytes())) {
-      TC_LOG(ERROR) << "Not enough bytes for blob #i" << i;
-      return false;
-    }
-  }
-
-  return true;
-}
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/memory_image/memory-image-reader.h b/common/memory_image/memory-image-reader.h
deleted file mode 100644
index c5954fd..0000000
--- a/common/memory_image/memory-image-reader.h
+++ /dev/null
@@ -1,154 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// MemoryImageReader, class for reading a memory image.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_MEMORY_IMAGE_READER_H_
-#define LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_MEMORY_IMAGE_READER_H_
-
-#include <string>
-#include <vector>
-
-#include "common/memory_image/memory-image.pb.h"
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-#include "util/strings/stringpiece.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// General, non-templatized class, to reduce code duplication.
-//
-// Given a memory area (pointer to start + size in bytes) parses a memory image
-// from there into (1) MemoryImageHeader proto (it includes the serialized form
-// of the trimmed down original proto) and (2) a list of void* pointers to the
-// beginning of all data blobs.
-//
-// In case of parsing errors, we prefer to log the error and set the
-// success_status() to false, instead of CHECK-failing .  This way, the client
-// has the option of performing error recovery or crashing.  Some mobile apps
-// don't like crashing (a restart is very slow) so, if possible, we try to avoid
-// that.
-class GeneralMemoryImageReader {
- public:
-  // Constructs this object.  See class-level comments.  Note: the memory area
-  // pointed to by start should not be deallocated while this object is used:
-  // this object does not copy it; instead, it keeps pointers inside that memory
-  // area.
-  GeneralMemoryImageReader(const void *start, uint64 num_bytes)
-      : start_(start), num_bytes_(num_bytes) {
-    success_ = ReadMemoryImage();
-  }
-
-  virtual ~GeneralMemoryImageReader() {}
-
-  // Returns true if reading the memory image has been successful.  If this
-  // returns false, then none of the other accessors should be used.
-  bool success_status() const { return success_; }
-
-  // Returns number of data blobs from the memory image.
-  int num_data_blobs() const {
-    return data_blob_views_.size();
-  }
-
-  // Returns pointer to the beginning of the data blob #i.
-  StringPiece data_blob_view(int i) const {
-    if ((i < 0) || (i >= num_data_blobs())) {
-      TC_LOG(ERROR) << "Blob index " << i << " outside range [0, "
-                    << num_data_blobs() << "); will return empty data chunk";
-      return StringPiece();
-    }
-    return data_blob_views_[i];
-  }
-
-  // Returns std::string with binary serialization of the original proto, but
-  // trimmed of the large fields (those were placed in the data blobs).
-  std::string trimmed_proto_str() const {
-    return trimmed_proto_serialization_.ToString();
-  }
-
-  // Same as above but returns the trimmed proto as a string piece pointing to
-  // the image.
-  StringPiece trimmed_proto_view() const {
-    return trimmed_proto_serialization_;
-  }
-
-  const MemoryImageHeader &header() { return header_; }
-
- protected:
-  void set_as_failed() {
-    success_ = false;
-  }
-
- private:
-  bool ReadMemoryImage();
-
-  // Pointer to beginning of memory image.  Not owned.
-  const void *const start_;
-
-  // Number of bytes in the memory image.  This class will not read more bytes.
-  const uint64 num_bytes_;
-
-  // MemoryImageHeader parsed from the memory image.
-  MemoryImageHeader header_;
-
-  // Binary serialization of the trimmed version of the original proto.
-  // Represented as a StringPiece backed up by the underlying memory image
-  // bytes.
-  StringPiece trimmed_proto_serialization_;
-
-  // List of StringPiece objects for all data blobs from the memory image (in
-  // order).
-  std::vector<StringPiece> data_blob_views_;
-
-  // Memory reading success status.
-  bool success_;
-
-  TC_DISALLOW_COPY_AND_ASSIGN(GeneralMemoryImageReader);
-};
-
-// Like GeneralMemoryImageReader, but has knowledge about the type of the
-// original proto.  As such, it can parse it (well, the trimmed version) and
-// offer access to it.
-//
-// Template parameter T should be the type of the original proto.
-template<class T>
-class MemoryImageReader : public GeneralMemoryImageReader {
- public:
-  MemoryImageReader(const void *start, uint64 num_bytes)
-      : GeneralMemoryImageReader(start, num_bytes) {
-    if (!trimmed_proto_.ParseFromString(trimmed_proto_str())) {
-      TC_LOG(INFO) << "Unable to parse the trimmed proto";
-      set_as_failed();
-    }
-  }
-
-  // Returns const reference to the trimmed version of the original proto.
-  // Useful for retrieving the many small fields that are not converted into
-  // data blobs.
-  const T &trimmed_proto() const { return trimmed_proto_; }
-
- private:
-  T trimmed_proto_;
-
-  TC_DISALLOW_COPY_AND_ASSIGN(MemoryImageReader);
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_MEMORY_IMAGE_MEMORY_IMAGE_READER_H_
diff --git a/common/memory_image/memory-image.proto b/common/memory_image/memory-image.proto
deleted file mode 100644
index f6b624c..0000000
--- a/common/memory_image/memory-image.proto
+++ /dev/null
@@ -1,63 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// Protos for "memory images".
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-package libtextclassifier.nlp_core;
-
-message MemoryImageDataBlobInfo {
-  // Size (in bytes) of this data blob.
-  optional uint64 num_bytes = 1;
-
-  // Indicates whether this data blob corresponds to an array.
-  optional bool is_array = 2 [default = true];
-
-  // Size (in bytes) of each array element.  Useful for little <-> big endian
-  // conversions.  -1 means unknown: no endianness conversion in that case.
-  optional int32 element_size = 3 [default = -1];
-}
-
-message MemoryImageHeader {
-  // Version of the algorithm used to produce the memory image.  We should
-  // increase the value used here every time we perform an incompatible change.
-  // Algorithm version v should handle only memory images of the same version,
-  // and crash otherwise.
-  optional int32 version = 1 [default = -1];
-
-  // True if the info stored in the data blobs uses the little endian
-  // convention.  Almost all machines today are little-endian but we want to be
-  // able to crash with an informative message or perform a (costly) conversion
-  // in the rare cases when that's not true.
-  optional bool is_little_endian = 2 [default = true];
-
-  // Alignment (in bytes) for all data blobs.  E.g., if this field is 16, then
-  // each data blob starts at an offset that's a multiple of 16, where the
-  // offset is measured from the beginning of the memory image.  On the client
-  // side, allocating the entire memory image at an aligned address (by same
-  // alignment) makes sure all data blobs are properly aligned.
-  //
-  // NOTE: I (salcianu) explored the idea of a different alignment for each data
-  // blob: e.g., float[] should be fine with 4-byte alignment (alignment = 4)
-  // but char[] are fine with no alignment (alignment = 1).  As we expect only a
-  // few (but large) data blobs, the space benefit is not worth the extra code
-  // complexity.
-  optional int32 alignment = 3 [default = 8];
-
-  // One MemoryImageDataBlobInfo for each data blob, in order.  There is one
-  // data blob for each large field we handle specially.
-  repeated MemoryImageDataBlobInfo blob_info = 4;
-}
diff --git a/common/mock_functions.cc b/common/mock_functions.cc
deleted file mode 100644
index c661b70..0000000
--- a/common/mock_functions.cc
+++ /dev/null
@@ -1,29 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/mock_functions.h"
-
-#include "common/registry.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-TC_DEFINE_CLASS_REGISTRY_NAME("function", functions::Function);
-
-TC_DEFINE_CLASS_REGISTRY_NAME("int-function", functions::IntFunction);
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/mock_functions.h b/common/mock_functions.h
deleted file mode 100644
index b5bcb07..0000000
--- a/common/mock_functions.h
+++ /dev/null
@@ -1,75 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_MOCK_FUNCTIONS_H_
-#define LIBTEXTCLASSIFIER_COMMON_MOCK_FUNCTIONS_H_
-
-#include <math.h>
-
-#include "common/registry.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace functions {
-
-// Abstract double -> double function.
-class Function : public RegisterableClass<Function> {
- public:
-  virtual ~Function() {}
-  virtual double Evaluate(double x) = 0;
-};
-
-class Cos : public Function {
- public:
-  double Evaluate(double x) override { return cos(x); }
-  TC_DEFINE_REGISTRATION_METHOD("cos", Cos);
-};
-
-class Exp : public Function {
- public:
-  double Evaluate(double x) override { return exp(x); }
-  TC_DEFINE_REGISTRATION_METHOD("exp", Exp);
-};
-
-// Abstract int -> int function.
-class IntFunction : public RegisterableClass<IntFunction> {
- public:
-  virtual ~IntFunction() {}
-  virtual int Evaluate(int k) = 0;
-};
-
-class Inc : public IntFunction {
- public:
-  int Evaluate(int k) override { return k + 1; }
-  TC_DEFINE_REGISTRATION_METHOD("inc", Inc);
-};
-
-class Dec : public IntFunction {
- public:
-  int Evaluate(int k) override { return k + 1; }
-  TC_DEFINE_REGISTRATION_METHOD("dec", Dec);
-};
-
-}  // namespace functions
-
-// Should be inside namespace libtextclassifier::nlp_core.
-TC_DECLARE_CLASS_REGISTRY_NAME(functions::Function);
-TC_DECLARE_CLASS_REGISTRY_NAME(functions::IntFunction);
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_MOCK_FUNCTIONS_H_
diff --git a/common/registry.h b/common/registry.h
deleted file mode 100644
index d958225..0000000
--- a/common/registry.h
+++ /dev/null
@@ -1,281 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Mechanism to instantiate classes by name.
-//
-// This mechanism is useful if the concrete classes to be instantiated are not
-// statically known (e.g., if their names are read from a dynamically-provided
-// config).
-//
-// In that case, the first step is to define the API implemented by the
-// instantiated classes.  E.g.,
-//
-//  // In a header file function.h:
-//
-//  // Abstract function that takes a double and returns a double.
-//  class Function : public RegisterableClass<Function> {
-//   public:
-//    virtual ~Function() {}
-//    virtual double Evaluate(double x) = 0;
-//  };
-//
-//  // Should be inside namespace libtextclassifier::nlp_core.
-//  TC_DECLARE_CLASS_REGISTRY_NAME(Function);
-//
-// Notice the inheritance from RegisterableClass<Function>.  RegisterableClass
-// is defined by this file (registry.h).  Under the hood, this inheritanace
-// defines a "registry" that maps names (zero-terminated arrays of chars) to
-// factory methods that create Functions.  You should give a human-readable name
-// to this registry.  To do that, use the following macro in a .cc file (it has
-// to be a .cc file, as it defines some static data):
-//
-//  // Inside function.cc
-//  // Should be inside namespace libtextclassifier::nlp_core.
-//  TC_DEFINE_CLASS_REGISTRY_NAME("function", Function);
-//
-// Now, let's define a few concrete Functions: e.g.,
-//
-//   class Cos : public Function {
-//    public:
-//     double Evaluate(double x) override { return cos(x); }
-//     TC_DEFINE_REGISTRATION_METHOD("cos", Cos);
-//   };
-//
-//   class Exp : public Function {
-//    public:
-//     double Evaluate(double x) override { return exp(x); }
-//     TC_DEFINE_REGISTRATION_METHOD("sin", Sin);
-//   };
-//
-// Each concrete Function implementation should have (in the public section) the
-// macro
-//
-//   TC_DEFINE_REGISTRATION_METHOD("name", implementation_class);
-//
-// This defines a RegisterClass static method that, when invoked, associates
-// "name" with a factory method that creates instances of implementation_class.
-//
-// Before instantiating Functions by name, we need to tell our system which
-// Functions we may be interested in.  This is done by calling the
-// Foo::RegisterClass() for each relevant Foo implementation of Function.  It is
-// ok to call Foo::RegisterClass() multiple times (even in parallel): only the
-// first call will perform something, the others will return immediately.
-//
-//   Cos::RegisterClass();
-//   Exp::RegisterClass();
-//
-// Now, let's instantiate a Function based on its name.  This get a lot more
-// interesting if the Function name is not statically known (i.e.,
-// read from an input proto:
-//
-//   std::unique_ptr<Function> f(Function::Create("cos"));
-//   double result = f->Evaluate(arg);
-//
-// NOTE: the same binary can use this mechanism for different APIs.  E.g., one
-// can also have (in the binary with Function, Sin, Cos, etc):
-//
-// class IntFunction : public RegisterableClass<IntFunction> {
-//  public:
-//   virtual ~IntFunction() {}
-//   virtual int Evaluate(int k) = 0;
-// };
-//
-// TC_DECLARE_CLASS_REGISTRY_NAME(IntFunction);
-//
-// TC_DEFINE_CLASS_REGISTRY_NAME("int function", IntFunction);
-//
-// class Inc : public IntFunction {
-//  public:
-//   int Evaluate(int k) override { return k + 1; }
-//   TC_DEFINE_REGISTRATION_METHOD("inc", Inc);
-// };
-//
-// RegisterableClass<Function> and RegisterableClass<IntFunction> define their
-// own registries: each maps string names to implementation of the corresponding
-// API.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_REGISTRY_H_
-#define LIBTEXTCLASSIFIER_COMMON_REGISTRY_H_
-
-#include <stdlib.h>
-#include <string.h>
-
-#include <string>
-
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace internal {
-// Registry that associates keys (zero-terminated array of chars) with values.
-// Values are pointers to type T (the template parameter).  This is used to
-// store the association between component names and factory methods that
-// produce those components; the error messages are focused on that case.
-//
-// Internally, this registry uses a linked list of (key, value) pairs.  We do
-// not use an STL map, list, etc because we aim for small code size.
-template <class T>
-class ComponentRegistry {
- public:
-  explicit ComponentRegistry(const char *name) : name_(name), head_(nullptr) {}
-
-  // Adds a the (key, value) pair to this registry (if the key does not already
-  // exists in this registry) and returns true.  If the registry already has a
-  // mapping for key, returns false and does not modify the registry.  NOTE: the
-  // error (false) case happens even if the existing value for key is equal with
-  // the new one.
-  //
-  // This method does not take ownership of key, nor of value.
-  bool Add(const char *key, T *value) {
-    const Cell *old_cell = FindCell(key);
-    if (old_cell != nullptr) {
-      TC_LOG(ERROR) << "Duplicate component: " << key;
-      return false;
-    }
-    Cell *new_cell = new Cell(key, value, head_);
-    head_ = new_cell;
-    return true;
-  }
-
-  // Returns the value attached to a key in this registry.  Returns nullptr on
-  // error (e.g., unknown key).
-  T *Lookup(const char *key) const {
-    const Cell *cell = FindCell(key);
-    if (cell == nullptr) {
-      TC_LOG(ERROR) << "Unknown " << name() << " component: " << key;
-    }
-    return (cell == nullptr) ? nullptr : cell->value();
-  }
-
-  T *Lookup(const std::string &key) const { return Lookup(key.c_str()); }
-
-  // Returns name of this ComponentRegistry.
-  const char *name() const { return name_; }
-
- private:
-  // Cell for the singly-linked list underlying this ComponentRegistry.  Each
-  // cell contains a key, the value for that key, as well as a pointer to the
-  // next Cell from the list.
-  class Cell {
-   public:
-    // Constructs a new Cell.
-    Cell(const char *key, T *value, Cell *next)
-        : key_(key), value_(value), next_(next) {}
-
-    const char *key() const { return key_; }
-    T *value() const { return value_; }
-    Cell *next() const { return next_; }
-
-   private:
-    const char *const key_;
-    T *const value_;
-    Cell *const next_;
-  };
-
-  // Finds Cell for indicated key in the singly-linked list pointed to by head_.
-  // Returns pointer to that first Cell with that key, or nullptr if no such
-  // Cell (i.e., unknown key).
-  //
-  // Caller does NOT own the returned pointer.
-  const Cell *FindCell(const char *key) const {
-    Cell *c = head_;
-    while (c != nullptr && strcmp(key, c->key()) != 0) {
-      c = c->next();
-    }
-    return c;
-  }
-
-  // Human-readable description for this ComponentRegistry.  For debug purposes.
-  const char *const name_;
-
-  // Pointer to the first Cell from the underlying list of (key, value) pairs.
-  Cell *head_;
-};
-}  // namespace internal
-
-// Base class for registerable classes.
-template <class T>
-class RegisterableClass {
- public:
-  // Factory function type.
-  typedef T *(Factory)();
-
-  // Registry type.
-  typedef internal::ComponentRegistry<Factory> Registry;
-
-  // Creates a new instance of T.  Returns pointer to new instance or nullptr in
-  // case of errors (e.g., unknown component).
-  //
-  // Passes ownership of the returned pointer to the caller.
-  static T *Create(const std::string &name) {  // NOLINT
-    auto *factory = registry()->Lookup(name);
-    if (factory == nullptr) {
-      TC_LOG(ERROR) << "Unknown RegisterableClass " << name;
-      return nullptr;
-    }
-    return factory();
-  }
-
-  // Returns registry for class.
-  static Registry *registry() {
-    static Registry *registry_for_type_t = new Registry(kRegistryName);
-    return registry_for_type_t;
-  }
-
- protected:
-  // Factory method for subclass ComponentClass.  Used internally by the static
-  // method RegisterClass() defined by TC_DEFINE_REGISTRATION_METHOD.
-  template <class ComponentClass>
-  static T *_internal_component_factory() {
-    return new ComponentClass();
-  }
-
- private:
-  // Human-readable name for the registry for this class.
-  static const char kRegistryName[];
-};
-
-// Defines the static method component_class::RegisterClass() that should be
-// called before trying to instantiate component_class by name.  Should be used
-// inside the public section of the declaration of component_class.  See
-// comments at the top-level of this file.
-#define TC_DEFINE_REGISTRATION_METHOD(component_name, component_class)  \
-  static void RegisterClass() {                                         \
-    static bool once = registry()->Add(                                 \
-        component_name, &_internal_component_factory<component_class>); \
-    if (!once) {                                                        \
-      TC_LOG(ERROR) << "Problem registering " << component_name;        \
-    }                                                                   \
-    TC_DCHECK(once);                                                    \
-  }
-
-// Defines the human-readable name of the registry associated with base_class.
-#define TC_DECLARE_CLASS_REGISTRY_NAME(base_class)             \
-  template <>                                                  \
-  const char ::libtextclassifier::nlp_core::RegisterableClass< \
-      base_class>::kRegistryName[]
-
-// Defines the human-readable name of the registry associated with base_class.
-#define TC_DEFINE_CLASS_REGISTRY_NAME(registry_name, base_class) \
-  template <>                                                    \
-  const char ::libtextclassifier::nlp_core::RegisterableClass<   \
-      base_class>::kRegistryName[] = registry_name
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_REGISTRY_H_
diff --git a/common/registry_test.cc b/common/registry_test.cc
deleted file mode 100644
index d5d7006..0000000
--- a/common/registry_test.cc
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <memory>
-
-#include "common/mock_functions.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace functions {
-
-TEST(RegistryTest, InstantiateFunctionsByName) {
-  // First, we need to register the functions we are interested in:
-  Exp::RegisterClass();
-  Inc::RegisterClass();
-  Cos::RegisterClass();
-
-  // RegisterClass methods can be called in any order, even multiple times :)
-  Cos::RegisterClass();
-  Inc::RegisterClass();
-  Inc::RegisterClass();
-  Cos::RegisterClass();
-  Inc::RegisterClass();
-
-  // NOTE: we intentionally do not register Dec.  Attempts to create an instance
-  // of that function by name should fail.
-
-  // Instantiate a few functions and check that the created functions produce
-  // the expected results for a few sample values.
-  std::unique_ptr<Function> f1(Function::Create("cos"));
-  ASSERT_NE(f1, nullptr);
-  std::unique_ptr<Function> f2(Function::Create("exp"));
-  ASSERT_NE(f2, nullptr);
-  EXPECT_NEAR(f1->Evaluate(-3), -0.9899, 0.0001);
-  EXPECT_NEAR(f2->Evaluate(2.3), 9.9741, 0.0001);
-
-  std::unique_ptr<IntFunction> f3(IntFunction::Create("inc"));
-  ASSERT_NE(f3, nullptr);
-  EXPECT_EQ(f3->Evaluate(7), 8);
-
-  // Instantiating unknown functions should return nullptr, but not crash
-  // anything.
-  EXPECT_EQ(Function::Create("mambo"), nullptr);
-
-  // Functions that are defined in the code, but are not registered are unknown.
-  EXPECT_EQ(IntFunction::Create("dec"), nullptr);
-
-  // Function and IntFunction use different registries.
-  EXPECT_EQ(IntFunction::Create("exp"), nullptr);
-}
-
-}  // namespace functions
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/simple-adder.h b/common/simple-adder.h
deleted file mode 100644
index c16cc8a..0000000
--- a/common/simple-adder.h
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_SIMPLE_ADDER_H_
-#define LIBTEXTCLASSIFIER_COMMON_SIMPLE_ADDER_H_
-
-#include "util/base/integral_types.h"
-#include "util/base/port.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// Implements add and scaleadd in the most straight-forward way, and it doesn't
-// have any additional requirement on the alignment and array size.
-class SimpleAdder {
- public:
-  TC_ATTRIBUTE_ALWAYS_INLINE SimpleAdder(float *dest, int num_floats)
-      : dest_(dest), num_floats_(num_floats) {}
-
-  TC_ATTRIBUTE_ALWAYS_INLINE void LazyAdd(const float *source) const {
-    AddImpl(source, num_floats_, dest_);
-  }
-
-  TC_ATTRIBUTE_ALWAYS_INLINE void LazyScaleAdd(const float *source,
-                                               const float scale) const {
-    ScaleAddImpl(source, num_floats_, scale, dest_);
-  }
-
-  // Simple fast while loop to implement dest += source.
-  TC_ATTRIBUTE_ALWAYS_INLINE static void AddImpl(const float *__restrict source,
-                                                 uint32 size,
-                                                 float *__restrict dest) {
-    for (uint32 i = 0; i < size; ++i) {
-      dest[i] += source[i];
-    }
-  }
-
-  // Simple fast while loop to implement dest += scale * source.
-  TC_ATTRIBUTE_ALWAYS_INLINE static void ScaleAddImpl(
-      const float *__restrict source, uint32 size, const float scale,
-      float *__restrict dest) {
-    for (uint32 i = 0; i < size; ++i) {
-      dest[i] += source[i] * scale;
-    }
-  }
-
- private:
-  float *dest_;
-  int num_floats_;
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_SIMPLE_ADDER_H_
diff --git a/common/task-context.cc b/common/task-context.cc
deleted file mode 100644
index e4c1090..0000000
--- a/common/task-context.cc
+++ /dev/null
@@ -1,206 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/task-context.h"
-
-#include <stdlib.h>
-
-#include <string>
-
-#include "util/base/integral_types.h"
-#include "util/base/logging.h"
-#include "util/strings/numbers.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-namespace {
-int32 ParseInt32WithDefault(const std::string &s, int32 defval) {
-  int32 value = defval;
-  return ParseInt32(s.c_str(), &value) ? value : defval;
-}
-
-int64 ParseInt64WithDefault(const std::string &s, int64 defval) {
-  int64 value = defval;
-  return ParseInt64(s.c_str(), &value) ? value : defval;
-}
-
-double ParseDoubleWithDefault(const std::string &s, double defval) {
-  double value = defval;
-  return ParseDouble(s.c_str(), &value) ? value : defval;
-}
-}  // namespace
-
-TaskInput *TaskContext::GetInput(const std::string &name) {
-  // Return existing input if it exists.
-  for (int i = 0; i < spec_.input_size(); ++i) {
-    if (spec_.input(i).name() == name) return spec_.mutable_input(i);
-  }
-
-  // Create new input.
-  TaskInput *input = spec_.add_input();
-  input->set_name(name);
-  return input;
-}
-
-TaskInput *TaskContext::GetInput(const std::string &name,
-                                 const std::string &file_format,
-                                 const std::string &record_format) {
-  TaskInput *input = GetInput(name);
-  if (!file_format.empty()) {
-    bool found = false;
-    for (int i = 0; i < input->file_format_size(); ++i) {
-      if (input->file_format(i) == file_format) found = true;
-    }
-    if (!found) input->add_file_format(file_format);
-  }
-  if (!record_format.empty()) {
-    bool found = false;
-    for (int i = 0; i < input->record_format_size(); ++i) {
-      if (input->record_format(i) == record_format) found = true;
-    }
-    if (!found) input->add_record_format(record_format);
-  }
-  return input;
-}
-
-void TaskContext::SetParameter(const std::string &name,
-                               const std::string &value) {
-  TC_LOG(INFO) << "SetParameter(" << name << ", " << value << ")";
-
-  // If the parameter already exists update the value.
-  for (int i = 0; i < spec_.parameter_size(); ++i) {
-    if (spec_.parameter(i).name() == name) {
-      spec_.mutable_parameter(i)->set_value(value);
-      return;
-    }
-  }
-
-  // Add new parameter.
-  TaskSpec::Parameter *param = spec_.add_parameter();
-  param->set_name(name);
-  param->set_value(value);
-}
-
-std::string TaskContext::GetParameter(const std::string &name) const {
-  // First try to find parameter in task specification.
-  for (int i = 0; i < spec_.parameter_size(); ++i) {
-    if (spec_.parameter(i).name() == name) return spec_.parameter(i).value();
-  }
-
-  // Parameter not found, return empty std::string.
-  return "";
-}
-
-int TaskContext::GetIntParameter(const std::string &name) const {
-  std::string value = GetParameter(name);
-  return ParseInt32WithDefault(value, 0);
-}
-
-int64 TaskContext::GetInt64Parameter(const std::string &name) const {
-  std::string value = GetParameter(name);
-  return ParseInt64WithDefault(value, 0);
-}
-
-bool TaskContext::GetBoolParameter(const std::string &name) const {
-  std::string value = GetParameter(name);
-  return value == "true";
-}
-
-double TaskContext::GetFloatParameter(const std::string &name) const {
-  std::string value = GetParameter(name);
-  return ParseDoubleWithDefault(value, 0.0);
-}
-
-std::string TaskContext::Get(const std::string &name,
-                             const char *defval) const {
-  // First try to find parameter in task specification.
-  for (int i = 0; i < spec_.parameter_size(); ++i) {
-    if (spec_.parameter(i).name() == name) return spec_.parameter(i).value();
-  }
-
-  // Parameter not found, return default value.
-  return defval;
-}
-
-std::string TaskContext::Get(const std::string &name,
-                             const std::string &defval) const {
-  return Get(name, defval.c_str());
-}
-
-int TaskContext::Get(const std::string &name, int defval) const {
-  std::string value = Get(name, "");
-  return ParseInt32WithDefault(value, defval);
-}
-
-int64 TaskContext::Get(const std::string &name, int64 defval) const {
-  std::string value = Get(name, "");
-  return ParseInt64WithDefault(value, defval);
-}
-
-double TaskContext::Get(const std::string &name, double defval) const {
-  std::string value = Get(name, "");
-  return ParseDoubleWithDefault(value, defval);
-}
-
-bool TaskContext::Get(const std::string &name, bool defval) const {
-  std::string value = Get(name, "");
-  return value.empty() ? defval : value == "true";
-}
-
-std::string TaskContext::InputFile(const TaskInput &input) {
-  if (input.part_size() == 0) {
-    TC_LOG(ERROR) << "No file for TaskInput " << input.name();
-    return "";
-  }
-  if (input.part_size() > 1) {
-    TC_LOG(ERROR) << "Ambiguous: multiple files for TaskInput " << input.name();
-  }
-  return input.part(0).file_pattern();
-}
-
-bool TaskContext::Supports(const TaskInput &input,
-                           const std::string &file_format,
-                           const std::string &record_format) {
-  // Check file format.
-  if (input.file_format_size() > 0) {
-    bool found = false;
-    for (int i = 0; i < input.file_format_size(); ++i) {
-      if (input.file_format(i) == file_format) {
-        found = true;
-        break;
-      }
-    }
-    if (!found) return false;
-  }
-
-  // Check record format.
-  if (input.record_format_size() > 0) {
-    bool found = false;
-    for (int i = 0; i < input.record_format_size(); ++i) {
-      if (input.record_format(i) == record_format) {
-        found = true;
-        break;
-      }
-    }
-    if (!found) return false;
-  }
-
-  return true;
-}
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/task-context.h b/common/task-context.h
deleted file mode 100644
index c55ed67..0000000
--- a/common/task-context.h
+++ /dev/null
@@ -1,87 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_TASK_CONTEXT_H_
-#define LIBTEXTCLASSIFIER_COMMON_TASK_CONTEXT_H_
-
-#include <string>
-#include <vector>
-
-#include "common/task-spec.pb.h"
-#include "util/base/integral_types.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// A task context holds configuration information for a task. It is basically a
-// wrapper around a TaskSpec protocol buffer.
-class TaskContext {
- public:
-  // Returns the underlying task specification protocol buffer for the context.
-  const TaskSpec &spec() const { return spec_; }
-  TaskSpec *mutable_spec() { return &spec_; }
-
-  // Returns a named input descriptor for the task. A new input  is created if
-  // the task context does not already have an input with that name.
-  TaskInput *GetInput(const std::string &name);
-  TaskInput *GetInput(const std::string &name,
-                      const std::string &file_format,
-                      const std::string &record_format);
-
-  // Sets task parameter.
-  void SetParameter(const std::string &name, const std::string &value);
-
-  // Returns task parameter. If the parameter is not in the task configuration
-  // the (default) value of the corresponding command line flag is returned.
-  std::string GetParameter(const std::string &name) const;
-  int GetIntParameter(const std::string &name) const;
-  int64 GetInt64Parameter(const std::string &name) const;
-  bool GetBoolParameter(const std::string &name) const;
-  double GetFloatParameter(const std::string &name) const;
-
-  // Returns task parameter. If the parameter is not in the task configuration
-  // the default value is returned.
-  std::string Get(const std::string &name, const std::string &defval) const;
-  std::string Get(const std::string &name, const char *defval) const;
-  int Get(const std::string &name, int defval) const;
-  int64 Get(const std::string &name, int64 defval) const;
-  double Get(const std::string &name, double defval) const;
-  bool Get(const std::string &name, bool defval) const;
-
-  // Returns input file name for a single-file task input.
-  //
-  // Special cases: returns the empty string if the TaskInput does not have any
-  // input files.  Returns the first file if the TaskInput has multiple input
-  // files.
-  static std::string InputFile(const TaskInput &input);
-
-  // Returns true if task input supports the file and record format.
-  static bool Supports(const TaskInput &input, const std::string &file_format,
-                       const std::string &record_format);
-
- private:
-  // Underlying task specification protocol buffer.
-  TaskSpec spec_;
-
-  // Vector of parameters required by this task.  These must be specified in the
-  // task rather than relying on default values.
-  std::vector<std::string> required_parameters_;
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_TASK_CONTEXT_H_
diff --git a/common/task-spec.proto b/common/task-spec.proto
deleted file mode 100644
index ab986ce..0000000
--- a/common/task-spec.proto
+++ /dev/null
@@ -1,61 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// LINT: ALLOW_GROUPS
-// Protocol buffer specifications for task configuration.
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-package libtextclassifier.nlp_core;
-
-// Task input descriptor.
-message TaskInput {
-  // Name of input resource.
-  required string name = 1;
-
-  // File format for resource.
-  repeated string file_format = 3;
-
-  // Record format for resource.
-  repeated string record_format = 4;
-
-  // An input can consist of multiple file sets.
-  repeated group Part = 6 {
-    // File pattern for file set.
-    optional string file_pattern = 7;
-
-    // File format for file set.
-    optional string file_format = 8;
-
-    // Record format for file set.
-    optional string record_format = 9;
-  }
-
-  reserved 2, 5;
-}
-
-// A task specification is used for describing executing parameters.
-message TaskSpec {
-  // Task parameters.
-  repeated group Parameter = 3 {
-    required string name = 4;
-    optional string value = 5;
-  }
-
-  // Task inputs.
-  repeated TaskInput input = 6;
-
-  reserved 1, 2, 7;
-}
diff --git a/common/vector-span.h b/common/vector-span.h
deleted file mode 100644
index d7fbfe9..0000000
--- a/common/vector-span.h
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_VECTOR_SPAN_H_
-#define LIBTEXTCLASSIFIER_COMMON_VECTOR_SPAN_H_
-
-#include <vector>
-
-namespace libtextclassifier {
-
-// StringPiece analogue for std::vector<T>.
-template <class T>
-class VectorSpan {
- public:
-  VectorSpan() : begin_(), end_() {}
-  VectorSpan(const std::vector<T>& v)  // NOLINT(runtime/explicit)
-      : begin_(v.begin()), end_(v.end()) {}
-  VectorSpan(typename std::vector<T>::const_iterator begin,
-             typename std::vector<T>::const_iterator end)
-      : begin_(begin), end_(end) {}
-
-  const T& operator[](typename std::vector<T>::size_type i) const {
-    return *(begin_ + i);
-  }
-
-  int size() const { return end_ - begin_; }
-  typename std::vector<T>::const_iterator begin() const { return begin_; }
-  typename std::vector<T>::const_iterator end() const { return end_; }
-
- private:
-  typename std::vector<T>::const_iterator begin_;
-  typename std::vector<T>::const_iterator end_;
-};
-
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_VECTOR_SPAN_H_
diff --git a/common/workspace.cc b/common/workspace.cc
deleted file mode 100644
index 770e4be..0000000
--- a/common/workspace.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "common/workspace.h"
-
-#include <atomic>
-#include <string>
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// static
-int GetFreshTypeId() {
-  // Static local below is initialized the first time this method is run.
-  static std::atomic<int> counter(0);
-  return counter++;
-}
-
-std::string WorkspaceRegistry::DebugString() const {
-  std::string str;
-  for (auto &it : workspace_names_) {
-    const std::string &type_name = workspace_types_.at(it.first);
-    for (size_t index = 0; index < it.second.size(); ++index) {
-      const std::string &workspace_name = it.second[index];
-      str.append("\n  ");
-      str.append(type_name);
-      str.append(" :: ");
-      str.append(workspace_name);
-    }
-  }
-  return str;
-}
-
-VectorIntWorkspace::VectorIntWorkspace(int size) : elements_(size) {}
-
-VectorIntWorkspace::VectorIntWorkspace(int size, int value)
-    : elements_(size, value) {}
-
-VectorIntWorkspace::VectorIntWorkspace(const std::vector<int> &elements)
-    : elements_(elements) {}
-
-std::string VectorIntWorkspace::TypeName() { return "Vector"; }
-
-VectorVectorIntWorkspace::VectorVectorIntWorkspace(int size)
-    : elements_(size) {}
-
-std::string VectorVectorIntWorkspace::TypeName() { return "VectorVector"; }
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/common/workspace.h b/common/workspace.h
deleted file mode 100644
index e003bde..0000000
--- a/common/workspace.h
+++ /dev/null
@@ -1,245 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Notes on thread-safety: All of the classes here are thread-compatible.  More
-// specifically, the registry machinery is thread-safe, as long as each thread
-// performs feature extraction on a different Sentence object.
-
-#ifndef LIBTEXTCLASSIFIER_COMMON_WORKSPACE_H_
-#define LIBTEXTCLASSIFIER_COMMON_WORKSPACE_H_
-
-#include <stddef.h>
-#include <string>
-#include <unordered_map>
-#include <utility>
-#include <vector>
-
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// A base class for shared workspaces. Derived classes implement a static member
-// function TypeName() which returns a human readable std::string name for the
-// class.
-class Workspace {
- public:
-  // Polymorphic destructor.
-  virtual ~Workspace() {}
-
- protected:
-  // Create an empty workspace.
-  Workspace() {}
-
- private:
-  TC_DISALLOW_COPY_AND_ASSIGN(Workspace);
-};
-
-// Returns a new, strictly increasing int every time it is invoked.
-int GetFreshTypeId();
-
-// Struct to simulate typeid, but without RTTI.
-template <typename T>
-struct TypeId {
-  static int type_id;
-};
-
-template <typename T>
-int TypeId<T>::type_id = GetFreshTypeId();
-
-// A registry that keeps track of workspaces.
-class WorkspaceRegistry {
- public:
-  // Create an empty registry.
-  WorkspaceRegistry() {}
-
-  // Returns the index of a named workspace, adding it to the registry first
-  // if necessary.
-  template <class W>
-  int Request(const std::string &name) {
-    const int id = TypeId<W>::type_id;
-    max_workspace_id_ = std::max(id, max_workspace_id_);
-    workspace_types_[id] = W::TypeName();
-    std::vector<std::string> &names = workspace_names_[id];
-    for (int i = 0; i < names.size(); ++i) {
-      if (names[i] == name) return i;
-    }
-    names.push_back(name);
-    return names.size() - 1;
-  }
-
-  // Returns the maximum workspace id that has been registered.
-  int MaxId() const {
-    return max_workspace_id_;
-  }
-
-  const std::unordered_map<int, std::vector<std::string> > &WorkspaceNames()
-      const {
-    return workspace_names_;
-  }
-
-  // Returns a std::string describing the registered workspaces.
-  std::string DebugString() const;
-
- private:
-  // Workspace type names, indexed as workspace_types_[typeid].
-  std::unordered_map<int, std::string> workspace_types_;
-
-  // Workspace names, indexed as workspace_names_[typeid][workspace].
-  std::unordered_map<int, std::vector<std::string> > workspace_names_;
-
-  // The maximum workspace id that has been registered.
-  int max_workspace_id_ = 0;
-
-  TC_DISALLOW_COPY_AND_ASSIGN(WorkspaceRegistry);
-};
-
-// A typed collected of workspaces. The workspaces are indexed according to an
-// external WorkspaceRegistry. If the WorkspaceSet is const, the contents are
-// also immutable.
-class WorkspaceSet {
- public:
-  ~WorkspaceSet() { Reset(WorkspaceRegistry()); }
-
-  // Returns true if a workspace has been set.
-  template <class W>
-  bool Has(int index) const {
-    const int id = TypeId<W>::type_id;
-    TC_DCHECK_GE(id, 0);
-    TC_DCHECK_LT(id, workspaces_.size());
-    TC_DCHECK_GE(index, 0);
-    TC_DCHECK_LT(index, workspaces_[id].size());
-    if (id >= workspaces_.size()) return false;
-    return workspaces_[id][index] != nullptr;
-  }
-
-  // Returns an indexed workspace; the workspace must have been set.
-  template <class W>
-  const W &Get(int index) const {
-    TC_DCHECK(Has<W>(index));
-    const int id = TypeId<W>::type_id;
-    const Workspace *w = workspaces_[id][index];
-    return reinterpret_cast<const W &>(*w);
-  }
-
-  // Sets an indexed workspace; this takes ownership of the workspace, which
-  // must have been new-allocated.  It is an error to set a workspace twice.
-  template <class W>
-  void Set(int index, W *workspace) {
-    const int id = TypeId<W>::type_id;
-    TC_DCHECK_GE(id, 0);
-    TC_DCHECK_LT(id, workspaces_.size());
-    TC_DCHECK_GE(index, 0);
-    TC_DCHECK_LT(index, workspaces_[id].size());
-    TC_DCHECK(workspaces_[id][index] == nullptr);
-    TC_DCHECK(workspace != nullptr);
-    workspaces_[id][index] = workspace;
-  }
-
-  void Reset(const WorkspaceRegistry &registry) {
-    // Deallocate current workspaces.
-    for (auto &it : workspaces_) {
-      for (size_t index = 0; index < it.size(); ++index) {
-        delete it[index];
-      }
-    }
-    workspaces_.clear();
-    workspaces_.resize(registry.MaxId() + 1, std::vector<Workspace *>());
-    for (auto &it : registry.WorkspaceNames()) {
-      workspaces_[it.first].resize(it.second.size());
-    }
-  }
-
- private:
-  // The set of workspaces, indexed as workspaces_[typeid][index].
-  std::vector<std::vector<Workspace *> > workspaces_;
-};
-
-// A workspace that wraps around a single int.
-class SingletonIntWorkspace : public Workspace {
- public:
-  // Default-initializes the int value.
-  SingletonIntWorkspace() {}
-
-  // Initializes the int with the given value.
-  explicit SingletonIntWorkspace(int value) : value_(value) {}
-
-  // Returns the name of this type of workspace.
-  static std::string TypeName() { return "SingletonInt"; }
-
-  // Returns the int value.
-  int get() const { return value_; }
-
-  // Sets the int value.
-  void set(int value) { value_ = value; }
-
- private:
-  // The enclosed int.
-  int value_ = 0;
-};
-
-// A workspace that wraps around a vector of int.
-class VectorIntWorkspace : public Workspace {
- public:
-  // Creates a vector of the given size.
-  explicit VectorIntWorkspace(int size);
-
-  // Creates a vector initialized with the given array.
-  explicit VectorIntWorkspace(const std::vector<int> &elements);
-
-  // Creates a vector of the given size, with each element initialized to the
-  // given value.
-  VectorIntWorkspace(int size, int value);
-
-  // Returns the name of this type of workspace.
-  static std::string TypeName();
-
-  // Returns the i'th element.
-  int element(int i) const { return elements_[i]; }
-
-  // Sets the i'th element.
-  void set_element(int i, int value) { elements_[i] = value; }
-
- private:
-  // The enclosed vector.
-  std::vector<int> elements_;
-};
-
-// A workspace that wraps around a vector of vector of int.
-class VectorVectorIntWorkspace : public Workspace {
- public:
-  // Creates a vector of empty vectors of the given size.
-  explicit VectorVectorIntWorkspace(int size);
-
-  // Returns the name of this type of workspace.
-  static std::string TypeName();
-
-  // Returns the i'th vector of elements.
-  const std::vector<int> &elements(int i) const { return elements_[i]; }
-
-  // Mutable access to the i'th vector of elements.
-  std::vector<int> *mutable_elements(int i) { return &(elements_[i]); }
-
- private:
-  // The enclosed vector of vector of elements.
-  std::vector<std::vector<int> > elements_;
-};
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_COMMON_WORKSPACE_H_
diff --git a/smartselect/feature-processor.cc b/feature-processor.cc
similarity index 76%
rename from smartselect/feature-processor.cc
rename to feature-processor.cc
index c1db95a..c607b13 100644
--- a/smartselect/feature-processor.cc
+++ b/feature-processor.cc
@@ -14,59 +14,51 @@
  * limitations under the License.
  */
 
-#include "smartselect/feature-processor.h"
+#include "feature-processor.h"
 
 #include <iterator>
 #include <set>
 #include <vector>
 
-#include "smartselect/text-classification-model.pb.h"
 #include "util/base/logging.h"
 #include "util/strings/utf8.h"
 #include "util/utf8/unicodetext.h"
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-#include "unicode/brkiter.h"
-#include "unicode/errorcode.h"
-#include "unicode/uchar.h"
-#endif
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 namespace internal {
 
 TokenFeatureExtractorOptions BuildTokenFeatureExtractorOptions(
-    const FeatureProcessorOptions& options) {
+    const FeatureProcessorOptions* const options) {
   TokenFeatureExtractorOptions extractor_options;
 
-  extractor_options.num_buckets = options.num_buckets();
-  for (int order : options.chargram_orders()) {
-    extractor_options.chargram_orders.push_back(order);
+  extractor_options.num_buckets = options->num_buckets();
+  if (options->chargram_orders() != nullptr) {
+    for (int order : *options->chargram_orders()) {
+      extractor_options.chargram_orders.push_back(order);
+    }
   }
-  extractor_options.max_word_length = options.max_word_length();
-  extractor_options.extract_case_feature = options.extract_case_feature();
-  extractor_options.unicode_aware_features = options.unicode_aware_features();
+  extractor_options.max_word_length = options->max_word_length();
+  extractor_options.extract_case_feature = options->extract_case_feature();
+  extractor_options.unicode_aware_features = options->unicode_aware_features();
   extractor_options.extract_selection_mask_feature =
-      options.extract_selection_mask_feature();
-  for (int i = 0; i < options.regexp_feature_size(); ++i) {
-    extractor_options.regexp_features.push_back(options.regexp_feature(i));
+      options->extract_selection_mask_feature();
+  if (options->regexp_feature() != nullptr) {
+    for (const auto& regexp_feauture : *options->regexp_feature()) {
+      extractor_options.regexp_features.push_back(regexp_feauture->str());
+    }
   }
-  extractor_options.remap_digits = options.remap_digits();
-  extractor_options.lowercase_tokens = options.lowercase_tokens();
+  extractor_options.remap_digits = options->remap_digits();
+  extractor_options.lowercase_tokens = options->lowercase_tokens();
 
-  for (const auto& chargram : options.allowed_chargrams()) {
-    extractor_options.allowed_chargrams.insert(chargram);
+  if (options->allowed_chargrams() != nullptr) {
+    for (const auto& chargram : *options->allowed_chargrams()) {
+      extractor_options.allowed_chargrams.insert(chargram->str());
+    }
   }
-
   return extractor_options;
 }
 
-FeatureProcessorOptions ParseSerializedOptions(
-    const std::string& serialized_options) {
-  FeatureProcessorOptions options;
-  options.ParseFromString(serialized_options);
-  return options;
-}
-
 void SplitTokensOnSelectionBoundaries(CodepointSpan selection,
                                       std::vector<Token>* tokens) {
   for (auto it = tokens->begin(); it != tokens->end(); ++it) {
@@ -119,6 +111,16 @@
   }
 }
 
+UniLib* MaybeCreateUnilib(UniLib* unilib,
+                          std::unique_ptr<UniLib>* owned_unilib) {
+  if (unilib) {
+    return unilib;
+  } else {
+    owned_unilib->reset(new UniLib);
+    return owned_unilib->get();
+  }
+}
+
 }  // namespace internal
 
 void FeatureProcessor::StripTokensFromOtherLines(
@@ -157,30 +159,30 @@
 }
 
 std::string FeatureProcessor::GetDefaultCollection() const {
-  if (options_.default_collection() < 0 ||
-      options_.default_collection() >= options_.collections_size()) {
+  if (options_->default_collection() < 0 ||
+      options_->default_collection() >= options_->collections()->size()) {
     TC_LOG(ERROR)
         << "Invalid or missing default collection. Returning empty string.";
     return "";
   }
-  return options_.collections(options_.default_collection());
+  return (*options_->collections())[options_->default_collection()]->str();
 }
 
 std::vector<Token> FeatureProcessor::Tokenize(
     const std::string& utf8_text) const {
-  if (options_.tokenization_type() ==
-      libtextclassifier::FeatureProcessorOptions::INTERNAL_TOKENIZER) {
+  if (options_->tokenization_type() ==
+      FeatureProcessorOptions_::TokenizationType_INTERNAL_TOKENIZER) {
     return tokenizer_.Tokenize(utf8_text);
-  } else if (options_.tokenization_type() ==
-                 libtextclassifier::FeatureProcessorOptions::ICU ||
-             options_.tokenization_type() ==
-                 libtextclassifier::FeatureProcessorOptions::MIXED) {
+  } else if (options_->tokenization_type() ==
+                 FeatureProcessorOptions_::TokenizationType_ICU ||
+             options_->tokenization_type() ==
+                 FeatureProcessorOptions_::TokenizationType_MIXED) {
     std::vector<Token> result;
     if (!ICUTokenize(utf8_text, &result)) {
       return {};
     }
-    if (options_.tokenization_type() ==
-        libtextclassifier::FeatureProcessorOptions::MIXED) {
+    if (options_->tokenization_type() ==
+        FeatureProcessorOptions_::TokenizationType_MIXED) {
       InternalRetokenize(utf8_text, &result);
     }
     return result;
@@ -205,11 +207,11 @@
 
   const int result_begin_token_index = token_span.first;
   const Token& result_begin_token =
-      tokens[options_.context_size() - result_begin_token_index];
+      tokens[options_->context_size() - result_begin_token_index];
   const int result_begin_codepoint = result_begin_token.start;
   const int result_end_token_index = token_span.second;
   const Token& result_end_token =
-      tokens[options_.context_size() + result_end_token_index];
+      tokens[options_->context_size() + result_end_token_index];
   const int result_end_codepoint = result_end_token.end;
 
   if (result_begin_codepoint == kInvalidIndex ||
@@ -224,9 +226,11 @@
     UnicodeText::const_iterator token_end = token_end_unicode.end();
 
     const int begin_ignored = CountIgnoredSpanBoundaryCodepoints(
-        token_begin, token_begin_unicode.end(), /*count_from_beginning=*/true);
-    const int end_ignored = CountIgnoredSpanBoundaryCodepoints(
-        token_end_unicode.begin(), token_end, /*count_from_beginning=*/false);
+        token_begin, token_begin_unicode.end(),
+        /*count_from_beginning=*/true);
+    const int end_ignored =
+        CountIgnoredSpanBoundaryCodepoints(token_end_unicode.begin(), token_end,
+                                           /*count_from_beginning=*/false);
     // In case everything would be stripped, set the span to the original
     // beginning and zero length.
     if (begin_ignored == (result_end_codepoint - result_begin_codepoint)) {
@@ -257,8 +261,8 @@
   }
 
   const int click_position =
-      options_.context_size();  // Click is always in the middle.
-  const int padding = options_.context_size() - options_.max_selection_span();
+      options_->context_size();  // Click is always in the middle.
+  const int padding = options_->context_size() - options_->max_selection_span();
 
   int span_left = 0;
   for (int i = click_position - 1; i >= padding; i--) {
@@ -282,7 +286,7 @@
   bool tokens_match_span;
   const CodepointIndex tokens_start = tokens[click_position - span_left].start;
   const CodepointIndex tokens_end = tokens[click_position + span_right].end;
-  if (options_.snap_label_span_boundaries_to_containing_tokens()) {
+  if (options_->snap_label_span_boundaries_to_containing_tokens()) {
     tokens_match_span = tokens_start <= span.first && tokens_end >= span.second;
   } else {
     const UnicodeText token_left_unicode = UTF8ToUnicodeText(
@@ -296,7 +300,8 @@
     const int num_punctuation_start = CountIgnoredSpanBoundaryCodepoints(
         span_begin, token_left_unicode.end(), /*count_from_beginning=*/true);
     const int num_punctuation_end = CountIgnoredSpanBoundaryCodepoints(
-        token_right_unicode.begin(), span_end, /*count_from_beginning=*/false);
+        token_right_unicode.begin(), span_end,
+        /*count_from_beginning=*/false);
 
     tokens_match_span = tokens_start <= span.first &&
                         tokens_start + num_punctuation_start >= span.first &&
@@ -422,19 +427,22 @@
 
 int FeatureProcessor::FindCenterToken(CodepointSpan span,
                                       const std::vector<Token>& tokens) const {
-  if (options_.center_token_selection_method() ==
-      FeatureProcessorOptions::CENTER_TOKEN_FROM_CLICK) {
+  if (options_->center_token_selection_method() ==
+      FeatureProcessorOptions_::
+          CenterTokenSelectionMethod_CENTER_TOKEN_FROM_CLICK) {
     return internal::CenterTokenFromClick(span, tokens);
-  } else if (options_.center_token_selection_method() ==
-             FeatureProcessorOptions::CENTER_TOKEN_MIDDLE_OF_SELECTION) {
+  } else if (options_->center_token_selection_method() ==
+             FeatureProcessorOptions_::
+                 CenterTokenSelectionMethod_CENTER_TOKEN_MIDDLE_OF_SELECTION) {
     return internal::CenterTokenFromMiddleOfSelection(span, tokens);
-  } else if (options_.center_token_selection_method() ==
-             FeatureProcessorOptions::DEFAULT_CENTER_TOKEN_METHOD) {
+  } else if (options_->center_token_selection_method() ==
+             FeatureProcessorOptions_::
+                 CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD) {
     // TODO(zilka): Remove once we have new models on the device.
     // It uses the fact that sharing model use
     // split_tokens_on_selection_boundaries and selection not. So depending on
     // this we select the right way of finding the click location.
-    if (!options_.split_tokens_on_selection_boundaries()) {
+    if (!options_->split_tokens_on_selection_boundaries()) {
       // SmartSelection model.
       return internal::CenterTokenFromClick(span, tokens);
     } else {
@@ -462,15 +470,15 @@
 }
 
 void FeatureProcessor::PrepareCodepointRanges(
-    const std::vector<FeatureProcessorOptions::CodepointRange>&
+    const std::vector<const FeatureProcessorOptions_::CodepointRange*>&
         codepoint_ranges,
     std::vector<CodepointRange>* prepared_codepoint_ranges) {
   prepared_codepoint_ranges->clear();
   prepared_codepoint_ranges->reserve(codepoint_ranges.size());
-  for (const FeatureProcessorOptions::CodepointRange& range :
+  for (const FeatureProcessorOptions_::CodepointRange* range :
        codepoint_ranges) {
     prepared_codepoint_ranges->push_back(
-        CodepointRange(range.start(), range.end()));
+        CodepointRange(range->start(), range->end()));
   }
 
   std::sort(prepared_codepoint_ranges->begin(),
@@ -481,8 +489,10 @@
 }
 
 void FeatureProcessor::PrepareIgnoredSpanBoundaryCodepoints() {
-  for (const int codepoint : options_.ignored_span_boundary_codepoints()) {
-    ignored_span_boundary_codepoints_.insert(codepoint);
+  if (options_->ignored_span_boundary_codepoints() != nullptr) {
+    for (const int codepoint : *options_->ignored_span_boundary_codepoints()) {
+      ignored_span_boundary_codepoints_.insert(codepoint);
+    }
   }
 }
 
@@ -555,7 +565,7 @@
 
 std::vector<UnicodeTextRange> FeatureProcessor::SplitContext(
     const UnicodeText& context_unicode) const {
-  if (options_.only_use_line_with_click()) {
+  if (options_->only_use_line_with_click()) {
     std::vector<UnicodeTextRange> lines;
     std::set<char32> codepoints;
     codepoints.insert('\n');
@@ -589,21 +599,17 @@
 }
 
 float FeatureProcessor::SupportedCodepointsRatio(
-    int click_pos, const std::vector<Token>& tokens) const {
+    const TokenSpan& token_span, const std::vector<Token>& tokens) const {
   int num_supported = 0;
   int num_total = 0;
-  for (int i = click_pos - options_.context_size();
-       i <= click_pos + options_.context_size(); ++i) {
-    const bool is_valid_token = i >= 0 && i < tokens.size();
-    if (is_valid_token) {
-      const UnicodeText value =
-          UTF8ToUnicodeText(tokens[i].value, /*do_copy=*/false);
-      for (auto codepoint : value) {
-        if (IsCodepointInRanges(codepoint, supported_codepoint_ranges_)) {
-          ++num_supported;
-        }
-        ++num_total;
+  for (int i = token_span.first; i < token_span.second; ++i) {
+    const UnicodeText value =
+        UTF8ToUnicodeText(tokens[i].value, /*do_copy=*/false);
+    for (auto codepoint : value) {
+      if (IsCodepointInRanges(codepoint, supported_codepoint_ranges_)) {
+        ++num_supported;
       }
+      ++num_total;
     }
   }
   return static_cast<float>(num_supported) / static_cast<float>(num_total);
@@ -640,7 +646,7 @@
 int FeatureProcessor::CollectionToLabel(const std::string& collection) const {
   const auto it = collection_to_label_.find(collection);
   if (it == collection_to_label_.end()) {
-    return options_.default_collection();
+    return options_->default_collection();
   } else {
     return it->second;
   }
@@ -648,22 +654,24 @@
 
 std::string FeatureProcessor::LabelToCollection(int label) const {
   if (label >= 0 && label < collection_to_label_.size()) {
-    return options_.collections(label);
+    return (*options_->collections())[label]->str();
   } else {
     return GetDefaultCollection();
   }
 }
 
 void FeatureProcessor::MakeLabelMaps() {
-  for (int i = 0; i < options_.collections().size(); ++i) {
-    collection_to_label_[options_.collections(i)] = i;
+  if (options_->collections() != nullptr) {
+    for (int i = 0; i < options_->collections()->size(); ++i) {
+      collection_to_label_[(*options_->collections())[i]->str()] = i;
+    }
   }
 
   int selection_label_id = 0;
-  for (int l = 0; l < (options_.max_selection_span() + 1); ++l) {
-    for (int r = 0; r < (options_.max_selection_span() + 1); ++r) {
-      if (!options_.selection_reduced_output_space() ||
-          r + l <= options_.max_selection_span()) {
+  for (int l = 0; l < (options_->max_selection_span() + 1); ++l) {
+    for (int r = 0; r < (options_->max_selection_span() + 1); ++r) {
+      if (!options_->selection_reduced_output_space() ||
+          r + l <= options_->max_selection_span()) {
         TokenSpan token_span{l, r};
         selection_to_label_[token_span] = selection_label_id;
         label_to_selection_.push_back(token_span);
@@ -680,11 +688,11 @@
   TC_CHECK(tokens != nullptr);
   *tokens = Tokenize(context);
 
-  if (options_.split_tokens_on_selection_boundaries()) {
+  if (options_->split_tokens_on_selection_boundaries()) {
     internal::SplitTokensOnSelectionBoundaries(input_span, tokens);
   }
 
-  if (options_.only_use_line_with_click()) {
+  if (options_->only_use_line_with_click()) {
     StripTokensFromOtherLines(context, input_span, tokens);
   }
 
@@ -693,6 +701,11 @@
     click_pos = &local_click_pos;
   }
   *click_pos = FindCenterToken(input_span, *tokens);
+  if (*click_pos == kInvalidIndex) {
+    // If the default click method failed, let's try to do sub-token matching
+    // before we fail.
+    *click_pos = internal::CenterTokenFromClick(input_span, *tokens);
+  }
 }
 
 namespace internal {
@@ -734,118 +747,102 @@
 }  // namespace internal
 
 bool FeatureProcessor::ExtractFeatures(
-    const std::string& context, CodepointSpan input_span,
-    TokenSpan relative_click_span, const FeatureVectorFn& feature_vector_fn,
-    int feature_vector_size, std::vector<Token>* tokens, int* click_pos,
+    const std::vector<Token>& tokens, TokenSpan token_span,
+    EmbeddingExecutor* embedding_executor, int feature_vector_size,
     std::unique_ptr<CachedFeatures>* cached_features) const {
-  TokenizeAndFindClick(context, input_span, tokens, click_pos);
-
-  if (input_span.first != kInvalidIndex && input_span.second != kInvalidIndex) {
-    // If the default click method failed, let's try to do sub-token matching
-    // before we fail.
-    if (*click_pos == kInvalidIndex) {
-      *click_pos = internal::CenterTokenFromClick(input_span, *tokens);
-      if (*click_pos == kInvalidIndex) {
-        return false;
-      }
-    }
-  } else {
-    // If input_span is unspecified, click the first token and extract features
-    // from all tokens.
-    *click_pos = 0;
-    relative_click_span = {0, tokens->size()};
+  if (options_->feature_version() < 2) {
+    TC_LOG(ERROR) << "Unsupported feature version.";
+    return false;
+  }
+  if (!options_->bounds_sensitive_features() ||
+      !options_->bounds_sensitive_features()->enabled()) {
+    TC_LOG(ERROR) << "Bounds-sensitive features not enabled.";
+    return false;
   }
 
-  internal::StripOrPadTokens(relative_click_span, options_.context_size(),
-                             tokens, click_pos);
-
-  if (options_.min_supported_codepoint_ratio() > 0) {
+  if (options_->min_supported_codepoint_ratio() > 0) {
     const float supported_codepoint_ratio =
-        SupportedCodepointsRatio(*click_pos, *tokens);
-    if (supported_codepoint_ratio < options_.min_supported_codepoint_ratio()) {
+        SupportedCodepointsRatio(token_span, tokens);
+    if (supported_codepoint_ratio < options_->min_supported_codepoint_ratio()) {
       TC_VLOG(1) << "Not enough supported codepoints in the context: "
                  << supported_codepoint_ratio;
       return false;
     }
   }
 
-  std::vector<std::vector<int>> sparse_features(tokens->size());
-  std::vector<std::vector<float>> dense_features(tokens->size());
-  for (int i = 0; i < tokens->size(); ++i) {
-    const Token& token = (*tokens)[i];
-    if (!feature_extractor_.Extract(token, token.IsContainedInSpan(input_span),
-                                    &(sparse_features[i]),
-                                    &(dense_features[i]))) {
+  std::vector<std::vector<int>> sparse_features(TokenSpanSize(token_span));
+  std::vector<std::vector<float>> dense_features(TokenSpanSize(token_span));
+  for (int i = token_span.first; i < token_span.second; ++i) {
+    const Token& token = tokens[i];
+    const int features_index = i - token_span.first;
+    if (!feature_extractor_.Extract(token, false,
+                                    &(sparse_features[features_index]),
+                                    &(dense_features[features_index]))) {
       TC_LOG(ERROR) << "Could not extract token's features: " << token;
       return false;
     }
   }
 
-  cached_features->reset(new CachedFeatures(
-      *tokens, options_.context_size(), sparse_features, dense_features,
-      feature_vector_fn, feature_vector_size));
-
-  if (*cached_features == nullptr) {
+  std::vector<int> padding_sparse_features;
+  std::vector<float> padding_dense_features;
+  if (!feature_extractor_.Extract(Token(), false, &padding_sparse_features,
+                                  &padding_dense_features)) {
+    TC_LOG(ERROR) << "Could not extract padding token's features.";
     return false;
   }
 
-  if (options_.feature_version() == 0) {
-    (*cached_features)
-        ->SetV0FeatureMode(feature_vector_size -
-                           feature_extractor_.DenseFeaturesCount());
-  }
+  cached_features->reset(new CachedFeatures(
+      token_span, sparse_features, dense_features, padding_sparse_features,
+      padding_dense_features, options_->bounds_sensitive_features(),
+      embedding_executor, feature_vector_size));
 
   return true;
 }
 
 bool FeatureProcessor::ICUTokenize(const std::string& context,
                                    std::vector<Token>* result) const {
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-  icu::ErrorCode status;
-  icu::UnicodeString unicode_text = icu::UnicodeString::fromUTF8(context);
-  std::unique_ptr<icu::BreakIterator> break_iterator(
-      icu::BreakIterator::createWordInstance(icu::Locale("en"), status));
-  if (!status.isSuccess()) {
-    TC_LOG(ERROR) << "Break iterator did not initialize properly: "
-                  << status.errorName();
+  std::unique_ptr<UniLib::BreakIterator> break_iterator =
+      unilib_->CreateBreakIterator(context);
+  if (!break_iterator) {
     return false;
   }
 
-  break_iterator->setText(unicode_text);
-
-  size_t last_break_index = 0;
-  size_t break_index = 0;
-  size_t last_unicode_index = 0;
-  size_t unicode_index = 0;
-  while ((break_index = break_iterator->next()) != icu::BreakIterator::DONE) {
-    icu::UnicodeString token(unicode_text, last_break_index,
-                             break_index - last_break_index);
-    int token_length = token.countChar32();
+  UnicodeText context_unicode = UTF8ToUnicodeText(context, /*do_copy=*/false);
+  int last_break_index = 0;
+  int break_index = 0;
+  int last_unicode_index = 0;
+  int unicode_index = 0;
+  auto token_begin_it = context_unicode.begin();
+  while ((break_index = break_iterator->Next()) !=
+         UniLib::BreakIterator::kDone) {
+    const int token_length = break_index - last_break_index;
     unicode_index = last_unicode_index + token_length;
 
-    std::string token_utf8;
-    token.toUTF8String(token_utf8);
+    auto token_end_it = token_begin_it;
+    std::advance(token_end_it, token_length);
 
+    // Determine if the whole token is whitespace.
     bool is_whitespace = true;
-    for (int i = 0; i < token.length(); i++) {
-      if (!u_isWhitespace(token.char32At(i))) {
+    for (auto char_it = token_begin_it; char_it < token_end_it; ++char_it) {
+      if (!unilib_->IsWhitespace(*char_it)) {
         is_whitespace = false;
+        break;
       }
     }
 
-    if (!is_whitespace || options_.icu_preserve_whitespace_tokens()) {
-      result->push_back(Token(token_utf8, last_unicode_index, unicode_index));
+    const std::string token =
+        context_unicode.UTF8Substring(token_begin_it, token_end_it);
+
+    if (!is_whitespace || options_->icu_preserve_whitespace_tokens()) {
+      result->push_back(Token(token, last_unicode_index, unicode_index));
     }
 
     last_break_index = break_index;
     last_unicode_index = unicode_index;
+    token_begin_it = token_end_it;
   }
 
   return true;
-#else
-  TC_LOG(WARNING) << "Can't tokenize, ICU not supported";
-  return false;
-#endif
 }
 
 void FeatureProcessor::InternalRetokenize(const std::string& context,
@@ -914,4 +911,4 @@
   }
 }
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/smartselect/feature-processor.h b/feature-processor.h
similarity index 76%
rename from smartselect/feature-processor.h
rename to feature-processor.h
index ef9a3df..834c260 100644
--- a/smartselect/feature-processor.h
+++ b/feature-processor.h
@@ -16,42 +16,33 @@
 
 // Feature processing for FFModel (feed-forward SmartSelection model).
 
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_FEATURE_PROCESSOR_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_FEATURE_PROCESSOR_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_FEATURE_PROCESSOR_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_FEATURE_PROCESSOR_H_
 
+#include <map>
 #include <memory>
 #include <set>
 #include <string>
 #include <vector>
 
-#include "smartselect/cached-features.h"
-#include "smartselect/text-classification-model.pb.h"
-#include "smartselect/token-feature-extractor.h"
-#include "smartselect/tokenizer.h"
-#include "smartselect/types.h"
+#include "cached-features.h"
+#include "model_generated.h"
+#include "token-feature-extractor.h"
+#include "tokenizer.h"
+#include "types.h"
+#include "util/base/integral_types.h"
 #include "util/base/logging.h"
 #include "util/utf8/unicodetext.h"
+#include "util/utf8/unilib.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 constexpr int kInvalidLabel = -1;
 
-// Maps a vector of sparse features and a vector of dense features to a vector
-// of features that combines both.
-// The output is written to the memory location pointed to  by the last float*
-// argument.
-// Returns true on success false on failure.
-using FeatureVectorFn = std::function<bool(const std::vector<int>&,
-                                           const std::vector<float>&, float*)>;
-
 namespace internal {
 
-// Parses the serialized protocol buffer.
-FeatureProcessorOptions ParseSerializedOptions(
-    const std::string& serialized_options);
-
 TokenFeatureExtractorOptions BuildTokenFeatureExtractorOptions(
-    const FeatureProcessorOptions& options);
+    const FeatureProcessorOptions* options);
 
 // Splits tokens that contain the selection boundary inside them.
 // E.g. "foo{bar}@google.com" -> "foo", "bar", "@google.com"
@@ -73,6 +64,11 @@
 void StripOrPadTokens(TokenSpan relative_click_span, int context_size,
                       std::vector<Token>* tokens, int* click_pos);
 
+// If unilib is not nullptr, just returns unilib. Otherwise, if unilib is
+// nullptr, will create UniLib, assign ownership to owned_unilib, and return it.
+UniLib* MaybeCreateUnilib(UniLib* unilib,
+                          std::unique_ptr<UniLib>* owned_unilib);
+
 }  // namespace internal
 
 // Converts a codepoint span to a token span in the given list of tokens.
@@ -90,27 +86,36 @@
 // Takes care of preparing features for the span prediction model.
 class FeatureProcessor {
  public:
-  explicit FeatureProcessor(const FeatureProcessorOptions& options)
-      : feature_extractor_(
-            internal::BuildTokenFeatureExtractorOptions(options)),
+  // If unilib is nullptr, will create and own an instance of a UniLib,
+  // otherwise will use what's passed in.
+  explicit FeatureProcessor(const FeatureProcessorOptions* options,
+                            UniLib* unilib = nullptr)
+      : owned_unilib_(nullptr),
+        unilib_(internal::MaybeCreateUnilib(unilib, &owned_unilib_)),
+        feature_extractor_(internal::BuildTokenFeatureExtractorOptions(options),
+                           *unilib_),
         options_(options),
-        tokenizer_({options.tokenization_codepoint_config().begin(),
-                    options.tokenization_codepoint_config().end()}) {
+        tokenizer_(
+            options->tokenization_codepoint_config() != nullptr
+                ? Tokenizer({options->tokenization_codepoint_config()->begin(),
+                             options->tokenization_codepoint_config()->end()},
+                            options->tokenize_on_script_change())
+                : Tokenizer({}, /*split_on_script_change=*/false)) {
     MakeLabelMaps();
-    PrepareCodepointRanges({options.supported_codepoint_ranges().begin(),
-                            options.supported_codepoint_ranges().end()},
-                           &supported_codepoint_ranges_);
-    PrepareCodepointRanges(
-        {options.internal_tokenizer_codepoint_ranges().begin(),
-         options.internal_tokenizer_codepoint_ranges().end()},
-        &internal_tokenizer_codepoint_ranges_);
+    if (options->supported_codepoint_ranges() != nullptr) {
+      PrepareCodepointRanges({options->supported_codepoint_ranges()->begin(),
+                              options->supported_codepoint_ranges()->end()},
+                             &supported_codepoint_ranges_);
+    }
+    if (options->internal_tokenizer_codepoint_ranges() != nullptr) {
+      PrepareCodepointRanges(
+          {options->internal_tokenizer_codepoint_ranges()->begin(),
+           options->internal_tokenizer_codepoint_ranges()->end()},
+          &internal_tokenizer_codepoint_ranges_);
+    }
     PrepareIgnoredSpanBoundaryCodepoints();
   }
 
-  explicit FeatureProcessor(const std::string& serialized_options)
-      : FeatureProcessor(internal::ParseSerializedOptions(serialized_options)) {
-  }
-
   // Tokenizes the input string using the selected tokenization method.
   std::vector<Token> Tokenize(const std::string& utf8_text) const;
 
@@ -129,7 +134,7 @@
   // Gets the name of the default collection.
   std::string GetDefaultCollection() const;
 
-  const FeatureProcessorOptions& GetOptions() const { return options_; }
+  const FeatureProcessorOptions* GetOptions() const { return options_; }
 
   // Tokenizes the context and input span, and finds the click position.
   void TokenizeAndFindClick(const std::string& context,
@@ -138,13 +143,9 @@
 
   // Extracts features as a CachedFeatures object that can be used for repeated
   // inference over token spans in the given context.
-  // When input_span == {kInvalidIndex, kInvalidIndex} then, relative_click_span
-  // is ignored, and all tokens extracted from context will be considered.
-  bool ExtractFeatures(const std::string& context, CodepointSpan input_span,
-                       TokenSpan relative_click_span,
-                       const FeatureVectorFn& feature_vector_fn,
-                       int feature_vector_size, std::vector<Token>* tokens,
-                       int* click_pos,
+  bool ExtractFeatures(const std::vector<Token>& tokens, TokenSpan token_span,
+                       EmbeddingExecutor* embedding_executor,
+                       int feature_vector_size,
                        std::unique_ptr<CachedFeatures>* cached_features) const;
 
   // Fills selection_label_spans with CodepointSpans that correspond to the
@@ -158,6 +159,8 @@
     return feature_extractor_.DenseFeaturesCount();
   }
 
+  int EmbeddingSize() const { return options_->embedding_size(); }
+
   // Splits context to several segments according to configuration.
   std::vector<UnicodeTextRange> SplitContext(
       const UnicodeText& context_unicode) const;
@@ -191,7 +194,7 @@
   // Spannable tokens are those tokens of context, which the model predicts
   // selection spans over (i.e., there is 1:1 correspondence between the output
   // classes of the model and each of the spannable tokens).
-  int GetNumContextTokens() const { return options_.context_size() * 2 + 1; }
+  int GetNumContextTokens() const { return options_->context_size() * 2 + 1; }
 
   // Converts a label into a span of codepoint indices corresponding to it
   // given output_tokens.
@@ -206,13 +209,13 @@
   int TokenSpanToLabel(const std::pair<TokenIndex, TokenIndex>& span) const;
 
   void PrepareCodepointRanges(
-      const std::vector<FeatureProcessorOptions::CodepointRange>&
+      const std::vector<const FeatureProcessorOptions_::CodepointRange*>&
           codepoint_ranges,
       std::vector<CodepointRange>* prepared_codepoint_ranges);
 
   // Returns the ratio of supported codepoints to total number of codepoints in
-  // the input context around given click position.
-  float SupportedCodepointsRatio(int click_pos,
+  // the given token span.
+  float SupportedCodepointsRatio(const TokenSpan& token_span,
                                  const std::vector<Token>& tokens) const;
 
   // Returns true if given codepoint is covered by the given sorted vector of
@@ -257,6 +260,11 @@
   void StripTokensFromOtherLines(const std::string& context, CodepointSpan span,
                                  std::vector<Token>* tokens) const;
 
+ private:
+  std::unique_ptr<UniLib> owned_unilib_;
+  UniLib* unilib_;
+
+ protected:
   const TokenFeatureExtractor feature_extractor_;
 
   // Codepoint ranges that define what codepoints are supported by the model.
@@ -274,7 +282,7 @@
   // predicted spans.
   std::set<int32> ignored_span_boundary_codepoints_;
 
-  const FeatureProcessorOptions options_;
+  const FeatureProcessorOptions* const options_;
 
   // Mapping between token selection spans and labels ids.
   std::map<TokenSpan, int> selection_to_label_;
@@ -286,6 +294,6 @@
   Tokenizer tokenizer_;
 };
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_SMARTSELECT_FEATURE_PROCESSOR_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_FEATURE_PROCESSOR_H_
diff --git a/smartselect/feature-processor_test.cc b/feature-processor_test.cc
similarity index 69%
rename from smartselect/feature-processor_test.cc
rename to feature-processor_test.cc
index 9bee67a..5af8b96 100644
--- a/smartselect/feature-processor_test.cc
+++ b/feature-processor_test.cc
@@ -14,17 +14,27 @@
  * limitations under the License.
  */
 
-#include "smartselect/feature-processor.h"
+#include "feature-processor.h"
+
+#include "model-executor.h"
+#include "tensor-view.h"
 
 #include "gmock/gmock.h"
 #include "gtest/gtest.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 namespace {
 
 using testing::ElementsAreArray;
 using testing::FloatEq;
 
+flatbuffers::DetachedBuffer PackFeatureProcessorOptions(
+    const FeatureProcessorOptionsT& options) {
+  flatbuffers::FlatBufferBuilder builder;
+  builder.Finish(CreateFeatureProcessorOptions(builder, &options));
+  return builder.Release();
+}
+
 class TestingFeatureProcessor : public FeatureProcessor {
  public:
   using FeatureProcessor::CountIgnoredSpanBoundaryCodepoints;
@@ -37,6 +47,24 @@
   using FeatureProcessor::SupportedCodepointsRatio;
 };
 
+// EmbeddingExecutor that always returns features based on
+class FakeEmbeddingExecutor : public EmbeddingExecutor {
+ public:
+  bool AddEmbedding(const TensorView<int>& sparse_features, float* dest,
+                    int dest_size) override {
+    TC_CHECK_GE(dest_size, 4);
+    EXPECT_EQ(sparse_features.size(), 1);
+    dest[0] = sparse_features.data()[0];
+    dest[1] = sparse_features.data()[0];
+    dest[2] = -sparse_features.data()[0];
+    dest[3] = -sparse_features.data()[0];
+    return true;
+  }
+
+ private:
+  std::vector<float> storage_;
+};
+
 TEST(FeatureProcessorTest, SplitTokensOnSelectionBoundariesMiddle) {
   std::vector<Token> tokens{Token("Hělló", 0, 5),
                             Token("fěěbař@google.com", 6, 23),
@@ -119,9 +147,11 @@
 }
 
 TEST(FeatureProcessorTest, KeepLineWithClickFirst) {
-  FeatureProcessorOptions options;
-  options.set_only_use_line_with_click(true);
-  TestingFeatureProcessor feature_processor(options);
+  FeatureProcessorOptionsT options;
+  options.only_use_line_with_click = true;
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
 
   const std::string context = "Fiřst Lině\nSěcond Lině\nThiřd Lině";
   const CodepointSpan span = {0, 5};
@@ -141,9 +171,11 @@
 }
 
 TEST(FeatureProcessorTest, KeepLineWithClickSecond) {
-  FeatureProcessorOptions options;
-  options.set_only_use_line_with_click(true);
-  TestingFeatureProcessor feature_processor(options);
+  FeatureProcessorOptionsT options;
+  options.only_use_line_with_click = true;
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
 
   const std::string context = "Fiřst Lině\nSěcond Lině\nThiřd Lině";
   const CodepointSpan span = {18, 22};
@@ -163,9 +195,11 @@
 }
 
 TEST(FeatureProcessorTest, KeepLineWithClickThird) {
-  FeatureProcessorOptions options;
-  options.set_only_use_line_with_click(true);
-  TestingFeatureProcessor feature_processor(options);
+  FeatureProcessorOptionsT options;
+  options.only_use_line_with_click = true;
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
 
   const std::string context = "Fiřst Lině\nSěcond Lině\nThiřd Lině";
   const CodepointSpan span = {24, 33};
@@ -185,9 +219,11 @@
 }
 
 TEST(FeatureProcessorTest, KeepLineWithClickSecondWithPipe) {
-  FeatureProcessorOptions options;
-  options.set_only_use_line_with_click(true);
-  TestingFeatureProcessor feature_processor(options);
+  FeatureProcessorOptionsT options;
+  options.only_use_line_with_click = true;
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
 
   const std::string context = "Fiřst Lině|Sěcond Lině\nThiřd Lině";
   const CodepointSpan span = {18, 22};
@@ -207,9 +243,11 @@
 }
 
 TEST(FeatureProcessorTest, KeepLineWithCrosslineClick) {
-  FeatureProcessorOptions options;
-  options.set_only_use_line_with_click(true);
-  TestingFeatureProcessor feature_processor(options);
+  FeatureProcessorOptionsT options;
+  options.only_use_line_with_click = true;
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
 
   const std::string context = "Fiřst Lině\nSěcond Lině\nThiřd Lině";
   const CodepointSpan span = {5, 23};
@@ -231,18 +269,21 @@
 }
 
 TEST(FeatureProcessorTest, SpanToLabel) {
-  FeatureProcessorOptions options;
-  options.set_context_size(1);
-  options.set_max_selection_span(1);
-  options.set_snap_label_span_boundaries_to_containing_tokens(false);
+  FeatureProcessorOptionsT options;
+  options.context_size = 1;
+  options.max_selection_span = 1;
+  options.snap_label_span_boundaries_to_containing_tokens = false;
 
-  TokenizationCodepointRange* config =
-      options.add_tokenization_codepoint_config();
-  config->set_start(32);
-  config->set_end(33);
-  config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
+  options.tokenization_codepoint_config.emplace_back(
+      new TokenizationCodepointRangeT());
+  auto& config = options.tokenization_codepoint_config.back();
+  config->start = 32;
+  config->end = 33;
+  config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
 
-  TestingFeatureProcessor feature_processor(options);
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
   std::vector<Token> tokens = feature_processor.Tokenize("one, two, three");
   ASSERT_EQ(3, tokens.size());
   int label;
@@ -256,8 +297,11 @@
   EXPECT_EQ(0, token_span.second);
 
   // Reconfigure with snapping enabled.
-  options.set_snap_label_span_boundaries_to_containing_tokens(true);
-  TestingFeatureProcessor feature_processor2(options);
+  options.snap_label_span_boundaries_to_containing_tokens = true;
+  flatbuffers::DetachedBuffer options2_fb =
+      PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor2(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options2_fb.data()));
   int label2;
   ASSERT_TRUE(feature_processor2.SpanToLabel({5, 8}, tokens, &label2));
   EXPECT_EQ(label, label2);
@@ -273,9 +317,12 @@
   EXPECT_EQ(kInvalidLabel, label2);
 
   // Multiple tokens.
-  options.set_context_size(2);
-  options.set_max_selection_span(2);
-  TestingFeatureProcessor feature_processor3(options);
+  options.context_size = 2;
+  options.max_selection_span = 2;
+  flatbuffers::DetachedBuffer options3_fb =
+      PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor3(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options3_fb.data()));
   tokens = feature_processor3.Tokenize("zero, one, two, three, four");
   ASSERT_TRUE(feature_processor3.SpanToLabel({6, 15}, tokens, &label2));
   EXPECT_NE(kInvalidLabel, label2);
@@ -293,18 +340,21 @@
 }
 
 TEST(FeatureProcessorTest, SpanToLabelIgnoresPunctuation) {
-  FeatureProcessorOptions options;
-  options.set_context_size(1);
-  options.set_max_selection_span(1);
-  options.set_snap_label_span_boundaries_to_containing_tokens(false);
+  FeatureProcessorOptionsT options;
+  options.context_size = 1;
+  options.max_selection_span = 1;
+  options.snap_label_span_boundaries_to_containing_tokens = false;
 
-  TokenizationCodepointRange* config =
-      options.add_tokenization_codepoint_config();
-  config->set_start(32);
-  config->set_end(33);
-  config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
+  options.tokenization_codepoint_config.emplace_back(
+      new TokenizationCodepointRangeT());
+  auto& config = options.tokenization_codepoint_config.back();
+  config->start = 32;
+  config->end = 33;
+  config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
 
-  TestingFeatureProcessor feature_processor(options);
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
   std::vector<Token> tokens = feature_processor.Tokenize("one, two, three");
   ASSERT_EQ(3, tokens.size());
   int label;
@@ -318,8 +368,11 @@
   EXPECT_EQ(0, token_span.second);
 
   // Reconfigure with snapping enabled.
-  options.set_snap_label_span_boundaries_to_containing_tokens(true);
-  TestingFeatureProcessor feature_processor2(options);
+  options.snap_label_span_boundaries_to_containing_tokens = true;
+  flatbuffers::DetachedBuffer options2_fb =
+      PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor2(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options2_fb.data()));
   int label2;
   ASSERT_TRUE(feature_processor2.SpanToLabel({5, 8}, tokens, &label2));
   EXPECT_EQ(label, label2);
@@ -335,9 +388,12 @@
   EXPECT_EQ(kInvalidLabel, label2);
 
   // Multiple tokens.
-  options.set_context_size(2);
-  options.set_max_selection_span(2);
-  TestingFeatureProcessor feature_processor3(options);
+  options.context_size = 2;
+  options.max_selection_span = 2;
+  flatbuffers::DetachedBuffer options3_fb =
+      PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor3(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options3_fb.data()));
   tokens = feature_processor3.Tokenize("zero, one, two, three, four");
   ASSERT_TRUE(feature_processor3.SpanToLabel({6, 15}, tokens, &label2));
   EXPECT_NE(kInvalidLabel, label2);
@@ -420,39 +476,64 @@
 }
 
 TEST(FeatureProcessorTest, SupportedCodepointsRatio) {
-  FeatureProcessorOptions options;
-  options.set_context_size(2);
-  options.set_max_selection_span(2);
-  options.set_snap_label_span_boundaries_to_containing_tokens(false);
+  FeatureProcessorOptionsT options;
+  options.context_size = 2;
+  options.max_selection_span = 2;
+  options.snap_label_span_boundaries_to_containing_tokens = false;
+  options.feature_version = 2;
+  options.embedding_size = 4;
+  options.bounds_sensitive_features.reset(
+      new FeatureProcessorOptions_::BoundsSensitiveFeaturesT());
+  options.bounds_sensitive_features->enabled = true;
+  options.bounds_sensitive_features->num_tokens_before = 5;
+  options.bounds_sensitive_features->num_tokens_inside_left = 3;
+  options.bounds_sensitive_features->num_tokens_inside_right = 3;
+  options.bounds_sensitive_features->num_tokens_after = 5;
+  options.bounds_sensitive_features->include_inside_bag = true;
+  options.bounds_sensitive_features->include_inside_length = true;
 
-  TokenizationCodepointRange* config =
-      options.add_tokenization_codepoint_config();
-  config->set_start(32);
-  config->set_end(33);
-  config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
+  options.tokenization_codepoint_config.emplace_back(
+      new TokenizationCodepointRangeT());
+  auto& config = options.tokenization_codepoint_config.back();
+  config->start = 32;
+  config->end = 33;
+  config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
 
-  FeatureProcessorOptions::CodepointRange* range;
-  range = options.add_supported_codepoint_ranges();
-  range->set_start(0);
-  range->set_end(128);
+  {
+    options.supported_codepoint_ranges.emplace_back(
+        new FeatureProcessorOptions_::CodepointRangeT());
+    auto& range = options.supported_codepoint_ranges.back();
+    range->start = 0;
+    range->end = 128;
+  }
 
-  range = options.add_supported_codepoint_ranges();
-  range->set_start(10000);
-  range->set_end(10001);
+  {
+    options.supported_codepoint_ranges.emplace_back(
+        new FeatureProcessorOptions_::CodepointRangeT());
+    auto& range = options.supported_codepoint_ranges.back();
+    range->start = 10000;
+    range->end = 10001;
+  }
 
-  range = options.add_supported_codepoint_ranges();
-  range->set_start(20000);
-  range->set_end(30000);
+  {
+    options.supported_codepoint_ranges.emplace_back(
+        new FeatureProcessorOptions_::CodepointRangeT());
+    auto& range = options.supported_codepoint_ranges.back();
+    range->start = 20000;
+    range->end = 30000;
+  }
 
-  TestingFeatureProcessor feature_processor(options);
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
   EXPECT_THAT(feature_processor.SupportedCodepointsRatio(
-                  1, feature_processor.Tokenize("aaa bbb ccc")),
+                  {0, 3}, feature_processor.Tokenize("aaa bbb ccc")),
               FloatEq(1.0));
   EXPECT_THAT(feature_processor.SupportedCodepointsRatio(
-                  1, feature_processor.Tokenize("aaa bbb ěěě")),
+                  {0, 3}, feature_processor.Tokenize("aaa bbb ěěě")),
               FloatEq(2.0 / 3));
   EXPECT_THAT(feature_processor.SupportedCodepointsRatio(
-                  1, feature_processor.Tokenize("ěěě řřř ěěě")),
+                  {0, 3}, feature_processor.Tokenize("ěěě řřř ěěě")),
               FloatEq(0.0));
   EXPECT_FALSE(feature_processor.IsCodepointInRanges(
       -1, feature_processor.supported_codepoint_ranges_));
@@ -473,32 +554,39 @@
   EXPECT_TRUE(feature_processor.IsCodepointInRanges(
       25000, feature_processor.supported_codepoint_ranges_));
 
-  std::vector<Token> tokens;
-  int click_pos;
-  std::vector<float> extra_features;
   std::unique_ptr<CachedFeatures> cached_features;
 
-  auto feature_fn = [](const std::vector<int>& sparse_features,
-                       const std::vector<float>& dense_features,
-                       float* embedding) { return true; };
+  FakeEmbeddingExecutor embedding_executor;
 
-  options.set_min_supported_codepoint_ratio(0.0);
-  TestingFeatureProcessor feature_processor2(options);
-  EXPECT_TRUE(feature_processor2.ExtractFeatures("ěěě řřř eee", {4, 7}, {0, 0},
-                                                 feature_fn, 2, &tokens,
-                                                 &click_pos, &cached_features));
+  const std::vector<Token> tokens = {Token("ěěě", 0, 3), Token("řřř", 4, 7),
+                                     Token("eee", 8, 11)};
 
-  options.set_min_supported_codepoint_ratio(0.2);
-  TestingFeatureProcessor feature_processor3(options);
-  EXPECT_TRUE(feature_processor3.ExtractFeatures("ěěě řřř eee", {4, 7}, {0, 0},
-                                                 feature_fn, 2, &tokens,
-                                                 &click_pos, &cached_features));
+  options.min_supported_codepoint_ratio = 0.0;
+  flatbuffers::DetachedBuffer options2_fb =
+      PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor2(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options2_fb.data()));
+  EXPECT_TRUE(feature_processor2.ExtractFeatures(
+      tokens, {0, 3}, &embedding_executor,
+      /*feature_vector_size=*/4, &cached_features));
 
-  options.set_min_supported_codepoint_ratio(0.5);
-  TestingFeatureProcessor feature_processor4(options);
+  options.min_supported_codepoint_ratio = 0.2;
+  flatbuffers::DetachedBuffer options3_fb =
+      PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor3(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options3_fb.data()));
+  EXPECT_TRUE(feature_processor3.ExtractFeatures(
+      tokens, {0, 3}, &embedding_executor,
+      /*feature_vector_size=*/4, &cached_features));
+
+  options.min_supported_codepoint_ratio = 0.5;
+  flatbuffers::DetachedBuffer options4_fb =
+      PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor4(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options4_fb.data()));
   EXPECT_FALSE(feature_processor4.ExtractFeatures(
-      "ěěě řřř eee", {4, 7}, {0, 0}, feature_fn, 2, &tokens, &click_pos,
-      &cached_features));
+      tokens, {0, 3}, &embedding_executor,
+      /*feature_vector_size=*/4, &cached_features));
 }
 
 TEST(FeatureProcessorTest, StripUnusedTokensWithNoRelativeClick) {
@@ -613,12 +701,45 @@
   EXPECT_EQ(click_index, 5);
 }
 
-TEST(FeatureProcessorTest, ICUTokenize) {
-  FeatureProcessorOptions options;
-  options.set_tokenization_type(
-      libtextclassifier::FeatureProcessorOptions::ICU);
+TEST(FeatureProcessorTest, InternalTokenizeOnScriptChange) {
+  FeatureProcessorOptionsT options;
+  options.tokenization_codepoint_config.emplace_back(
+      new TokenizationCodepointRangeT());
+  {
+    auto& config = options.tokenization_codepoint_config.back();
+    config->start = 0;
+    config->end = 256;
+    config->role = TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+    config->script_id = 1;
+  }
+  options.tokenize_on_script_change = false;
 
-  TestingFeatureProcessor feature_processor(options);
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
+
+  EXPECT_EQ(feature_processor.Tokenize("앨라배마123웹사이트"),
+            std::vector<Token>({Token("앨라배마123웹사이트", 0, 11)}));
+
+  options.tokenize_on_script_change = true;
+  flatbuffers::DetachedBuffer options_fb2 =
+      PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor2(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb2.data()));
+
+  EXPECT_EQ(feature_processor2.Tokenize("앨라배마123웹사이트"),
+            std::vector<Token>({Token("앨라배마", 0, 4), Token("123", 4, 7),
+                                Token("웹사이트", 7, 11)}));
+}
+
+#ifdef LIBTEXTCLASSIFIER_TEST_ICU
+TEST(FeatureProcessorTest, ICUTokenize) {
+  FeatureProcessorOptionsT options;
+  options.tokenization_type = FeatureProcessorOptions_::TokenizationType_ICU;
+
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
   std::vector<Token> tokens = feature_processor.Tokenize("พระบาทสมเด็จพระปรมิ");
   ASSERT_EQ(tokens,
             // clang-format off
@@ -629,14 +750,17 @@
                                 Token("มิ", 17, 19)}));
   // clang-format on
 }
+#endif
 
+#ifdef LIBTEXTCLASSIFIER_TEST_ICU
 TEST(FeatureProcessorTest, ICUTokenizeWithWhitespaces) {
-  FeatureProcessorOptions options;
-  options.set_tokenization_type(
-      libtextclassifier::FeatureProcessorOptions::ICU);
-  options.set_icu_preserve_whitespace_tokens(true);
+  FeatureProcessorOptionsT options;
+  options.tokenization_type = FeatureProcessorOptions_::TokenizationType_ICU;
+  options.icu_preserve_whitespace_tokens = true;
 
-  TestingFeatureProcessor feature_processor(options);
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
   std::vector<Token> tokens =
       feature_processor.Tokenize("พระบาท สมเด็จ พระ ปร มิ");
   ASSERT_EQ(tokens,
@@ -652,36 +776,55 @@
                                 Token("มิ", 21, 23)}));
   // clang-format on
 }
+#endif
 
+#ifdef LIBTEXTCLASSIFIER_TEST_ICU
 TEST(FeatureProcessorTest, MixedTokenize) {
-  FeatureProcessorOptions options;
-  options.set_tokenization_type(
-      libtextclassifier::FeatureProcessorOptions::MIXED);
+  FeatureProcessorOptionsT options;
+  options.tokenization_type = FeatureProcessorOptions_::TokenizationType_MIXED;
 
-  TokenizationCodepointRange* config =
-      options.add_tokenization_codepoint_config();
-  config->set_start(32);
-  config->set_end(33);
-  config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
+  options.tokenization_codepoint_config.emplace_back(
+      new TokenizationCodepointRangeT());
+  auto& config = options.tokenization_codepoint_config.back();
+  config->start = 32;
+  config->end = 33;
+  config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
 
-  FeatureProcessorOptions::CodepointRange* range;
-  range = options.add_internal_tokenizer_codepoint_ranges();
-  range->set_start(0);
-  range->set_end(128);
+  {
+    options.internal_tokenizer_codepoint_ranges.emplace_back(
+        new FeatureProcessorOptions_::CodepointRangeT());
+    auto& range = options.internal_tokenizer_codepoint_ranges.back();
+    range->start = 0;
+    range->end = 128;
+  }
 
-  range = options.add_internal_tokenizer_codepoint_ranges();
-  range->set_start(128);
-  range->set_end(256);
+  {
+    options.internal_tokenizer_codepoint_ranges.emplace_back(
+        new FeatureProcessorOptions_::CodepointRangeT());
+    auto& range = options.internal_tokenizer_codepoint_ranges.back();
+    range->start = 128;
+    range->end = 256;
+  }
 
-  range = options.add_internal_tokenizer_codepoint_ranges();
-  range->set_start(256);
-  range->set_end(384);
+  {
+    options.internal_tokenizer_codepoint_ranges.emplace_back(
+        new FeatureProcessorOptions_::CodepointRangeT());
+    auto& range = options.internal_tokenizer_codepoint_ranges.back();
+    range->start = 256;
+    range->end = 384;
+  }
 
-  range = options.add_internal_tokenizer_codepoint_ranges();
-  range->set_start(384);
-  range->set_end(592);
+  {
+    options.internal_tokenizer_codepoint_ranges.emplace_back(
+        new FeatureProcessorOptions_::CodepointRangeT());
+    auto& range = options.internal_tokenizer_codepoint_ranges.back();
+    range->start = 384;
+    range->end = 592;
+  }
 
-  TestingFeatureProcessor feature_processor(options);
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
   std::vector<Token> tokens = feature_processor.Tokenize(
       "こんにちはJapanese-ląnguagę text 世界 http://www.google.com/");
   ASSERT_EQ(tokens,
@@ -693,15 +836,18 @@
                                 Token("http://www.google.com/", 31, 53)}));
   // clang-format on
 }
+#endif
 
 TEST(FeatureProcessorTest, IgnoredSpanBoundaryCodepoints) {
-  FeatureProcessorOptions options;
-  options.add_ignored_span_boundary_codepoints('.');
-  options.add_ignored_span_boundary_codepoints(',');
-  options.add_ignored_span_boundary_codepoints('[');
-  options.add_ignored_span_boundary_codepoints(']');
+  FeatureProcessorOptionsT options;
+  options.ignored_span_boundary_codepoints.push_back('.');
+  options.ignored_span_boundary_codepoints.push_back(',');
+  options.ignored_span_boundary_codepoints.push_back('[');
+  options.ignored_span_boundary_codepoints.push_back(']');
 
-  TestingFeatureProcessor feature_processor(options);
+  flatbuffers::DetachedBuffer options_fb = PackFeatureProcessorOptions(options);
+  TestingFeatureProcessor feature_processor(
+      flatbuffers::GetRoot<FeatureProcessorOptions>(options_fb.data()));
 
   const std::string text1_utf8 = "ěščř";
   const UnicodeText text1 = UTF8ToUnicodeText(text1_utf8, /*do_copy=*/false);
@@ -834,4 +980,4 @@
 }
 
 }  // namespace
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/jni.lds b/jni.lds
index 75d5bc5..171cc0e 100644
--- a/jni.lds
+++ b/jni.lds
@@ -1,7 +1,7 @@
-{
-  # Export symbols that correspond to our JNIEXPORTed functions.
+VERS_1.0 {
+  # Export JNI symbols.
   global:
-    Java_android_view_textclassifier_*;
+    Java_*;
 
   # Hide everything else.
   local:
diff --git a/lang_id/custom-tokenizer.cc b/lang_id/custom-tokenizer.cc
deleted file mode 100644
index 7e30cc7..0000000
--- a/lang_id/custom-tokenizer.cc
+++ /dev/null
@@ -1,109 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "lang_id/custom-tokenizer.h"
-
-#include <ctype.h>
-
-#include <string>
-
-#include "util/strings/utf8.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-namespace {
-inline bool IsTokenSeparator(int num_bytes, const char *curr) {
-  if (num_bytes != 1) {
-    return false;
-  }
-  return !isalpha(*curr);
-}
-}  // namespace
-
-const char *GetSafeEndOfString(const char *data, size_t size) {
-  const char *const hard_end = data + size;
-  const char *curr = data;
-  while (curr < hard_end) {
-    int num_bytes = GetNumBytesForUTF8Char(curr);
-    if (num_bytes == 0) {
-      break;
-    }
-    const char *new_curr = curr + num_bytes;
-    if (new_curr > hard_end) {
-      return curr;
-    }
-    curr = new_curr;
-  }
-  return curr;
-}
-
-void TokenizeTextForLangId(const std::string &text, LightSentence *sentence) {
-  const char *const start = text.data();
-  const char *curr = start;
-  const char *end = GetSafeEndOfString(start, text.size());
-
-  // Corner case: empty safe part of the text.
-  if (curr >= end) {
-    return;
-  }
-
-  // Number of bytes for UTF8 character starting at *curr.  Note: the loop below
-  // is guaranteed to terminate because in each iteration, we move curr by at
-  // least num_bytes, and num_bytes is guaranteed to be > 0.
-  int num_bytes = GetNumBytesForNonZeroUTF8Char(curr);
-  while (curr < end) {
-    // Jump over consecutive token separators.
-    while (IsTokenSeparator(num_bytes, curr)) {
-      curr += num_bytes;
-      if (curr >= end) {
-        return;
-      }
-      num_bytes = GetNumBytesForNonZeroUTF8Char(curr);
-    }
-
-    // If control reaches this point, we are at beginning of a non-empty token.
-    std::string *word = sentence->add_word();
-
-    // Add special token-start character.
-    word->push_back('^');
-
-    // Add UTF8 characters to word, until we hit the end of the safe text or a
-    // token separator.
-    while (true) {
-      word->append(curr, num_bytes);
-      curr += num_bytes;
-      if (curr >= end) {
-        break;
-      }
-      num_bytes = GetNumBytesForNonZeroUTF8Char(curr);
-      if (IsTokenSeparator(num_bytes, curr)) {
-        curr += num_bytes;
-        num_bytes = GetNumBytesForNonZeroUTF8Char(curr);
-        break;
-      }
-    }
-    word->push_back('$');
-
-    // Note: we intentionally do not token.set_start()/end(), as those fields
-    // are not used by the langid model.
-  }
-}
-
-}  // namespace lang_id
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/lang_id/custom-tokenizer.h b/lang_id/custom-tokenizer.h
deleted file mode 100644
index c9c291c..0000000
--- a/lang_id/custom-tokenizer.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_CUSTOM_TOKENIZER_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_CUSTOM_TOKENIZER_H_
-
-#include <cstddef>
-#include <string>
-
-#include "lang_id/light-sentence.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Perform custom tokenization of text.  Customized for the language
-// identification project.  Currently (Sep 15, 2016) we tokenize on space,
-// newline, and tab, ignore all empty tokens, and (for each of the remaining
-// tokens) prepend "^" (special token begin marker) and append "$" (special
-// token end marker).
-//
-// Tokens are stored into the words of the LightSentence *sentence.
-void TokenizeTextForLangId(const std::string &text, LightSentence *sentence);
-
-// Returns a pointer "end" inside [data, data + size) such that the prefix from
-// [data, end) is the largest one that does not contain '\0' and offers the
-// following guarantee: if one starts with
-//
-//   curr = text.data()
-//
-// and keeps executing
-//
-//   curr += utils::GetNumBytesForNonZeroUTF8Char(curr)
-//
-// one would eventually reach curr == end (the pointer returned by this
-// function) without accessing data outside the std::string.  This guards
-// against scenarios like a broken UTF-8 string which has only e.g., the first 2
-// bytes from a 3-byte UTF8 sequence.
-const char *GetSafeEndOfString(const char *data, size_t size);
-
-static inline const char *GetSafeEndOfString(const std::string &text) {
-  return GetSafeEndOfString(text.data(), text.size());
-}
-
-}  // namespace lang_id
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_LANG_ID_CUSTOM_TOKENIZER_H_
diff --git a/lang_id/lang-id-brain-interface.h b/lang_id/lang-id-brain-interface.h
deleted file mode 100644
index ce79497..0000000
--- a/lang_id/lang-id-brain-interface.h
+++ /dev/null
@@ -1,88 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_LANG_ID_BRAIN_INTERFACE_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_LANG_ID_BRAIN_INTERFACE_H_
-
-#include <string>
-#include <vector>
-
-#include "common/embedding-feature-extractor.h"
-#include "common/feature-extractor.h"
-#include "common/task-context.h"
-#include "common/workspace.h"
-#include "lang_id/light-sentence-features.h"
-#include "lang_id/light-sentence.h"
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Specialization of EmbeddingFeatureExtractor that extracts from LightSentence.
-class LangIdEmbeddingFeatureExtractor
-    : public EmbeddingFeatureExtractor<LightSentenceExtractor, LightSentence> {
- public:
-  LangIdEmbeddingFeatureExtractor() {}
-  const std::string ArgPrefix() const override { return "language_identifier"; }
-
-  TC_DISALLOW_COPY_AND_ASSIGN(LangIdEmbeddingFeatureExtractor);
-};
-
-// Handles sentence -> numeric_features and numeric_prediction -> language
-// conversions.
-class LangIdBrainInterface {
- public:
-  LangIdBrainInterface() {}
-
-  // Initializes resources and parameters.
-  bool Init(TaskContext *context) {
-    if (!feature_extractor_.Init(context)) {
-      return false;
-    }
-    feature_extractor_.RequestWorkspaces(&workspace_registry_);
-    return true;
-  }
-
-  // Extract features from sentence.  On return, FeatureVector features[i]
-  // contains the features for the embedding space #i.
-  void GetFeatures(LightSentence *sentence,
-                   std::vector<FeatureVector> *features) const {
-    WorkspaceSet workspace;
-    workspace.Reset(workspace_registry_);
-    feature_extractor_.Preprocess(&workspace, sentence);
-    return feature_extractor_.ExtractFeatures(workspace, *sentence, features);
-  }
-
-  int NumEmbeddings() const {
-    return feature_extractor_.NumEmbeddings();
-  }
-
- private:
-  // Typed feature extractor for embeddings.
-  LangIdEmbeddingFeatureExtractor feature_extractor_;
-
-  // The registry of shared workspaces in the feature extractor.
-  WorkspaceRegistry workspace_registry_;
-
-  TC_DISALLOW_COPY_AND_ASSIGN(LangIdBrainInterface);
-};
-
-}  // namespace lang_id
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_LANG_ID_LANG_ID_BRAIN_INTERFACE_H_
diff --git a/lang_id/lang-id.cc b/lang_id/lang-id.cc
deleted file mode 100644
index 8383d33..0000000
--- a/lang_id/lang-id.cc
+++ /dev/null
@@ -1,402 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "lang_id/lang-id.h"
-
-#include <stdio.h>
-
-#include <algorithm>
-#include <limits>
-#include <memory>
-#include <string>
-#include <vector>
-
-#include "common/algorithm.h"
-#include "common/embedding-network-params-from-proto.h"
-#include "common/embedding-network.pb.h"
-#include "common/embedding-network.h"
-#include "common/feature-extractor.h"
-#include "common/file-utils.h"
-#include "common/list-of-strings.pb.h"
-#include "common/memory_image/in-memory-model-data.h"
-#include "common/mmap.h"
-#include "common/softmax.h"
-#include "common/task-context.h"
-#include "lang_id/custom-tokenizer.h"
-#include "lang_id/lang-id-brain-interface.h"
-#include "lang_id/language-identifier-features.h"
-#include "lang_id/light-sentence-features.h"
-#include "lang_id/light-sentence.h"
-#include "lang_id/relevant-script-feature.h"
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-
-using ::libtextclassifier::nlp_core::file_utils::ParseProtoFromMemory;
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-namespace {
-// Default value for the probability threshold; see comments for
-// LangId::SetProbabilityThreshold().
-static const float kDefaultProbabilityThreshold = 0.50;
-
-// Default value for min text size below which our model can't provide a
-// meaningful prediction.
-static const int kDefaultMinTextSizeInBytes = 20;
-
-// Initial value for the default language for LangId::FindLanguage().  The
-// default language can be changed (for an individual LangId object) using
-// LangId::SetDefaultLanguage().
-static const char kInitialDefaultLanguage[] = "";
-
-// Returns total number of bytes of the words from sentence, without the ^
-// (start-of-word) and $ (end-of-word) markers.  Note: "real text" means that
-// this ignores whitespace and punctuation characters from the original text.
-int GetRealTextSize(const LightSentence &sentence) {
-  int total = 0;
-  for (int i = 0; i < sentence.num_words(); ++i) {
-    TC_DCHECK(!sentence.word(i).empty());
-    TC_DCHECK_EQ('^', sentence.word(i).front());
-    TC_DCHECK_EQ('$', sentence.word(i).back());
-    total += sentence.word(i).size() - 2;
-  }
-  return total;
-}
-
-}  // namespace
-
-// Class that performs all work behind LangId.
-class LangIdImpl {
- public:
-  explicit LangIdImpl(const std::string &filename) {
-    // Using mmap as a fast way to read the model bytes.
-    ScopedMmap scoped_mmap(filename);
-    MmapHandle mmap_handle = scoped_mmap.handle();
-    if (!mmap_handle.ok()) {
-      TC_LOG(ERROR) << "Unable to read model bytes.";
-      return;
-    }
-
-    Initialize(mmap_handle.to_stringpiece());
-  }
-
-  explicit LangIdImpl(int fd) {
-    // Using mmap as a fast way to read the model bytes.
-    ScopedMmap scoped_mmap(fd);
-    MmapHandle mmap_handle = scoped_mmap.handle();
-    if (!mmap_handle.ok()) {
-      TC_LOG(ERROR) << "Unable to read model bytes.";
-      return;
-    }
-
-    Initialize(mmap_handle.to_stringpiece());
-  }
-
-  LangIdImpl(const char *ptr, size_t length) {
-    Initialize(StringPiece(ptr, length));
-  }
-
-  void Initialize(StringPiece model_bytes) {
-    // Will set valid_ to true only on successful initialization.
-    valid_ = false;
-
-    // Make sure all relevant features are registered:
-    ContinuousBagOfNgramsFunction::RegisterClass();
-    RelevantScriptFeature::RegisterClass();
-
-    // NOTE(salcianu): code below relies on the fact that the current features
-    // do not rely on data from a TaskInput.  Otherwise, one would have to use
-    // the more complex model registration mechanism, which requires more code.
-    InMemoryModelData model_data(model_bytes);
-    TaskContext context;
-    if (!model_data.GetTaskSpec(context.mutable_spec())) {
-      TC_LOG(ERROR) << "Unable to get model TaskSpec";
-      return;
-    }
-
-    if (!ParseNetworkParams(model_data, &context)) {
-      return;
-    }
-    if (!ParseListOfKnownLanguages(model_data, &context)) {
-      return;
-    }
-
-    network_.reset(new EmbeddingNetwork(network_params_.get()));
-    if (!network_->is_valid()) {
-      return;
-    }
-
-    probability_threshold_ =
-        context.Get("reliability_thresh", kDefaultProbabilityThreshold);
-    min_text_size_in_bytes_ =
-        context.Get("min_text_size_in_bytes", kDefaultMinTextSizeInBytes);
-    version_ = context.Get("version", 0);
-
-    if (!lang_id_brain_interface_.Init(&context)) {
-      return;
-    }
-    valid_ = true;
-  }
-
-  void SetProbabilityThreshold(float threshold) {
-    probability_threshold_ = threshold;
-  }
-
-  void SetDefaultLanguage(const std::string &lang) { default_language_ = lang; }
-
-  std::string FindLanguage(const std::string &text) const {
-    std::vector<float> scores = ScoreLanguages(text);
-    if (scores.empty()) {
-      return default_language_;
-    }
-
-    // Softmax label with max score.
-    int label = GetArgMax(scores);
-    float probability = scores[label];
-    if (probability < probability_threshold_) {
-      return default_language_;
-    }
-    return GetLanguageForSoftmaxLabel(label);
-  }
-
-  std::vector<std::pair<std::string, float>> FindLanguages(
-      const std::string &text) const {
-    std::vector<float> scores = ScoreLanguages(text);
-
-    std::vector<std::pair<std::string, float>> result;
-    for (int i = 0; i < scores.size(); i++) {
-      result.push_back({GetLanguageForSoftmaxLabel(i), scores[i]});
-    }
-
-    // To avoid crashing clients that always expect at least one predicted
-    // language, we promised (see doc for this method) that the result always
-    // contains at least one element.
-    if (result.empty()) {
-      // We use a tiny probability, such that any client that uses a meaningful
-      // probability threshold ignores this prediction.  We don't use 0.0f, to
-      // avoid crashing clients that normalize the probabilities we return here.
-      result.push_back({default_language_, 0.001f});
-    }
-    return result;
-  }
-
-  std::vector<float> ScoreLanguages(const std::string &text) const {
-    if (!is_valid()) {
-      return {};
-    }
-
-    // Create a Sentence storing the input text.
-    LightSentence sentence;
-    TokenizeTextForLangId(text, &sentence);
-
-    if (GetRealTextSize(sentence) < min_text_size_in_bytes_) {
-      return {};
-    }
-
-    // TODO(salcianu): reuse vector<FeatureVector>.
-    std::vector<FeatureVector> features(
-        lang_id_brain_interface_.NumEmbeddings());
-    lang_id_brain_interface_.GetFeatures(&sentence, &features);
-
-    // Predict language.
-    EmbeddingNetwork::Vector scores;
-    network_->ComputeFinalScores(features, &scores);
-
-    return ComputeSoftmax(scores);
-  }
-
-  bool is_valid() const { return valid_; }
-
-  int version() const { return version_; }
-
- private:
-  // Returns name of the (in-memory) file for the indicated TaskInput from
-  // context.
-  static std::string GetInMemoryFileNameForTaskInput(
-      const std::string &input_name, TaskContext *context) {
-    TaskInput *task_input = context->GetInput(input_name);
-    if (task_input->part_size() != 1) {
-      TC_LOG(ERROR) << "TaskInput " << input_name << " has "
-                    << task_input->part_size() << " parts";
-      return "";
-    }
-    return task_input->part(0).file_pattern();
-  }
-
-  bool ParseNetworkParams(const InMemoryModelData &model_data,
-                          TaskContext *context) {
-    const std::string input_name = "language-identifier-network";
-    const std::string input_file_name =
-        GetInMemoryFileNameForTaskInput(input_name, context);
-    if (input_file_name.empty()) {
-      TC_LOG(ERROR) << "No input file name for TaskInput " << input_name;
-      return false;
-    }
-    StringPiece bytes = model_data.GetBytesForInputFile(input_file_name);
-    if (bytes.data() == nullptr) {
-      TC_LOG(ERROR) << "Unable to get bytes for TaskInput " << input_name;
-      return false;
-    }
-    std::unique_ptr<EmbeddingNetworkProto> proto(new EmbeddingNetworkProto());
-    if (!ParseProtoFromMemory(bytes, proto.get())) {
-      TC_LOG(ERROR) << "Unable to parse EmbeddingNetworkProto";
-      return false;
-    }
-    network_params_.reset(
-        new EmbeddingNetworkParamsFromProto(std::move(proto)));
-    if (!network_params_->is_valid()) {
-      TC_LOG(ERROR) << "EmbeddingNetworkParamsFromProto not valid";
-      return false;
-    }
-    return true;
-  }
-
-  // Parses dictionary with known languages (i.e., field languages_) from a
-  // TaskInput of context.  Note: that TaskInput should be a ListOfStrings proto
-  // with a single element, the serialized form of a ListOfStrings.
-  //
-  bool ParseListOfKnownLanguages(const InMemoryModelData &model_data,
-                                 TaskContext *context) {
-    const std::string input_name = "language-name-id-map";
-    const std::string input_file_name =
-        GetInMemoryFileNameForTaskInput(input_name, context);
-    if (input_file_name.empty()) {
-      TC_LOG(ERROR) << "No input file name for TaskInput " << input_name;
-      return false;
-    }
-    StringPiece bytes = model_data.GetBytesForInputFile(input_file_name);
-    if (bytes.data() == nullptr) {
-      TC_LOG(ERROR) << "Unable to get bytes for TaskInput " << input_name;
-      return false;
-    }
-    ListOfStrings records;
-    if (!ParseProtoFromMemory(bytes, &records)) {
-      TC_LOG(ERROR) << "Unable to parse ListOfStrings from TaskInput "
-                    << input_name;
-      return false;
-    }
-    if (records.element_size() != 1) {
-      TC_LOG(ERROR) << "Wrong number of records in TaskInput " << input_name
-                    << " : " << records.element_size();
-      return false;
-    }
-    if (!ParseProtoFromMemory(std::string(records.element(0)), &languages_)) {
-      TC_LOG(ERROR) << "Unable to parse dictionary with known languages";
-      return false;
-    }
-    return true;
-  }
-
-  // Returns language code for a softmax label.  See comments for languages_
-  // field.  If label is out of range, returns default_language_.
-  std::string GetLanguageForSoftmaxLabel(int label) const {
-    if ((label >= 0) && (label < languages_.element_size())) {
-      return languages_.element(label);
-    } else {
-      TC_LOG(ERROR) << "Softmax label " << label << " outside range [0, "
-                    << languages_.element_size() << ")";
-      return default_language_;
-    }
-  }
-
-  LangIdBrainInterface lang_id_brain_interface_;
-
-  // Parameters for the neural network network_ (see below).
-  std::unique_ptr<EmbeddingNetworkParamsFromProto> network_params_;
-
-  // Neural network to use for scoring.
-  std::unique_ptr<EmbeddingNetwork> network_;
-
-  // True if this object is ready to perform language predictions.
-  bool valid_;
-
-  // Only predictions with a probability (confidence) above this threshold are
-  // reported.  Otherwise, we report default_language_.
-  float probability_threshold_ = kDefaultProbabilityThreshold;
-
-  // Min size of the input text for our predictions to be meaningful.  Below
-  // this threshold, the underlying model may report a wrong language and a high
-  // confidence score.
-  int min_text_size_in_bytes_ = kDefaultMinTextSizeInBytes;
-
-  // Version of the model.
-  int version_ = -1;
-
-  // Known languages: softmax label i (an integer) means languages_.element(i)
-  // (something like "en", "fr", "ru", etc).
-  ListOfStrings languages_;
-
-  // Language code to return in case of errors.
-  std::string default_language_ = kInitialDefaultLanguage;
-
-  TC_DISALLOW_COPY_AND_ASSIGN(LangIdImpl);
-};
-
-LangId::LangId(const std::string &filename) : pimpl_(new LangIdImpl(filename)) {
-  if (!pimpl_->is_valid()) {
-    TC_LOG(ERROR) << "Unable to construct a valid LangId based "
-                  << "on the data from " << filename
-                  << "; nothing should crash, but "
-                  << "accuracy will be bad.";
-  }
-}
-
-LangId::LangId(int fd) : pimpl_(new LangIdImpl(fd)) {
-  if (!pimpl_->is_valid()) {
-    TC_LOG(ERROR) << "Unable to construct a valid LangId based "
-                  << "on the data from descriptor " << fd
-                  << "; nothing should crash, "
-                  << "but accuracy will be bad.";
-  }
-}
-
-LangId::LangId(const char *ptr, size_t length)
-    : pimpl_(new LangIdImpl(ptr, length)) {
-  if (!pimpl_->is_valid()) {
-    TC_LOG(ERROR) << "Unable to construct a valid LangId based "
-                  << "on the memory region; nothing should crash, "
-                  << "but accuracy will be bad.";
-  }
-}
-
-LangId::~LangId() = default;
-
-void LangId::SetProbabilityThreshold(float threshold) {
-  pimpl_->SetProbabilityThreshold(threshold);
-}
-
-void LangId::SetDefaultLanguage(const std::string &lang) {
-  pimpl_->SetDefaultLanguage(lang);
-}
-
-std::string LangId::FindLanguage(const std::string &text) const {
-  return pimpl_->FindLanguage(text);
-}
-
-std::vector<std::pair<std::string, float>> LangId::FindLanguages(
-    const std::string &text) const {
-  return pimpl_->FindLanguages(text);
-}
-
-bool LangId::is_valid() const { return pimpl_->is_valid(); }
-
-int LangId::version() const { return pimpl_->version(); }
-
-}  // namespace lang_id
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/lang_id/lang-id.h b/lang_id/lang-id.h
deleted file mode 100644
index 7653dde..0000000
--- a/lang_id/lang-id.h
+++ /dev/null
@@ -1,112 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_LANG_ID_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_LANG_ID_H_
-
-// Clients who want to perform language identification should use this header.
-//
-// Note for lang id implementors: keep this header as linght as possible.  E.g.,
-// any macro defined here (or in a transitively #included file) is a potential
-// name conflict with our clients.
-
-#include <memory>
-#include <string>
-#include <vector>
-
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Forward-declaration of the class that performs all underlying work.
-class LangIdImpl;
-
-// Class for detecting the language of a document.
-//
-// NOTE: this class is thread-unsafe.
-class LangId {
- public:
-  // Constructs a LangId object, loading an EmbeddingNetworkProto model from the
-  // indicated file.
-  //
-  // Note: we don't crash if we detect a problem at construction time (e.g.,
-  // file doesn't exist, or its content is corrupted).  Instead, we mark the
-  // newly-constructed object as invalid; clients can invoke FindLanguage() on
-  // an invalid object: nothing crashes, but accuracy will be bad.
-  explicit LangId(const std::string &filename);
-
-  // Same as above but uses a file descriptor.
-  explicit LangId(int fd);
-
-  // Same as above but uses already mapped memory region
-  explicit LangId(const char *ptr, size_t length);
-
-  virtual ~LangId();
-
-  // Sets probability threshold for predictions.  If our likeliest prediction is
-  // below this threshold, we report the default language (see
-  // SetDefaultLanguage()).  Othewise, we report the likelist language.
-  //
-  // By default (if this method is not called) we use the probability threshold
-  // stored in the model, as the task parameter "reliability_thresh".  If that
-  // task parameter is not specified, we use 0.5.  A client can use this method
-  // to get a different precision / recall trade-off.  The higher the threshold,
-  // the higher the precision and lower the recall rate.
-  void SetProbabilityThreshold(float threshold);
-
-  // Sets default language to report if errors prevent running the real
-  // inference code or if prediction confidence is too small.
-  void SetDefaultLanguage(const std::string &lang);
-
-  // Returns language code for the most likely language that text is written in.
-  // Note: if this LangId object is not valid (see
-  // is_valid()), this method returns the default language specified via
-  // SetDefaultLanguage() or (if that method was never invoked), the empty
-  // std::string.
-  std::string FindLanguage(const std::string &text) const;
-
-  // Returns a vector of language codes along with the probability for each
-  // language.  The result contains at least one element.  The sum of
-  // probabilities may be less than 1.0.
-  std::vector<std::pair<std::string, float>> FindLanguages(
-      const std::string &text) const;
-
-  // Returns true if this object has been correctly initialized and is ready to
-  // perform predictions.  For more info, see doc for LangId
-  // constructor above.
-  bool is_valid() const;
-
-  // Returns version number for the model.
-  int version() const;
-
- private:
-  // Returns a vector of probabilities of languages of the text.
-  std::vector<float> ScoreLanguages(const std::string &text) const;
-
-  // Pimpl ("pointer to implementation") pattern, to hide all internals from our
-  // clients.
-  std::unique_ptr<LangIdImpl> pimpl_;
-
-  TC_DISALLOW_COPY_AND_ASSIGN(LangId);
-};
-
-}  // namespace lang_id
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_LANG_ID_LANG_ID_H_
diff --git a/lang_id/lang-id_test.cc b/lang_id/lang-id_test.cc
deleted file mode 100644
index 2f8aedd..0000000
--- a/lang_id/lang-id_test.cc
+++ /dev/null
@@ -1,152 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "lang_id/lang-id.h"
-
-#include <memory>
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "util/base/logging.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-namespace {
-
-std::string GetModelPath() {
-  return TEST_DATA_DIR "langid.model";
-}
-
-// Creates a LangId with default model.  Passes ownership to
-// the caller.
-LangId *CreateLanguageDetector() { return new LangId(GetModelPath()); }
-
-}  // namespace
-
-TEST(LangIdTest, Normal) {
-  std::unique_ptr<LangId> lang_id(CreateLanguageDetector());
-
-  EXPECT_EQ("en", lang_id->FindLanguage("This text is written in English."));
-  EXPECT_EQ("en",
-            lang_id->FindLanguage("This text   is written in   English.  "));
-  EXPECT_EQ("en",
-            lang_id->FindLanguage("  This text is written in English.  "));
-  EXPECT_EQ("fr", lang_id->FindLanguage("Vive la France!  Vive la France!"));
-  EXPECT_EQ("ro", lang_id->FindLanguage("Sunt foarte foarte foarte fericit!"));
-}
-
-// Test that for very small queries, we return the default language and a low
-// confidence score.
-TEST(LangIdTest, SuperSmallQueries) {
-  std::unique_ptr<LangId> lang_id(CreateLanguageDetector());
-
-  // Use a default language different from any real language: to be sure the
-  // result is the default language, not a language that happens to be the
-  // default language.
-  const std::string kDefaultLanguage = "dflt-lng";
-  lang_id->SetDefaultLanguage(kDefaultLanguage);
-
-  // Test the simple FindLanguage() method: that method returns a single
-  // language.
-  EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage("y"));
-  EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage("j"));
-  EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage("l"));
-  EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage("w"));
-  EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage("z"));
-  EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage("zulu"));
-
-  // Test the more complex FindLanguages() method: that method returns a vector
-  // of (language, confidence_score) pairs.
-  std::vector<std::pair<std::string, float>> languages;
-  languages = lang_id->FindLanguages("y");
-  EXPECT_EQ(1, languages.size());
-  EXPECT_EQ(kDefaultLanguage, languages[0].first);
-  EXPECT_GT(0.01f, languages[0].second);
-
-  languages = lang_id->FindLanguages("Todoist");
-  EXPECT_EQ(1, languages.size());
-  EXPECT_EQ(kDefaultLanguage, languages[0].first);
-  EXPECT_GT(0.01f, languages[0].second);
-
-  // A few tests with a default language that is a real language code.
-  const std::string kJapanese = "ja";
-  lang_id->SetDefaultLanguage(kJapanese);
-  EXPECT_EQ(kJapanese, lang_id->FindLanguage("y"));
-  EXPECT_EQ(kJapanese, lang_id->FindLanguage("j"));
-  EXPECT_EQ(kJapanese, lang_id->FindLanguage("l"));
-  languages = lang_id->FindLanguages("y");
-  EXPECT_EQ(1, languages.size());
-  EXPECT_EQ(kJapanese, languages[0].first);
-  EXPECT_GT(0.01f, languages[0].second);
-
-  // Make sure the min text size limit is applied to the number of real
-  // characters (e.g., without spaces and punctuation chars, which don't
-  // influence language identification).
-  const std::string kWhitespaces = "   \t   \n   \t\t\t\n    \t";
-  const std::string kPunctuation = "... ?!!--- -%%^...-";
-  std::string still_small_string = kWhitespaces + "y" + kWhitespaces +
-                                   kPunctuation + kWhitespaces + kPunctuation +
-                                   kPunctuation;
-  EXPECT_LE(100, still_small_string.size());
-  lang_id->SetDefaultLanguage(kDefaultLanguage);
-  EXPECT_EQ(kDefaultLanguage, lang_id->FindLanguage(still_small_string));
-  languages = lang_id->FindLanguages(still_small_string);
-  EXPECT_EQ(1, languages.size());
-  EXPECT_EQ(kDefaultLanguage, languages[0].first);
-  EXPECT_GT(0.01f, languages[0].second);
-}
-
-namespace {
-void CheckPredictionForGibberishStrings(const std::string &default_language) {
-  static const char *const kGibberish[] = {
-    "",
-    " ",
-    "       ",
-    "  ___  ",
-    "123 456 789",
-    "><> (-_-) <><",
-    nullptr,
-  };
-
-  std::unique_ptr<LangId> lang_id(CreateLanguageDetector());
-  TC_LOG(INFO) << "Default language: " << default_language;
-  lang_id->SetDefaultLanguage(default_language);
-  for (int i = 0; true; ++i) {
-    const char *gibberish = kGibberish[i];
-    if (gibberish == nullptr) {
-      break;
-    }
-    const std::string predicted_language = lang_id->FindLanguage(gibberish);
-    TC_LOG(INFO) << "Predicted " << predicted_language << " for \"" << gibberish
-                 << "\"";
-    EXPECT_EQ(default_language, predicted_language);
-  }
-}
-}  // namespace
-
-TEST(LangIdTest, CornerCases) {
-  CheckPredictionForGibberishStrings("en");
-  CheckPredictionForGibberishStrings("ro");
-  CheckPredictionForGibberishStrings("fr");
-}
-
-}  // namespace lang_id
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/lang_id/language-identifier-features.cc b/lang_id/language-identifier-features.cc
deleted file mode 100644
index 2e3912e..0000000
--- a/lang_id/language-identifier-features.cc
+++ /dev/null
@@ -1,141 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "lang_id/language-identifier-features.h"
-
-#include <utility>
-#include <vector>
-
-#include "common/feature-extractor.h"
-#include "common/feature-types.h"
-#include "common/task-context.h"
-#include "util/hash/hash.h"
-#include "util/strings/utf8.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-bool ContinuousBagOfNgramsFunction::Setup(TaskContext *context) {
-  // Parameters in the feature function descriptor.
-  ngram_id_dimension_ = GetIntParameter("id_dim", 10000);
-  ngram_size_ = GetIntParameter("size", 3);
-
-  counts_.assign(ngram_id_dimension_, 0);
-  return true;
-}
-
-bool ContinuousBagOfNgramsFunction::Init(TaskContext *context) {
-  set_feature_type(new NumericFeatureType(name(), ngram_id_dimension_));
-  return true;
-}
-
-int ContinuousBagOfNgramsFunction::ComputeNgramCounts(
-    const LightSentence &sentence) const {
-  // Invariant 1: counts_.size() == ngram_id_dimension_.  Holds at the end of
-  // the constructor.  After that, no method changes counts_.size().
-  TC_DCHECK_EQ(counts_.size(), ngram_id_dimension_);
-
-  // Invariant 2: the vector non_zero_count_indices_ is empty.  The vector
-  // non_zero_count_indices_ is empty at construction time and gets emptied at
-  // the end of each call to Evaluate().  Hence, this invariant holds at the
-  // beginning of each run of Evaluate(), where the only call to this code takes
-  // place.
-  TC_DCHECK(non_zero_count_indices_.empty());
-
-  int total_count = 0;
-
-  for (int i = 0; i < sentence.num_words(); ++i) {
-    const std::string &word = sentence.word(i);
-    const char *const word_end = word.data() + word.size();
-
-    // Set ngram_start at the start of the current token (word).
-    const char *ngram_start = word.data();
-
-    // Set ngram_end ngram_size UTF8 characters after ngram_start.  Note: each
-    // UTF8 character contains between 1 and 4 bytes.
-    const char *ngram_end = ngram_start;
-    int num_utf8_chars = 0;
-    do {
-      ngram_end += GetNumBytesForNonZeroUTF8Char(ngram_end);
-      num_utf8_chars++;
-    } while ((num_utf8_chars < ngram_size_) && (ngram_end < word_end));
-
-    if (num_utf8_chars < ngram_size_) {
-      // Current token is so small, it does not contain a single ngram of
-      // ngram_size UTF8 characters.  Not much we can do in this case ...
-      continue;
-    }
-
-    // At this point, [ngram_start, ngram_end) is the first ngram of ngram_size
-    // UTF8 characters from current token.
-    while (true) {
-      // Compute ngram_id: hash(ngram) % ngram_id_dimension
-      int ngram_id =
-          (Hash32WithDefaultSeed(ngram_start, ngram_end - ngram_start) %
-           ngram_id_dimension_);
-
-      // Use a reference to the actual count, such that we can both test whether
-      // the count was 0 and increment it without perfoming two lookups.
-      //
-      // Due to the way we compute ngram_id, 0 <= ngram_id < ngram_id_dimension.
-      // Hence, by Invariant 1 (above), the access counts_[ngram_id] is safe.
-      int &ref_to_count_for_ngram = counts_[ngram_id];
-      if (ref_to_count_for_ngram == 0) {
-        non_zero_count_indices_.push_back(ngram_id);
-      }
-      ref_to_count_for_ngram++;
-      total_count++;
-      if (ngram_end >= word_end) {
-        break;
-      }
-
-      // Advance both ngram_start and ngram_end by one UTF8 character.  This
-      // way, the number of UTF8 characters between them remains constant
-      // (ngram_size).
-      ngram_start += GetNumBytesForNonZeroUTF8Char(ngram_start);
-      ngram_end += GetNumBytesForNonZeroUTF8Char(ngram_end);
-    }
-  }  // end of loop over tokens.
-
-  return total_count;
-}
-
-void ContinuousBagOfNgramsFunction::Evaluate(const WorkspaceSet &workspaces,
-                                             const LightSentence &sentence,
-                                             FeatureVector *result) const {
-  // Find the char ngram counts.
-  int total_count = ComputeNgramCounts(sentence);
-
-  // Populate the feature vector.
-  const float norm = static_cast<float>(total_count);
-
-  for (int ngram_id : non_zero_count_indices_) {
-    const float weight = counts_[ngram_id] / norm;
-    FloatFeatureValue value(ngram_id, weight);
-    result->add(feature_type(), value.discrete_value);
-
-    // Clear up counts_, for the next invocation of Evaluate().
-    counts_[ngram_id] = 0;
-  }
-
-  // Clear up non_zero_count_indices_, for the next invocation of Evaluate().
-  non_zero_count_indices_.clear();
-}
-
-}  // namespace lang_id
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/lang_id/language-identifier-features.h b/lang_id/language-identifier-features.h
deleted file mode 100644
index a4e3b3d..0000000
--- a/lang_id/language-identifier-features.h
+++ /dev/null
@@ -1,79 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_LANGUAGE_IDENTIFIER_FEATURES_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_LANGUAGE_IDENTIFIER_FEATURES_H_
-
-#include <string>
-
-#include "common/feature-extractor.h"
-#include "common/task-context.h"
-#include "common/workspace.h"
-#include "lang_id/light-sentence-features.h"
-#include "lang_id/light-sentence.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Class for computing continuous char ngram features.
-//
-// Feature function descriptor parameters:
-//   id_dim(int, 10000):
-//     The integer id of each char ngram is computed as follows:
-//     Hash32WithDefaultSeed(char ngram) % id_dim.
-//   size(int, 3):
-//     Only ngrams of this size will be extracted.
-//
-// NOTE: this class is not thread-safe.  TODO(salcianu): make it thread-safe.
-class ContinuousBagOfNgramsFunction : public LightSentenceFeature {
- public:
-  bool Setup(TaskContext *context) override;
-  bool Init(TaskContext *context) override;
-
-  // Appends the features computed from the sentence to the feature vector.
-  void Evaluate(const WorkspaceSet &workspaces, const LightSentence &sentence,
-                FeatureVector *result) const override;
-
-  TC_DEFINE_REGISTRATION_METHOD("continuous-bag-of-ngrams",
-                                ContinuousBagOfNgramsFunction);
-
- private:
-  // Auxiliary for Evaluate().  Fills counts_ and non_zero_count_indices_ (see
-  // below), and returns the total ngram count.
-  int ComputeNgramCounts(const LightSentence &sentence) const;
-
-  // counts_[i] is the count of all ngrams with id i.  Work data for Evaluate().
-  // NOTE: we declare this vector as a field, such that its underlying capacity
-  // stays allocated in between calls to Evaluate().
-  mutable std::vector<int> counts_;
-
-  // Indices of non-zero elements of counts_.  See comments for counts_.
-  mutable std::vector<int> non_zero_count_indices_;
-
-  // The integer id of each char ngram is computed as follows:
-  // Hash32WithDefaultSeed(char_ngram) % ngram_id_dimension_.
-  int ngram_id_dimension_;
-
-  // Only ngrams of size ngram_size_ will be extracted.
-  int ngram_size_;
-};
-
-}  // namespace lang_id
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_LANG_ID_LANGUAGE_IDENTIFIER_FEATURES_H_
diff --git a/lang_id/light-sentence-features.cc b/lang_id/light-sentence-features.cc
deleted file mode 100644
index aec6b81..0000000
--- a/lang_id/light-sentence-features.cc
+++ /dev/null
@@ -1,29 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "lang_id/light-sentence-features.h"
-
-#include "common/registry.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-
-// Registry for the features on whole light sentences.
-TC_DEFINE_CLASS_REGISTRY_NAME("light sentence feature function",
-                              lang_id::LightSentenceFeature);
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/lang_id/light-sentence-features.h b/lang_id/light-sentence-features.h
deleted file mode 100644
index a140f65..0000000
--- a/lang_id/light-sentence-features.h
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_LIGHT_SENTENCE_FEATURES_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_LIGHT_SENTENCE_FEATURES_H_
-
-#include "common/feature-extractor.h"
-#include "lang_id/light-sentence.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Feature function that extracts features from LightSentences.
-typedef FeatureFunction<LightSentence> LightSentenceFeature;
-
-// Feature extractor for LightSentences.
-typedef FeatureExtractor<LightSentence> LightSentenceExtractor;
-
-}  // namespace lang_id
-
-// Should be used in namespace libtextclassifier::nlp_core.
-TC_DECLARE_CLASS_REGISTRY_NAME(lang_id::LightSentenceFeature);
-
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_LANG_ID_LIGHT_SENTENCE_FEATURES_H_
diff --git a/lang_id/light-sentence.h b/lang_id/light-sentence.h
deleted file mode 100644
index e8451be..0000000
--- a/lang_id/light-sentence.h
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_LIGHT_SENTENCE_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_LIGHT_SENTENCE_H_
-
-#include <string>
-#include <vector>
-
-#include "util/base/logging.h"
-#include "util/base/macros.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Simplified replacement for the Sentence proto, for internal use in the
-// language identification code.
-//
-// In this simplified form, a sentence is a vector of words, each word being a
-// string.
-class LightSentence {
- public:
-  LightSentence() {}
-
-  // Adds a new word after all existing ones, and returns a pointer to it.  The
-  // new word is initialized to the empty string.
-  std::string *add_word() {
-    words_.emplace_back();
-    return &(words_.back());
-  }
-
-  // Returns number of words from this LightSentence.
-  int num_words() const { return words_.size(); }
-
-  // Returns the ith word from this LightSentence.  Note: undefined behavior if
-  // i is out of bounds.
-  const std::string &word(int i) const {
-    TC_DCHECK((i >= 0) && (i < num_words()));
-    return words_[i];
-  }
-
- private:
-  std::vector<std::string> words_;
-
-  TC_DISALLOW_COPY_AND_ASSIGN(LightSentence);
-};
-
-}  // namespace lang_id
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_LANG_ID_LIGHT_SENTENCE_H_
diff --git a/lang_id/relevant-script-feature.cc b/lang_id/relevant-script-feature.cc
deleted file mode 100644
index c865ce5..0000000
--- a/lang_id/relevant-script-feature.cc
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "lang_id/relevant-script-feature.h"
-
-#include <string>
-
-#include "common/feature-extractor.h"
-#include "common/feature-types.h"
-#include "common/task-context.h"
-#include "common/workspace.h"
-#include "lang_id/script-detector.h"
-#include "util/base/logging.h"
-#include "util/strings/utf8.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-bool RelevantScriptFeature::Setup(TaskContext *context) { return true; }
-
-bool RelevantScriptFeature::Init(TaskContext *context) {
-  set_feature_type(new NumericFeatureType(name(), kNumRelevantScripts));
-  return true;
-}
-
-void RelevantScriptFeature::Evaluate(const WorkspaceSet &workspaces,
-                                     const LightSentence &sentence,
-                                     FeatureVector *result) const {
-  // We expect kNumRelevantScripts to be small, so we stack-allocate the array
-  // of counts.  Still, if that changes, we want to find out.
-  static_assert(
-      kNumRelevantScripts < 25,
-      "switch counts to vector<int>: too big for stack-allocated int[]");
-
-  // counts[s] is the number of characters with script s.
-  // Note: {} "value-initializes" the array to zero.
-  int counts[kNumRelevantScripts]{};
-  int total_count = 0;
-  for (int i = 0; i < sentence.num_words(); ++i) {
-    const std::string &word = sentence.word(i);
-    const char *const word_end = word.data() + word.size();
-    const char *curr = word.data();
-
-    // Skip over token start '^'.
-    TC_DCHECK_EQ(*curr, '^');
-    curr += GetNumBytesForNonZeroUTF8Char(curr);
-    while (true) {
-      const int num_bytes = GetNumBytesForNonZeroUTF8Char(curr);
-      Script script = GetScript(curr, num_bytes);
-
-      // We do this update and the if (...) break below *before* incrementing
-      // counts[script] in order to skip the token end '$'.
-      curr += num_bytes;
-      if (curr >= word_end) {
-        TC_DCHECK_EQ(*(curr - num_bytes), '$');
-        break;
-      }
-      TC_DCHECK_GE(script, 0);
-      TC_DCHECK_LT(script, kNumRelevantScripts);
-      counts[script]++;
-      total_count++;
-    }
-  }
-
-  for (int script_id = 0; script_id < kNumRelevantScripts; ++script_id) {
-    int count = counts[script_id];
-    if (count > 0) {
-      const float weight = static_cast<float>(count) / total_count;
-      FloatFeatureValue value(script_id, weight);
-      result->add(feature_type(), value.discrete_value);
-    }
-  }
-}
-
-}  // namespace lang_id
-}  // namespace nlp_core
-}  // namespace libtextclassifier
diff --git a/lang_id/relevant-script-feature.h b/lang_id/relevant-script-feature.h
deleted file mode 100644
index 2aa2420..0000000
--- a/lang_id/relevant-script-feature.h
+++ /dev/null
@@ -1,58 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_RELEVANT_SCRIPT_FEATURE_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_RELEVANT_SCRIPT_FEATURE_H_
-
-#include "common/feature-extractor.h"
-#include "common/task-context.h"
-#include "common/workspace.h"
-#include "lang_id/light-sentence-features.h"
-#include "lang_id/light-sentence.h"
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Given a sentence, generates one FloatFeatureValue for each "relevant" Unicode
-// script (see below): each such feature indicates the script and the ratio of
-// UTF8 characters in that script, in the given sentence.
-//
-// What is a relevant script?  Recognizing all 100+ Unicode scripts would
-// require too much code size and runtime.  Instead, we focus only on a few
-// scripts that communicate a lot of language information: e.g., the use of
-// Hiragana characters almost always indicates Japanese, so Hiragana is a
-// "relevant" script for us.  The Latin script is used by dozens of language, so
-// Latin is not relevant in this context.
-class RelevantScriptFeature : public LightSentenceFeature {
- public:
-  // Idiomatic SAFT Setup() and Init().
-  bool Setup(TaskContext *context) override;
-  bool Init(TaskContext *context) override;
-
-  // Appends the features computed from the sentence to the feature vector.
-  void Evaluate(const WorkspaceSet &workspaces, const LightSentence &sentence,
-                FeatureVector *result) const override;
-
-  TC_DEFINE_REGISTRATION_METHOD("continuous-bag-of-relevant-scripts",
-                                RelevantScriptFeature);
-};
-
-}  // namespace lang_id
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_LANG_ID_RELEVANT_SCRIPT_FEATURE_H_
diff --git a/lang_id/script-detector.h b/lang_id/script-detector.h
deleted file mode 100644
index cf816ee..0000000
--- a/lang_id/script-detector.h
+++ /dev/null
@@ -1,162 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_LANG_ID_SCRIPT_DETECTOR_H_
-#define LIBTEXTCLASSIFIER_LANG_ID_SCRIPT_DETECTOR_H_
-
-namespace libtextclassifier {
-namespace nlp_core {
-namespace lang_id {
-
-// Unicode scripts we care about.  To get compact and fast code, we detect only
-// a few Unicode scripts that offer a strong indication about the language of
-// the text (e.g., Hiragana -> Japanese).
-enum Script {
-  // Special value to indicate internal errors in the script detection code.
-  kScriptError,
-
-  // Special values for all Unicode scripts that we do not detect.  One special
-  // value for Unicode characters of 1, 2, 3, respectively 4 bytes (as we
-  // already have that information, we use it).  kScriptOtherUtf8OneByte means
-  // ~Latin and kScriptOtherUtf8FourBytes means ~Han.
-  kScriptOtherUtf8OneByte,
-  kScriptOtherUtf8TwoBytes,
-  kScriptOtherUtf8ThreeBytes,
-  kScriptOtherUtf8FourBytes,
-
-  kScriptGreek,
-  kScriptCyrillic,
-  kScriptHebrew,
-  kScriptArabic,
-  kScriptHangulJamo,  // Used primarily for Korean.
-  kScriptHiragana,    // Used primarily for Japanese.
-  kScriptKatakana,    // Used primarily for Japanese.
-
-  // Add new scripts here.
-
-  // Do not add any script after kNumRelevantScripts.  This value indicates the
-  // number of elements in this enum Script (except this value) such that we can
-  // easily iterate over the scripts.
-  kNumRelevantScripts,
-};
-
-template<typename IntType>
-inline bool InRange(IntType value, IntType low, IntType hi) {
-  return (value >= low) && (value <= hi);
-}
-
-// Returns Script for the UTF8 character that starts at address p.
-// Precondition: p points to a valid UTF8 character of num_bytes bytes.
-inline Script GetScript(const unsigned char *p, int num_bytes) {
-  switch (num_bytes) {
-    case 1:
-      return kScriptOtherUtf8OneByte;
-
-    case 2: {
-      // 2-byte UTF8 characters have 11 bits of information.  unsigned int has
-      // at least 16 bits (http://en.cppreference.com/w/cpp/language/types) so
-      // it's enough.  It's also usually the fastest int type on the current
-      // CPU, so it's better to use than int32.
-      static const unsigned int kGreekStart = 0x370;
-
-      // Commented out (unsued in the code): kGreekEnd = 0x3FF;
-      static const unsigned int kCyrillicStart = 0x400;
-      static const unsigned int kCyrillicEnd = 0x4FF;
-      static const unsigned int kHebrewStart = 0x590;
-
-      // Commented out (unsued in the code): kHebrewEnd = 0x5FF;
-      static const unsigned int kArabicStart = 0x600;
-      static const unsigned int kArabicEnd = 0x6FF;
-      const unsigned int codepoint = ((p[0] & 0x1F) << 6) | (p[1] & 0x3F);
-      if (codepoint > kCyrillicEnd) {
-        if (codepoint >= kArabicStart) {
-          if (codepoint <= kArabicEnd) {
-            return kScriptArabic;
-          }
-        } else {
-          // At this point, codepoint < kArabicStart = kHebrewEnd + 1, so
-          // codepoint <= kHebrewEnd.
-          if (codepoint >= kHebrewStart) {
-            return kScriptHebrew;
-          }
-        }
-      } else {
-        if (codepoint >= kCyrillicStart) {
-          return kScriptCyrillic;
-        } else {
-          // At this point, codepoint < kCyrillicStart = kGreekEnd + 1, so
-          // codepoint <= kGreekEnd.
-          if (codepoint >= kGreekStart) {
-            return kScriptGreek;
-          }
-        }
-      }
-      return kScriptOtherUtf8TwoBytes;
-    }
-
-    case 3: {
-      // 3-byte UTF8 characters have 16 bits of information.  unsigned int has
-      // at least 16 bits.
-      static const unsigned int kHangulJamoStart = 0x1100;
-      static const unsigned int kHangulJamoEnd = 0x11FF;
-      static const unsigned int kHiraganaStart = 0x3041;
-      static const unsigned int kHiraganaEnd = 0x309F;
-
-      // Commented out (unsued in the code): kKatakanaStart = 0x30A0;
-      static const unsigned int kKatakanaEnd = 0x30FF;
-      const unsigned int codepoint =
-          ((p[0] & 0x0F) << 12) | ((p[1] & 0x3F) << 6) | (p[2] & 0x3F);
-      if (codepoint > kHiraganaEnd) {
-        // On this branch, codepoint > kHiraganaEnd = kKatakanaStart - 1, so
-        // codepoint >= kKatakanaStart.
-        if (codepoint <= kKatakanaEnd) {
-          return kScriptKatakana;
-        }
-      } else {
-        if (codepoint >= kHiraganaStart) {
-          return kScriptHiragana;
-        } else {
-          if (InRange(codepoint, kHangulJamoStart, kHangulJamoEnd)) {
-            return kScriptHangulJamo;
-          }
-        }
-      }
-      return kScriptOtherUtf8ThreeBytes;
-    }
-
-    case 4:
-      return kScriptOtherUtf8FourBytes;
-
-    default:
-      return kScriptError;
-  }
-}
-
-// Returns Script for the UTF8 character that starts at address p.  Similar to
-// the previous version of GetScript, except for "char" vs "unsigned char".
-// Most code works with "char *" pointers, ignoring the fact that char is
-// unsigned (by default) on most platforms, but signed on iOS.  This code takes
-// care of making sure we always treat chars as unsigned.
-inline Script GetScript(const char *p, int num_bytes) {
-  return GetScript(reinterpret_cast<const unsigned char *>(p),
-                   num_bytes);
-}
-
-}  // namespace lang_id
-}  // namespace nlp_core
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_LANG_ID_SCRIPT_DETECTOR_H_
diff --git a/model-executor.cc b/model-executor.cc
new file mode 100644
index 0000000..2b1fc11
--- /dev/null
+++ b/model-executor.cc
@@ -0,0 +1,124 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "model-executor.h"
+
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+namespace internal {
+bool FromModelSpec(const tflite::Model* model_spec,
+                   std::unique_ptr<tflite::FlatBufferModel>* model,
+                   std::unique_ptr<tflite::Interpreter>* interpreter) {
+  *model = tflite::FlatBufferModel::BuildFromModel(model_spec);
+  if (!(*model) || !(*model)->initialized()) {
+    TC_LOG(ERROR) << "Could not build TFLite model from a model spec. ";
+    return false;
+  }
+
+  tflite::ops::builtin::BuiltinOpResolver builtins;
+  tflite::InterpreterBuilder(**model, builtins)(interpreter);
+  if (!interpreter) {
+    TC_LOG(ERROR) << "Could not build TFLite interpreter.";
+    return false;
+  }
+  return true;
+}
+}  // namespace internal
+
+TFLiteEmbeddingExecutor::TFLiteEmbeddingExecutor(
+    const tflite::Model* model_spec) {
+  internal::FromModelSpec(model_spec, &model_, &interpreter_);
+  if (!interpreter_) {
+    return;
+  }
+  if (interpreter_->tensors_size() != 2) {
+    return;
+  }
+  embeddings_ = interpreter_->tensor(0);
+  if (embeddings_->dims->size != 2) {
+    return;
+  }
+  num_buckets_ = embeddings_->dims->data[0];
+  scales_ = interpreter_->tensor(1);
+  if (scales_->dims->size != 2 || scales_->dims->data[0] != num_buckets_ ||
+      scales_->dims->data[1] != 1) {
+    return;
+  }
+  embedding_size_ = embeddings_->dims->data[1];
+  initialized_ = true;
+}
+
+bool TFLiteEmbeddingExecutor::AddEmbedding(
+    const TensorView<int>& sparse_features, float* dest, int dest_size) {
+  if (!initialized_ || dest_size != embedding_size_) {
+    return false;
+  }
+  const int num_sparse_features = sparse_features.size();
+  for (int i = 0; i < num_sparse_features; ++i) {
+    const int bucket_id = sparse_features.data()[i];
+    if (bucket_id >= num_buckets_) {
+      return false;
+    }
+    const float multiplier = scales_->data.f[bucket_id];
+    for (int k = 0; k < embedding_size_; ++k) {
+      // Dequantize and add the embedding.
+      dest[k] +=
+          1.0 / num_sparse_features *
+          (static_cast<int>(
+               embeddings_->data.uint8[bucket_id * embedding_size_ + k]) -
+           kQuantBias) *
+          multiplier;
+    }
+  }
+  return true;
+}
+
+TensorView<float> ComputeLogitsHelper(const int input_index_features,
+                                      const int output_index_logits,
+                                      const TensorView<float>& features,
+                                      tflite::Interpreter* interpreter) {
+  interpreter->ResizeInputTensor(input_index_features, features.shape());
+  if (interpreter->AllocateTensors() != kTfLiteOk) {
+    TC_VLOG(1) << "Allocation failed.";
+    return TensorView<float>::Invalid();
+  }
+
+  TfLiteTensor* features_tensor =
+      interpreter->tensor(interpreter->inputs()[input_index_features]);
+  int size = 1;
+  for (int i = 0; i < features_tensor->dims->size; ++i) {
+    size *= features_tensor->dims->data[i];
+  }
+  features.copy_to(features_tensor->data.f, size);
+
+  if (interpreter->Invoke() != kTfLiteOk) {
+    TC_VLOG(1) << "Interpreter failed.";
+    return TensorView<float>::Invalid();
+  }
+
+  TfLiteTensor* logits_tensor =
+      interpreter->tensor(interpreter->outputs()[output_index_logits]);
+
+  std::vector<int> output_shape(logits_tensor->dims->size);
+  for (int i = 0; i < logits_tensor->dims->size; ++i) {
+    output_shape[i] = logits_tensor->dims->data[i];
+  }
+
+  return TensorView<float>(logits_tensor->data.f, output_shape);
+}
+
+}  // namespace libtextclassifier2
diff --git a/model-executor.h b/model-executor.h
new file mode 100644
index 0000000..b16d53d
--- /dev/null
+++ b/model-executor.h
@@ -0,0 +1,104 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Contains classes that can execute different models/parts of a model.
+
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_MODEL_EXECUTOR_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_MODEL_EXECUTOR_H_
+
+#include <memory>
+
+#include "tensor-view.h"
+#include "types.h"
+#include "util/base/logging.h"
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/model.h"
+
+namespace libtextclassifier2 {
+
+namespace internal {
+bool FromModelSpec(const tflite::Model* model_spec,
+                   std::unique_ptr<tflite::FlatBufferModel>* model,
+                   std::unique_ptr<tflite::Interpreter>* interpreter);
+}  // namespace internal
+
+// A helper function that given indices of feature and logits tensor, feature
+// values computes the logits using given interpreter.
+TensorView<float> ComputeLogitsHelper(const int input_index_features,
+                                      const int output_index_logits,
+                                      const TensorView<float>& features,
+                                      tflite::Interpreter* interpreter);
+
+// Executor for the text selection prediction and classification models.
+// NOTE: This class is not thread-safe.
+class ModelExecutor {
+ public:
+  explicit ModelExecutor(const tflite::Model* model_spec) {
+    internal::FromModelSpec(model_spec, &model_, &interpreter_);
+  }
+
+  TensorView<float> ComputeLogits(const TensorView<float>& features) {
+    return ComputeLogitsHelper(kInputIndexFeatures, kOutputIndexLogits,
+                               features, interpreter_.get());
+  }
+
+ protected:
+  static const int kInputIndexFeatures = 0;
+  static const int kOutputIndexLogits = 0;
+
+  std::unique_ptr<tflite::FlatBufferModel> model_ = nullptr;
+  std::unique_ptr<tflite::Interpreter> interpreter_ = nullptr;
+};
+
+// Executor for embedding sparse features into a dense vector.
+class EmbeddingExecutor {
+ public:
+  virtual ~EmbeddingExecutor() {}
+
+  // Embeds the sparse_features into a dense embedding and adds (+) it
+  // element-wise to the dest vector.
+  virtual bool AddEmbedding(const TensorView<int>& sparse_features, float* dest,
+                            int dest_size) = 0;
+
+  // Returns true when the model is ready to be used, false otherwise.
+  virtual bool IsReady() { return true; }
+};
+
+// NOTE: This class is not thread-safe.
+class TFLiteEmbeddingExecutor : public EmbeddingExecutor {
+ public:
+  explicit TFLiteEmbeddingExecutor(const tflite::Model* model_spec);
+  bool AddEmbedding(const TensorView<int>& sparse_features, float* dest,
+                    int dest_size) override;
+
+  bool IsReady() override { return initialized_; }
+
+ protected:
+  static const int kQuantBias = 128;
+  bool initialized_ = false;
+  int num_buckets_ = -1;
+  int embedding_size_ = -1;
+  const TfLiteTensor* scales_ = nullptr;
+  const TfLiteTensor* embeddings_ = nullptr;
+
+  std::unique_ptr<tflite::FlatBufferModel> model_ = nullptr;
+  std::unique_ptr<tflite::Interpreter> interpreter_ = nullptr;
+};
+
+}  // namespace libtextclassifier2
+
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_MODEL_EXECUTOR_H_
diff --git a/model.fbs b/model.fbs
new file mode 100755
index 0000000..d98e5ac
--- /dev/null
+++ b/model.fbs
@@ -0,0 +1,145 @@
+// Generated from model.proto
+
+namespace libtextclassifier2.TokenizationCodepointRange_;
+
+enum Role : int {
+  DEFAULT_ROLE = 0,
+  SPLIT_BEFORE = 1,
+  SPLIT_AFTER = 2,
+  TOKEN_SEPARATOR = 3,
+  DISCARD_CODEPOINT = 4,
+  WHITESPACE_SEPARATOR = 7,
+}
+
+namespace libtextclassifier2.FeatureProcessorOptions_;
+
+enum CenterTokenSelectionMethod : int {
+  DEFAULT_CENTER_TOKEN_METHOD = 0,
+  CENTER_TOKEN_FROM_CLICK = 1,
+  CENTER_TOKEN_MIDDLE_OF_SELECTION = 2,
+}
+
+enum TokenizationType : int {
+  INVALID_TOKENIZATION_TYPE = 0,
+  INTERNAL_TOKENIZER = 1,
+  ICU = 2,
+  MIXED = 3,
+}
+
+namespace libtextclassifier2;
+
+table SelectionModelOptions {
+  strip_unpaired_brackets:bool;
+  symmetry_context_size:int;
+}
+
+table ClassificationModelOptions {
+  phone_min_num_digits:int = 7;
+  phone_max_num_digits:int = 15;
+}
+
+table RegexModelOptions {
+  patterns:[libtextclassifier2.RegexModelOptions_.Pattern];
+}
+
+namespace libtextclassifier2.RegexModelOptions_;
+
+table Pattern {
+  collection_name:string;
+  pattern:string;
+}
+
+namespace libtextclassifier2;
+
+table StructuredRegexModel {
+  patterns:[libtextclassifier2.StructuredRegexModel_.StructuredPattern];
+}
+
+namespace libtextclassifier2.StructuredRegexModel_;
+
+table StructuredPattern {
+  pattern:string;
+  node_names:[string];
+}
+
+namespace libtextclassifier2;
+
+table Model {
+  language:string;
+  version:int;
+  selection_feature_options:libtextclassifier2.FeatureProcessorOptions;
+  classification_feature_options:libtextclassifier2.FeatureProcessorOptions;
+  selection_model:[ubyte];
+  classification_model:[ubyte];
+  embedding_model:[ubyte];
+  regex_options:libtextclassifier2.RegexModelOptions;
+  selection_options:libtextclassifier2.SelectionModelOptions;
+  classification_options:libtextclassifier2.ClassificationModelOptions;
+  regex_model:libtextclassifier2.StructuredRegexModel;
+}
+
+table TokenizationCodepointRange {
+  start:int;
+  end:int;
+  role:libtextclassifier2.TokenizationCodepointRange_.Role;
+  script_id:int;
+}
+
+table FeatureProcessorOptions {
+  num_buckets:int = -1;
+  embedding_size:int = -1;
+  context_size:int = -1;
+  max_selection_span:int = -1;
+  chargram_orders:[int];
+  max_word_length:int = 20;
+  unicode_aware_features:bool;
+  extract_case_feature:bool;
+  extract_selection_mask_feature:bool;
+  regexp_feature:[string];
+  remap_digits:bool;
+  lowercase_tokens:bool;
+  selection_reduced_output_space:bool;
+  collections:[string];
+  default_collection:int = -1;
+  only_use_line_with_click:bool;
+  split_tokens_on_selection_boundaries:bool;
+  tokenization_codepoint_config:[libtextclassifier2.TokenizationCodepointRange];
+  center_token_selection_method:libtextclassifier2.FeatureProcessorOptions_.CenterTokenSelectionMethod;
+  snap_label_span_boundaries_to_containing_tokens:bool;
+  supported_codepoint_ranges:[libtextclassifier2.FeatureProcessorOptions_.CodepointRange];
+  internal_tokenizer_codepoint_ranges:[libtextclassifier2.FeatureProcessorOptions_.CodepointRange];
+  min_supported_codepoint_ratio:float = 0.0;
+  feature_version:int;
+  tokenization_type:libtextclassifier2.FeatureProcessorOptions_.TokenizationType;
+  icu_preserve_whitespace_tokens:bool;
+  ignored_span_boundary_codepoints:[int];
+  click_random_token_in_selection:bool;
+  alternative_collection_map:[libtextclassifier2.FeatureProcessorOptions_.CollectionMapEntry];
+  bounds_sensitive_features:libtextclassifier2.FeatureProcessorOptions_.BoundsSensitiveFeatures;
+  split_selection_candidates:bool;
+  allowed_chargrams:[string];
+  tokenize_on_script_change:bool;
+}
+
+namespace libtextclassifier2.FeatureProcessorOptions_;
+
+table CodepointRange {
+  start:int;
+  end:int;
+}
+
+table CollectionMapEntry {
+  key:string;
+  value:string;
+}
+
+table BoundsSensitiveFeatures {
+  enabled:bool;
+  num_tokens_before:int;
+  num_tokens_inside_left:int;
+  num_tokens_inside_right:int;
+  num_tokens_after:int;
+  include_inside_bag:bool;
+  include_inside_length:bool;
+}
+
diff --git a/model_generated.h b/model_generated.h
new file mode 100755
index 0000000..fd11c39
--- /dev/null
+++ b/model_generated.h
@@ -0,0 +1,2189 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// automatically generated by the FlatBuffers compiler, do not modify
+
+
+#ifndef FLATBUFFERS_GENERATED_MODEL_LIBTEXTCLASSIFIER2_FEATUREPROCESSOROPTIONS__H_
+#define FLATBUFFERS_GENERATED_MODEL_LIBTEXTCLASSIFIER2_FEATUREPROCESSOROPTIONS__H_
+
+#include "flatbuffers/flatbuffers.h"
+
+namespace libtextclassifier2 {
+
+struct SelectionModelOptions;
+struct SelectionModelOptionsT;
+
+struct ClassificationModelOptions;
+struct ClassificationModelOptionsT;
+
+struct RegexModelOptions;
+struct RegexModelOptionsT;
+
+namespace RegexModelOptions_ {
+
+struct Pattern;
+struct PatternT;
+
+}  // namespace RegexModelOptions_
+
+struct StructuredRegexModel;
+struct StructuredRegexModelT;
+
+namespace StructuredRegexModel_ {
+
+struct StructuredPattern;
+struct StructuredPatternT;
+
+}  // namespace StructuredRegexModel_
+
+struct Model;
+struct ModelT;
+
+struct TokenizationCodepointRange;
+struct TokenizationCodepointRangeT;
+
+struct FeatureProcessorOptions;
+struct FeatureProcessorOptionsT;
+
+namespace FeatureProcessorOptions_ {
+
+struct CodepointRange;
+struct CodepointRangeT;
+
+struct CollectionMapEntry;
+struct CollectionMapEntryT;
+
+struct BoundsSensitiveFeatures;
+struct BoundsSensitiveFeaturesT;
+
+}  // namespace FeatureProcessorOptions_
+
+namespace TokenizationCodepointRange_ {
+
+enum Role {
+  Role_DEFAULT_ROLE = 0,
+  Role_SPLIT_BEFORE = 1,
+  Role_SPLIT_AFTER = 2,
+  Role_TOKEN_SEPARATOR = 3,
+  Role_DISCARD_CODEPOINT = 4,
+  Role_WHITESPACE_SEPARATOR = 7,
+  Role_MIN = Role_DEFAULT_ROLE,
+  Role_MAX = Role_WHITESPACE_SEPARATOR
+};
+
+inline Role (&EnumValuesRole())[6] {
+  static Role values[] = {
+    Role_DEFAULT_ROLE,
+    Role_SPLIT_BEFORE,
+    Role_SPLIT_AFTER,
+    Role_TOKEN_SEPARATOR,
+    Role_DISCARD_CODEPOINT,
+    Role_WHITESPACE_SEPARATOR
+  };
+  return values;
+}
+
+inline const char **EnumNamesRole() {
+  static const char *names[] = {
+    "DEFAULT_ROLE",
+    "SPLIT_BEFORE",
+    "SPLIT_AFTER",
+    "TOKEN_SEPARATOR",
+    "DISCARD_CODEPOINT",
+    "",
+    "",
+    "WHITESPACE_SEPARATOR",
+    nullptr
+  };
+  return names;
+}
+
+inline const char *EnumNameRole(Role e) {
+  const size_t index = static_cast<int>(e);
+  return EnumNamesRole()[index];
+}
+
+}  // namespace TokenizationCodepointRange_
+
+namespace FeatureProcessorOptions_ {
+
+enum CenterTokenSelectionMethod {
+  CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD = 0,
+  CenterTokenSelectionMethod_CENTER_TOKEN_FROM_CLICK = 1,
+  CenterTokenSelectionMethod_CENTER_TOKEN_MIDDLE_OF_SELECTION = 2,
+  CenterTokenSelectionMethod_MIN = CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD,
+  CenterTokenSelectionMethod_MAX = CenterTokenSelectionMethod_CENTER_TOKEN_MIDDLE_OF_SELECTION
+};
+
+inline CenterTokenSelectionMethod (&EnumValuesCenterTokenSelectionMethod())[3] {
+  static CenterTokenSelectionMethod values[] = {
+    CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD,
+    CenterTokenSelectionMethod_CENTER_TOKEN_FROM_CLICK,
+    CenterTokenSelectionMethod_CENTER_TOKEN_MIDDLE_OF_SELECTION
+  };
+  return values;
+}
+
+inline const char **EnumNamesCenterTokenSelectionMethod() {
+  static const char *names[] = {
+    "DEFAULT_CENTER_TOKEN_METHOD",
+    "CENTER_TOKEN_FROM_CLICK",
+    "CENTER_TOKEN_MIDDLE_OF_SELECTION",
+    nullptr
+  };
+  return names;
+}
+
+inline const char *EnumNameCenterTokenSelectionMethod(CenterTokenSelectionMethod e) {
+  const size_t index = static_cast<int>(e);
+  return EnumNamesCenterTokenSelectionMethod()[index];
+}
+
+enum TokenizationType {
+  TokenizationType_INVALID_TOKENIZATION_TYPE = 0,
+  TokenizationType_INTERNAL_TOKENIZER = 1,
+  TokenizationType_ICU = 2,
+  TokenizationType_MIXED = 3,
+  TokenizationType_MIN = TokenizationType_INVALID_TOKENIZATION_TYPE,
+  TokenizationType_MAX = TokenizationType_MIXED
+};
+
+inline TokenizationType (&EnumValuesTokenizationType())[4] {
+  static TokenizationType values[] = {
+    TokenizationType_INVALID_TOKENIZATION_TYPE,
+    TokenizationType_INTERNAL_TOKENIZER,
+    TokenizationType_ICU,
+    TokenizationType_MIXED
+  };
+  return values;
+}
+
+inline const char **EnumNamesTokenizationType() {
+  static const char *names[] = {
+    "INVALID_TOKENIZATION_TYPE",
+    "INTERNAL_TOKENIZER",
+    "ICU",
+    "MIXED",
+    nullptr
+  };
+  return names;
+}
+
+inline const char *EnumNameTokenizationType(TokenizationType e) {
+  const size_t index = static_cast<int>(e);
+  return EnumNamesTokenizationType()[index];
+}
+
+}  // namespace FeatureProcessorOptions_
+
+struct SelectionModelOptionsT : public flatbuffers::NativeTable {
+  typedef SelectionModelOptions TableType;
+  bool strip_unpaired_brackets;
+  int32_t symmetry_context_size;
+  SelectionModelOptionsT()
+      : strip_unpaired_brackets(false),
+        symmetry_context_size(0) {
+  }
+};
+
+struct SelectionModelOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef SelectionModelOptionsT NativeTableType;
+  enum {
+    VT_STRIP_UNPAIRED_BRACKETS = 4,
+    VT_SYMMETRY_CONTEXT_SIZE = 6
+  };
+  bool strip_unpaired_brackets() const {
+    return GetField<uint8_t>(VT_STRIP_UNPAIRED_BRACKETS, 0) != 0;
+  }
+  int32_t symmetry_context_size() const {
+    return GetField<int32_t>(VT_SYMMETRY_CONTEXT_SIZE, 0);
+  }
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           VerifyField<uint8_t>(verifier, VT_STRIP_UNPAIRED_BRACKETS) &&
+           VerifyField<int32_t>(verifier, VT_SYMMETRY_CONTEXT_SIZE) &&
+           verifier.EndTable();
+  }
+  SelectionModelOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(SelectionModelOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<SelectionModelOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectionModelOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct SelectionModelOptionsBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  void add_strip_unpaired_brackets(bool strip_unpaired_brackets) {
+    fbb_.AddElement<uint8_t>(SelectionModelOptions::VT_STRIP_UNPAIRED_BRACKETS, static_cast<uint8_t>(strip_unpaired_brackets), 0);
+  }
+  void add_symmetry_context_size(int32_t symmetry_context_size) {
+    fbb_.AddElement<int32_t>(SelectionModelOptions::VT_SYMMETRY_CONTEXT_SIZE, symmetry_context_size, 0);
+  }
+  explicit SelectionModelOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  SelectionModelOptionsBuilder &operator=(const SelectionModelOptionsBuilder &);
+  flatbuffers::Offset<SelectionModelOptions> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<SelectionModelOptions>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<SelectionModelOptions> CreateSelectionModelOptions(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    bool strip_unpaired_brackets = false,
+    int32_t symmetry_context_size = 0) {
+  SelectionModelOptionsBuilder builder_(_fbb);
+  builder_.add_symmetry_context_size(symmetry_context_size);
+  builder_.add_strip_unpaired_brackets(strip_unpaired_brackets);
+  return builder_.Finish();
+}
+
+flatbuffers::Offset<SelectionModelOptions> CreateSelectionModelOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectionModelOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct ClassificationModelOptionsT : public flatbuffers::NativeTable {
+  typedef ClassificationModelOptions TableType;
+  int32_t phone_min_num_digits;
+  int32_t phone_max_num_digits;
+  ClassificationModelOptionsT()
+      : phone_min_num_digits(7),
+        phone_max_num_digits(15) {
+  }
+};
+
+struct ClassificationModelOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef ClassificationModelOptionsT NativeTableType;
+  enum {
+    VT_PHONE_MIN_NUM_DIGITS = 4,
+    VT_PHONE_MAX_NUM_DIGITS = 6
+  };
+  int32_t phone_min_num_digits() const {
+    return GetField<int32_t>(VT_PHONE_MIN_NUM_DIGITS, 7);
+  }
+  int32_t phone_max_num_digits() const {
+    return GetField<int32_t>(VT_PHONE_MAX_NUM_DIGITS, 15);
+  }
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           VerifyField<int32_t>(verifier, VT_PHONE_MIN_NUM_DIGITS) &&
+           VerifyField<int32_t>(verifier, VT_PHONE_MAX_NUM_DIGITS) &&
+           verifier.EndTable();
+  }
+  ClassificationModelOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(ClassificationModelOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<ClassificationModelOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ClassificationModelOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct ClassificationModelOptionsBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  void add_phone_min_num_digits(int32_t phone_min_num_digits) {
+    fbb_.AddElement<int32_t>(ClassificationModelOptions::VT_PHONE_MIN_NUM_DIGITS, phone_min_num_digits, 7);
+  }
+  void add_phone_max_num_digits(int32_t phone_max_num_digits) {
+    fbb_.AddElement<int32_t>(ClassificationModelOptions::VT_PHONE_MAX_NUM_DIGITS, phone_max_num_digits, 15);
+  }
+  explicit ClassificationModelOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  ClassificationModelOptionsBuilder &operator=(const ClassificationModelOptionsBuilder &);
+  flatbuffers::Offset<ClassificationModelOptions> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<ClassificationModelOptions>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<ClassificationModelOptions> CreateClassificationModelOptions(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    int32_t phone_min_num_digits = 7,
+    int32_t phone_max_num_digits = 15) {
+  ClassificationModelOptionsBuilder builder_(_fbb);
+  builder_.add_phone_max_num_digits(phone_max_num_digits);
+  builder_.add_phone_min_num_digits(phone_min_num_digits);
+  return builder_.Finish();
+}
+
+flatbuffers::Offset<ClassificationModelOptions> CreateClassificationModelOptions(flatbuffers::FlatBufferBuilder &_fbb, const ClassificationModelOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct RegexModelOptionsT : public flatbuffers::NativeTable {
+  typedef RegexModelOptions TableType;
+  std::vector<std::unique_ptr<libtextclassifier2::RegexModelOptions_::PatternT>> patterns;
+  RegexModelOptionsT() {
+  }
+};
+
+struct RegexModelOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef RegexModelOptionsT NativeTableType;
+  enum {
+    VT_PATTERNS = 4
+  };
+  const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::RegexModelOptions_::Pattern>> *patterns() const {
+    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::RegexModelOptions_::Pattern>> *>(VT_PATTERNS);
+  }
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           VerifyOffset(verifier, VT_PATTERNS) &&
+           verifier.Verify(patterns()) &&
+           verifier.VerifyVectorOfTables(patterns()) &&
+           verifier.EndTable();
+  }
+  RegexModelOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(RegexModelOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<RegexModelOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const RegexModelOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct RegexModelOptionsBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  void add_patterns(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::RegexModelOptions_::Pattern>>> patterns) {
+    fbb_.AddOffset(RegexModelOptions::VT_PATTERNS, patterns);
+  }
+  explicit RegexModelOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  RegexModelOptionsBuilder &operator=(const RegexModelOptionsBuilder &);
+  flatbuffers::Offset<RegexModelOptions> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<RegexModelOptions>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<RegexModelOptions> CreateRegexModelOptions(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::RegexModelOptions_::Pattern>>> patterns = 0) {
+  RegexModelOptionsBuilder builder_(_fbb);
+  builder_.add_patterns(patterns);
+  return builder_.Finish();
+}
+
+inline flatbuffers::Offset<RegexModelOptions> CreateRegexModelOptionsDirect(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    const std::vector<flatbuffers::Offset<libtextclassifier2::RegexModelOptions_::Pattern>> *patterns = nullptr) {
+  return libtextclassifier2::CreateRegexModelOptions(
+      _fbb,
+      patterns ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::RegexModelOptions_::Pattern>>(*patterns) : 0);
+}
+
+flatbuffers::Offset<RegexModelOptions> CreateRegexModelOptions(flatbuffers::FlatBufferBuilder &_fbb, const RegexModelOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+namespace RegexModelOptions_ {
+
+struct PatternT : public flatbuffers::NativeTable {
+  typedef Pattern TableType;
+  std::string collection_name;
+  std::string pattern;
+  PatternT() {
+  }
+};
+
+struct Pattern FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef PatternT NativeTableType;
+  enum {
+    VT_COLLECTION_NAME = 4,
+    VT_PATTERN = 6
+  };
+  const flatbuffers::String *collection_name() const {
+    return GetPointer<const flatbuffers::String *>(VT_COLLECTION_NAME);
+  }
+  const flatbuffers::String *pattern() const {
+    return GetPointer<const flatbuffers::String *>(VT_PATTERN);
+  }
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           VerifyOffset(verifier, VT_COLLECTION_NAME) &&
+           verifier.Verify(collection_name()) &&
+           VerifyOffset(verifier, VT_PATTERN) &&
+           verifier.Verify(pattern()) &&
+           verifier.EndTable();
+  }
+  PatternT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(PatternT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<Pattern> Pack(flatbuffers::FlatBufferBuilder &_fbb, const PatternT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct PatternBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  void add_collection_name(flatbuffers::Offset<flatbuffers::String> collection_name) {
+    fbb_.AddOffset(Pattern::VT_COLLECTION_NAME, collection_name);
+  }
+  void add_pattern(flatbuffers::Offset<flatbuffers::String> pattern) {
+    fbb_.AddOffset(Pattern::VT_PATTERN, pattern);
+  }
+  explicit PatternBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  PatternBuilder &operator=(const PatternBuilder &);
+  flatbuffers::Offset<Pattern> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<Pattern>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<Pattern> CreatePattern(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    flatbuffers::Offset<flatbuffers::String> collection_name = 0,
+    flatbuffers::Offset<flatbuffers::String> pattern = 0) {
+  PatternBuilder builder_(_fbb);
+  builder_.add_pattern(pattern);
+  builder_.add_collection_name(collection_name);
+  return builder_.Finish();
+}
+
+inline flatbuffers::Offset<Pattern> CreatePatternDirect(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    const char *collection_name = nullptr,
+    const char *pattern = nullptr) {
+  return libtextclassifier2::RegexModelOptions_::CreatePattern(
+      _fbb,
+      collection_name ? _fbb.CreateString(collection_name) : 0,
+      pattern ? _fbb.CreateString(pattern) : 0);
+}
+
+flatbuffers::Offset<Pattern> CreatePattern(flatbuffers::FlatBufferBuilder &_fbb, const PatternT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+}  // namespace RegexModelOptions_
+
+struct StructuredRegexModelT : public flatbuffers::NativeTable {
+  typedef StructuredRegexModel TableType;
+  std::vector<std::unique_ptr<libtextclassifier2::StructuredRegexModel_::StructuredPatternT>> patterns;
+  StructuredRegexModelT() {
+  }
+};
+
+struct StructuredRegexModel FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef StructuredRegexModelT NativeTableType;
+  enum {
+    VT_PATTERNS = 4
+  };
+  const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::StructuredRegexModel_::StructuredPattern>> *patterns() const {
+    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::StructuredRegexModel_::StructuredPattern>> *>(VT_PATTERNS);
+  }
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           VerifyOffset(verifier, VT_PATTERNS) &&
+           verifier.Verify(patterns()) &&
+           verifier.VerifyVectorOfTables(patterns()) &&
+           verifier.EndTable();
+  }
+  StructuredRegexModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(StructuredRegexModelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<StructuredRegexModel> Pack(flatbuffers::FlatBufferBuilder &_fbb, const StructuredRegexModelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StructuredRegexModelBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  void add_patterns(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::StructuredRegexModel_::StructuredPattern>>> patterns) {
+    fbb_.AddOffset(StructuredRegexModel::VT_PATTERNS, patterns);
+  }
+  explicit StructuredRegexModelBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  StructuredRegexModelBuilder &operator=(const StructuredRegexModelBuilder &);
+  flatbuffers::Offset<StructuredRegexModel> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<StructuredRegexModel>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<StructuredRegexModel> CreateStructuredRegexModel(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::StructuredRegexModel_::StructuredPattern>>> patterns = 0) {
+  StructuredRegexModelBuilder builder_(_fbb);
+  builder_.add_patterns(patterns);
+  return builder_.Finish();
+}
+
+inline flatbuffers::Offset<StructuredRegexModel> CreateStructuredRegexModelDirect(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    const std::vector<flatbuffers::Offset<libtextclassifier2::StructuredRegexModel_::StructuredPattern>> *patterns = nullptr) {
+  return libtextclassifier2::CreateStructuredRegexModel(
+      _fbb,
+      patterns ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::StructuredRegexModel_::StructuredPattern>>(*patterns) : 0);
+}
+
+flatbuffers::Offset<StructuredRegexModel> CreateStructuredRegexModel(flatbuffers::FlatBufferBuilder &_fbb, const StructuredRegexModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+namespace StructuredRegexModel_ {
+
+struct StructuredPatternT : public flatbuffers::NativeTable {
+  typedef StructuredPattern TableType;
+  std::string pattern;
+  std::vector<std::string> node_names;
+  StructuredPatternT() {
+  }
+};
+
+struct StructuredPattern FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef StructuredPatternT NativeTableType;
+  enum {
+    VT_PATTERN = 4,
+    VT_NODE_NAMES = 6
+  };
+  const flatbuffers::String *pattern() const {
+    return GetPointer<const flatbuffers::String *>(VT_PATTERN);
+  }
+  const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *node_names() const {
+    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_NODE_NAMES);
+  }
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           VerifyOffset(verifier, VT_PATTERN) &&
+           verifier.Verify(pattern()) &&
+           VerifyOffset(verifier, VT_NODE_NAMES) &&
+           verifier.Verify(node_names()) &&
+           verifier.VerifyVectorOfStrings(node_names()) &&
+           verifier.EndTable();
+  }
+  StructuredPatternT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(StructuredPatternT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<StructuredPattern> Pack(flatbuffers::FlatBufferBuilder &_fbb, const StructuredPatternT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct StructuredPatternBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  void add_pattern(flatbuffers::Offset<flatbuffers::String> pattern) {
+    fbb_.AddOffset(StructuredPattern::VT_PATTERN, pattern);
+  }
+  void add_node_names(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> node_names) {
+    fbb_.AddOffset(StructuredPattern::VT_NODE_NAMES, node_names);
+  }
+  explicit StructuredPatternBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  StructuredPatternBuilder &operator=(const StructuredPatternBuilder &);
+  flatbuffers::Offset<StructuredPattern> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<StructuredPattern>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<StructuredPattern> CreateStructuredPattern(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    flatbuffers::Offset<flatbuffers::String> pattern = 0,
+    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> node_names = 0) {
+  StructuredPatternBuilder builder_(_fbb);
+  builder_.add_node_names(node_names);
+  builder_.add_pattern(pattern);
+  return builder_.Finish();
+}
+
+inline flatbuffers::Offset<StructuredPattern> CreateStructuredPatternDirect(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    const char *pattern = nullptr,
+    const std::vector<flatbuffers::Offset<flatbuffers::String>> *node_names = nullptr) {
+  return libtextclassifier2::StructuredRegexModel_::CreateStructuredPattern(
+      _fbb,
+      pattern ? _fbb.CreateString(pattern) : 0,
+      node_names ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*node_names) : 0);
+}
+
+flatbuffers::Offset<StructuredPattern> CreateStructuredPattern(flatbuffers::FlatBufferBuilder &_fbb, const StructuredPatternT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+}  // namespace StructuredRegexModel_
+
+struct ModelT : public flatbuffers::NativeTable {
+  typedef Model TableType;
+  std::string language;
+  int32_t version;
+  std::unique_ptr<FeatureProcessorOptionsT> selection_feature_options;
+  std::unique_ptr<FeatureProcessorOptionsT> classification_feature_options;
+  std::vector<uint8_t> selection_model;
+  std::vector<uint8_t> classification_model;
+  std::vector<uint8_t> embedding_model;
+  std::unique_ptr<RegexModelOptionsT> regex_options;
+  std::unique_ptr<SelectionModelOptionsT> selection_options;
+  std::unique_ptr<ClassificationModelOptionsT> classification_options;
+  std::unique_ptr<StructuredRegexModelT> regex_model;
+  ModelT()
+      : version(0) {
+  }
+};
+
+struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef ModelT NativeTableType;
+  enum {
+    VT_LANGUAGE = 4,
+    VT_VERSION = 6,
+    VT_SELECTION_FEATURE_OPTIONS = 8,
+    VT_CLASSIFICATION_FEATURE_OPTIONS = 10,
+    VT_SELECTION_MODEL = 12,
+    VT_CLASSIFICATION_MODEL = 14,
+    VT_EMBEDDING_MODEL = 16,
+    VT_REGEX_OPTIONS = 18,
+    VT_SELECTION_OPTIONS = 20,
+    VT_CLASSIFICATION_OPTIONS = 22,
+    VT_REGEX_MODEL = 24
+  };
+  const flatbuffers::String *language() const {
+    return GetPointer<const flatbuffers::String *>(VT_LANGUAGE);
+  }
+  int32_t version() const {
+    return GetField<int32_t>(VT_VERSION, 0);
+  }
+  const FeatureProcessorOptions *selection_feature_options() const {
+    return GetPointer<const FeatureProcessorOptions *>(VT_SELECTION_FEATURE_OPTIONS);
+  }
+  const FeatureProcessorOptions *classification_feature_options() const {
+    return GetPointer<const FeatureProcessorOptions *>(VT_CLASSIFICATION_FEATURE_OPTIONS);
+  }
+  const flatbuffers::Vector<uint8_t> *selection_model() const {
+    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_SELECTION_MODEL);
+  }
+  const flatbuffers::Vector<uint8_t> *classification_model() const {
+    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CLASSIFICATION_MODEL);
+  }
+  const flatbuffers::Vector<uint8_t> *embedding_model() const {
+    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_EMBEDDING_MODEL);
+  }
+  const RegexModelOptions *regex_options() const {
+    return GetPointer<const RegexModelOptions *>(VT_REGEX_OPTIONS);
+  }
+  const SelectionModelOptions *selection_options() const {
+    return GetPointer<const SelectionModelOptions *>(VT_SELECTION_OPTIONS);
+  }
+  const ClassificationModelOptions *classification_options() const {
+    return GetPointer<const ClassificationModelOptions *>(VT_CLASSIFICATION_OPTIONS);
+  }
+  const StructuredRegexModel *regex_model() const {
+    return GetPointer<const StructuredRegexModel *>(VT_REGEX_MODEL);
+  }
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           VerifyOffset(verifier, VT_LANGUAGE) &&
+           verifier.Verify(language()) &&
+           VerifyField<int32_t>(verifier, VT_VERSION) &&
+           VerifyOffset(verifier, VT_SELECTION_FEATURE_OPTIONS) &&
+           verifier.VerifyTable(selection_feature_options()) &&
+           VerifyOffset(verifier, VT_CLASSIFICATION_FEATURE_OPTIONS) &&
+           verifier.VerifyTable(classification_feature_options()) &&
+           VerifyOffset(verifier, VT_SELECTION_MODEL) &&
+           verifier.Verify(selection_model()) &&
+           VerifyOffset(verifier, VT_CLASSIFICATION_MODEL) &&
+           verifier.Verify(classification_model()) &&
+           VerifyOffset(verifier, VT_EMBEDDING_MODEL) &&
+           verifier.Verify(embedding_model()) &&
+           VerifyOffset(verifier, VT_REGEX_OPTIONS) &&
+           verifier.VerifyTable(regex_options()) &&
+           VerifyOffset(verifier, VT_SELECTION_OPTIONS) &&
+           verifier.VerifyTable(selection_options()) &&
+           VerifyOffset(verifier, VT_CLASSIFICATION_OPTIONS) &&
+           verifier.VerifyTable(classification_options()) &&
+           VerifyOffset(verifier, VT_REGEX_MODEL) &&
+           verifier.VerifyTable(regex_model()) &&
+           verifier.EndTable();
+  }
+  ModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<Model> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct ModelBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  void add_language(flatbuffers::Offset<flatbuffers::String> language) {
+    fbb_.AddOffset(Model::VT_LANGUAGE, language);
+  }
+  void add_version(int32_t version) {
+    fbb_.AddElement<int32_t>(Model::VT_VERSION, version, 0);
+  }
+  void add_selection_feature_options(flatbuffers::Offset<FeatureProcessorOptions> selection_feature_options) {
+    fbb_.AddOffset(Model::VT_SELECTION_FEATURE_OPTIONS, selection_feature_options);
+  }
+  void add_classification_feature_options(flatbuffers::Offset<FeatureProcessorOptions> classification_feature_options) {
+    fbb_.AddOffset(Model::VT_CLASSIFICATION_FEATURE_OPTIONS, classification_feature_options);
+  }
+  void add_selection_model(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> selection_model) {
+    fbb_.AddOffset(Model::VT_SELECTION_MODEL, selection_model);
+  }
+  void add_classification_model(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> classification_model) {
+    fbb_.AddOffset(Model::VT_CLASSIFICATION_MODEL, classification_model);
+  }
+  void add_embedding_model(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> embedding_model) {
+    fbb_.AddOffset(Model::VT_EMBEDDING_MODEL, embedding_model);
+  }
+  void add_regex_options(flatbuffers::Offset<RegexModelOptions> regex_options) {
+    fbb_.AddOffset(Model::VT_REGEX_OPTIONS, regex_options);
+  }
+  void add_selection_options(flatbuffers::Offset<SelectionModelOptions> selection_options) {
+    fbb_.AddOffset(Model::VT_SELECTION_OPTIONS, selection_options);
+  }
+  void add_classification_options(flatbuffers::Offset<ClassificationModelOptions> classification_options) {
+    fbb_.AddOffset(Model::VT_CLASSIFICATION_OPTIONS, classification_options);
+  }
+  void add_regex_model(flatbuffers::Offset<StructuredRegexModel> regex_model) {
+    fbb_.AddOffset(Model::VT_REGEX_MODEL, regex_model);
+  }
+  explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  ModelBuilder &operator=(const ModelBuilder &);
+  flatbuffers::Offset<Model> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<Model>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<Model> CreateModel(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    flatbuffers::Offset<flatbuffers::String> language = 0,
+    int32_t version = 0,
+    flatbuffers::Offset<FeatureProcessorOptions> selection_feature_options = 0,
+    flatbuffers::Offset<FeatureProcessorOptions> classification_feature_options = 0,
+    flatbuffers::Offset<flatbuffers::Vector<uint8_t>> selection_model = 0,
+    flatbuffers::Offset<flatbuffers::Vector<uint8_t>> classification_model = 0,
+    flatbuffers::Offset<flatbuffers::Vector<uint8_t>> embedding_model = 0,
+    flatbuffers::Offset<RegexModelOptions> regex_options = 0,
+    flatbuffers::Offset<SelectionModelOptions> selection_options = 0,
+    flatbuffers::Offset<ClassificationModelOptions> classification_options = 0,
+    flatbuffers::Offset<StructuredRegexModel> regex_model = 0) {
+  ModelBuilder builder_(_fbb);
+  builder_.add_regex_model(regex_model);
+  builder_.add_classification_options(classification_options);
+  builder_.add_selection_options(selection_options);
+  builder_.add_regex_options(regex_options);
+  builder_.add_embedding_model(embedding_model);
+  builder_.add_classification_model(classification_model);
+  builder_.add_selection_model(selection_model);
+  builder_.add_classification_feature_options(classification_feature_options);
+  builder_.add_selection_feature_options(selection_feature_options);
+  builder_.add_version(version);
+  builder_.add_language(language);
+  return builder_.Finish();
+}
+
+inline flatbuffers::Offset<Model> CreateModelDirect(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    const char *language = nullptr,
+    int32_t version = 0,
+    flatbuffers::Offset<FeatureProcessorOptions> selection_feature_options = 0,
+    flatbuffers::Offset<FeatureProcessorOptions> classification_feature_options = 0,
+    const std::vector<uint8_t> *selection_model = nullptr,
+    const std::vector<uint8_t> *classification_model = nullptr,
+    const std::vector<uint8_t> *embedding_model = nullptr,
+    flatbuffers::Offset<RegexModelOptions> regex_options = 0,
+    flatbuffers::Offset<SelectionModelOptions> selection_options = 0,
+    flatbuffers::Offset<ClassificationModelOptions> classification_options = 0,
+    flatbuffers::Offset<StructuredRegexModel> regex_model = 0) {
+  return libtextclassifier2::CreateModel(
+      _fbb,
+      language ? _fbb.CreateString(language) : 0,
+      version,
+      selection_feature_options,
+      classification_feature_options,
+      selection_model ? _fbb.CreateVector<uint8_t>(*selection_model) : 0,
+      classification_model ? _fbb.CreateVector<uint8_t>(*classification_model) : 0,
+      embedding_model ? _fbb.CreateVector<uint8_t>(*embedding_model) : 0,
+      regex_options,
+      selection_options,
+      classification_options,
+      regex_model);
+}
+
+flatbuffers::Offset<Model> CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct TokenizationCodepointRangeT : public flatbuffers::NativeTable {
+  typedef TokenizationCodepointRange TableType;
+  int32_t start;
+  int32_t end;
+  libtextclassifier2::TokenizationCodepointRange_::Role role;
+  int32_t script_id;
+  TokenizationCodepointRangeT()
+      : start(0),
+        end(0),
+        role(libtextclassifier2::TokenizationCodepointRange_::Role_DEFAULT_ROLE),
+        script_id(0) {
+  }
+};
+
+struct TokenizationCodepointRange FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef TokenizationCodepointRangeT NativeTableType;
+  enum {
+    VT_START = 4,
+    VT_END = 6,
+    VT_ROLE = 8,
+    VT_SCRIPT_ID = 10
+  };
+  int32_t start() const {
+    return GetField<int32_t>(VT_START, 0);
+  }
+  int32_t end() const {
+    return GetField<int32_t>(VT_END, 0);
+  }
+  libtextclassifier2::TokenizationCodepointRange_::Role role() const {
+    return static_cast<libtextclassifier2::TokenizationCodepointRange_::Role>(GetField<int32_t>(VT_ROLE, 0));
+  }
+  int32_t script_id() const {
+    return GetField<int32_t>(VT_SCRIPT_ID, 0);
+  }
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           VerifyField<int32_t>(verifier, VT_START) &&
+           VerifyField<int32_t>(verifier, VT_END) &&
+           VerifyField<int32_t>(verifier, VT_ROLE) &&
+           VerifyField<int32_t>(verifier, VT_SCRIPT_ID) &&
+           verifier.EndTable();
+  }
+  TokenizationCodepointRangeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(TokenizationCodepointRangeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<TokenizationCodepointRange> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TokenizationCodepointRangeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct TokenizationCodepointRangeBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  void add_start(int32_t start) {
+    fbb_.AddElement<int32_t>(TokenizationCodepointRange::VT_START, start, 0);
+  }
+  void add_end(int32_t end) {
+    fbb_.AddElement<int32_t>(TokenizationCodepointRange::VT_END, end, 0);
+  }
+  void add_role(libtextclassifier2::TokenizationCodepointRange_::Role role) {
+    fbb_.AddElement<int32_t>(TokenizationCodepointRange::VT_ROLE, static_cast<int32_t>(role), 0);
+  }
+  void add_script_id(int32_t script_id) {
+    fbb_.AddElement<int32_t>(TokenizationCodepointRange::VT_SCRIPT_ID, script_id, 0);
+  }
+  explicit TokenizationCodepointRangeBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  TokenizationCodepointRangeBuilder &operator=(const TokenizationCodepointRangeBuilder &);
+  flatbuffers::Offset<TokenizationCodepointRange> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<TokenizationCodepointRange>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<TokenizationCodepointRange> CreateTokenizationCodepointRange(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    int32_t start = 0,
+    int32_t end = 0,
+    libtextclassifier2::TokenizationCodepointRange_::Role role = libtextclassifier2::TokenizationCodepointRange_::Role_DEFAULT_ROLE,
+    int32_t script_id = 0) {
+  TokenizationCodepointRangeBuilder builder_(_fbb);
+  builder_.add_script_id(script_id);
+  builder_.add_role(role);
+  builder_.add_end(end);
+  builder_.add_start(start);
+  return builder_.Finish();
+}
+
+flatbuffers::Offset<TokenizationCodepointRange> CreateTokenizationCodepointRange(flatbuffers::FlatBufferBuilder &_fbb, const TokenizationCodepointRangeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct FeatureProcessorOptionsT : public flatbuffers::NativeTable {
+  typedef FeatureProcessorOptions TableType;
+  int32_t num_buckets;
+  int32_t embedding_size;
+  int32_t context_size;
+  int32_t max_selection_span;
+  std::vector<int32_t> chargram_orders;
+  int32_t max_word_length;
+  bool unicode_aware_features;
+  bool extract_case_feature;
+  bool extract_selection_mask_feature;
+  std::vector<std::string> regexp_feature;
+  bool remap_digits;
+  bool lowercase_tokens;
+  bool selection_reduced_output_space;
+  std::vector<std::string> collections;
+  int32_t default_collection;
+  bool only_use_line_with_click;
+  bool split_tokens_on_selection_boundaries;
+  std::vector<std::unique_ptr<TokenizationCodepointRangeT>> tokenization_codepoint_config;
+  libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod center_token_selection_method;
+  bool snap_label_span_boundaries_to_containing_tokens;
+  std::vector<std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::CodepointRangeT>> supported_codepoint_ranges;
+  std::vector<std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::CodepointRangeT>> internal_tokenizer_codepoint_ranges;
+  float min_supported_codepoint_ratio;
+  int32_t feature_version;
+  libtextclassifier2::FeatureProcessorOptions_::TokenizationType tokenization_type;
+  bool icu_preserve_whitespace_tokens;
+  std::vector<int32_t> ignored_span_boundary_codepoints;
+  bool click_random_token_in_selection;
+  std::vector<std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::CollectionMapEntryT>> alternative_collection_map;
+  std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeaturesT> bounds_sensitive_features;
+  bool split_selection_candidates;
+  std::vector<std::string> allowed_chargrams;
+  bool tokenize_on_script_change;
+  FeatureProcessorOptionsT()
+      : num_buckets(-1),
+        embedding_size(-1),
+        context_size(-1),
+        max_selection_span(-1),
+        max_word_length(20),
+        unicode_aware_features(false),
+        extract_case_feature(false),
+        extract_selection_mask_feature(false),
+        remap_digits(false),
+        lowercase_tokens(false),
+        selection_reduced_output_space(false),
+        default_collection(-1),
+        only_use_line_with_click(false),
+        split_tokens_on_selection_boundaries(false),
+        center_token_selection_method(libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD),
+        snap_label_span_boundaries_to_containing_tokens(false),
+        min_supported_codepoint_ratio(0.0f),
+        feature_version(0),
+        tokenization_type(libtextclassifier2::FeatureProcessorOptions_::TokenizationType_INVALID_TOKENIZATION_TYPE),
+        icu_preserve_whitespace_tokens(false),
+        click_random_token_in_selection(false),
+        split_selection_candidates(false),
+        tokenize_on_script_change(false) {
+  }
+};
+
+struct FeatureProcessorOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef FeatureProcessorOptionsT NativeTableType;
+  enum {
+    VT_NUM_BUCKETS = 4,
+    VT_EMBEDDING_SIZE = 6,
+    VT_CONTEXT_SIZE = 8,
+    VT_MAX_SELECTION_SPAN = 10,
+    VT_CHARGRAM_ORDERS = 12,
+    VT_MAX_WORD_LENGTH = 14,
+    VT_UNICODE_AWARE_FEATURES = 16,
+    VT_EXTRACT_CASE_FEATURE = 18,
+    VT_EXTRACT_SELECTION_MASK_FEATURE = 20,
+    VT_REGEXP_FEATURE = 22,
+    VT_REMAP_DIGITS = 24,
+    VT_LOWERCASE_TOKENS = 26,
+    VT_SELECTION_REDUCED_OUTPUT_SPACE = 28,
+    VT_COLLECTIONS = 30,
+    VT_DEFAULT_COLLECTION = 32,
+    VT_ONLY_USE_LINE_WITH_CLICK = 34,
+    VT_SPLIT_TOKENS_ON_SELECTION_BOUNDARIES = 36,
+    VT_TOKENIZATION_CODEPOINT_CONFIG = 38,
+    VT_CENTER_TOKEN_SELECTION_METHOD = 40,
+    VT_SNAP_LABEL_SPAN_BOUNDARIES_TO_CONTAINING_TOKENS = 42,
+    VT_SUPPORTED_CODEPOINT_RANGES = 44,
+    VT_INTERNAL_TOKENIZER_CODEPOINT_RANGES = 46,
+    VT_MIN_SUPPORTED_CODEPOINT_RATIO = 48,
+    VT_FEATURE_VERSION = 50,
+    VT_TOKENIZATION_TYPE = 52,
+    VT_ICU_PRESERVE_WHITESPACE_TOKENS = 54,
+    VT_IGNORED_SPAN_BOUNDARY_CODEPOINTS = 56,
+    VT_CLICK_RANDOM_TOKEN_IN_SELECTION = 58,
+    VT_ALTERNATIVE_COLLECTION_MAP = 60,
+    VT_BOUNDS_SENSITIVE_FEATURES = 62,
+    VT_SPLIT_SELECTION_CANDIDATES = 64,
+    VT_ALLOWED_CHARGRAMS = 66,
+    VT_TOKENIZE_ON_SCRIPT_CHANGE = 68
+  };
+  int32_t num_buckets() const {
+    return GetField<int32_t>(VT_NUM_BUCKETS, -1);
+  }
+  int32_t embedding_size() const {
+    return GetField<int32_t>(VT_EMBEDDING_SIZE, -1);
+  }
+  int32_t context_size() const {
+    return GetField<int32_t>(VT_CONTEXT_SIZE, -1);
+  }
+  int32_t max_selection_span() const {
+    return GetField<int32_t>(VT_MAX_SELECTION_SPAN, -1);
+  }
+  const flatbuffers::Vector<int32_t> *chargram_orders() const {
+    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_CHARGRAM_ORDERS);
+  }
+  int32_t max_word_length() const {
+    return GetField<int32_t>(VT_MAX_WORD_LENGTH, 20);
+  }
+  bool unicode_aware_features() const {
+    return GetField<uint8_t>(VT_UNICODE_AWARE_FEATURES, 0) != 0;
+  }
+  bool extract_case_feature() const {
+    return GetField<uint8_t>(VT_EXTRACT_CASE_FEATURE, 0) != 0;
+  }
+  bool extract_selection_mask_feature() const {
+    return GetField<uint8_t>(VT_EXTRACT_SELECTION_MASK_FEATURE, 0) != 0;
+  }
+  const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *regexp_feature() const {
+    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_REGEXP_FEATURE);
+  }
+  bool remap_digits() const {
+    return GetField<uint8_t>(VT_REMAP_DIGITS, 0) != 0;
+  }
+  bool lowercase_tokens() const {
+    return GetField<uint8_t>(VT_LOWERCASE_TOKENS, 0) != 0;
+  }
+  bool selection_reduced_output_space() const {
+    return GetField<uint8_t>(VT_SELECTION_REDUCED_OUTPUT_SPACE, 0) != 0;
+  }
+  const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *collections() const {
+    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_COLLECTIONS);
+  }
+  int32_t default_collection() const {
+    return GetField<int32_t>(VT_DEFAULT_COLLECTION, -1);
+  }
+  bool only_use_line_with_click() const {
+    return GetField<uint8_t>(VT_ONLY_USE_LINE_WITH_CLICK, 0) != 0;
+  }
+  bool split_tokens_on_selection_boundaries() const {
+    return GetField<uint8_t>(VT_SPLIT_TOKENS_ON_SELECTION_BOUNDARIES, 0) != 0;
+  }
+  const flatbuffers::Vector<flatbuffers::Offset<TokenizationCodepointRange>> *tokenization_codepoint_config() const {
+    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<TokenizationCodepointRange>> *>(VT_TOKENIZATION_CODEPOINT_CONFIG);
+  }
+  libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod center_token_selection_method() const {
+    return static_cast<libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod>(GetField<int32_t>(VT_CENTER_TOKEN_SELECTION_METHOD, 0));
+  }
+  bool snap_label_span_boundaries_to_containing_tokens() const {
+    return GetField<uint8_t>(VT_SNAP_LABEL_SPAN_BOUNDARIES_TO_CONTAINING_TOKENS, 0) != 0;
+  }
+  const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> *supported_codepoint_ranges() const {
+    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> *>(VT_SUPPORTED_CODEPOINT_RANGES);
+  }
+  const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> *internal_tokenizer_codepoint_ranges() const {
+    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> *>(VT_INTERNAL_TOKENIZER_CODEPOINT_RANGES);
+  }
+  float min_supported_codepoint_ratio() const {
+    return GetField<float>(VT_MIN_SUPPORTED_CODEPOINT_RATIO, 0.0f);
+  }
+  int32_t feature_version() const {
+    return GetField<int32_t>(VT_FEATURE_VERSION, 0);
+  }
+  libtextclassifier2::FeatureProcessorOptions_::TokenizationType tokenization_type() const {
+    return static_cast<libtextclassifier2::FeatureProcessorOptions_::TokenizationType>(GetField<int32_t>(VT_TOKENIZATION_TYPE, 0));
+  }
+  bool icu_preserve_whitespace_tokens() const {
+    return GetField<uint8_t>(VT_ICU_PRESERVE_WHITESPACE_TOKENS, 0) != 0;
+  }
+  const flatbuffers::Vector<int32_t> *ignored_span_boundary_codepoints() const {
+    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_IGNORED_SPAN_BOUNDARY_CODEPOINTS);
+  }
+  bool click_random_token_in_selection() const {
+    return GetField<uint8_t>(VT_CLICK_RANDOM_TOKEN_IN_SELECTION, 0) != 0;
+  }
+  const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CollectionMapEntry>> *alternative_collection_map() const {
+    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CollectionMapEntry>> *>(VT_ALTERNATIVE_COLLECTION_MAP);
+  }
+  const libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeatures *bounds_sensitive_features() const {
+    return GetPointer<const libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeatures *>(VT_BOUNDS_SENSITIVE_FEATURES);
+  }
+  bool split_selection_candidates() const {
+    return GetField<uint8_t>(VT_SPLIT_SELECTION_CANDIDATES, 0) != 0;
+  }
+  const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *allowed_chargrams() const {
+    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>> *>(VT_ALLOWED_CHARGRAMS);
+  }
+  bool tokenize_on_script_change() const {
+    return GetField<uint8_t>(VT_TOKENIZE_ON_SCRIPT_CHANGE, 0) != 0;
+  }
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           VerifyField<int32_t>(verifier, VT_NUM_BUCKETS) &&
+           VerifyField<int32_t>(verifier, VT_EMBEDDING_SIZE) &&
+           VerifyField<int32_t>(verifier, VT_CONTEXT_SIZE) &&
+           VerifyField<int32_t>(verifier, VT_MAX_SELECTION_SPAN) &&
+           VerifyOffset(verifier, VT_CHARGRAM_ORDERS) &&
+           verifier.Verify(chargram_orders()) &&
+           VerifyField<int32_t>(verifier, VT_MAX_WORD_LENGTH) &&
+           VerifyField<uint8_t>(verifier, VT_UNICODE_AWARE_FEATURES) &&
+           VerifyField<uint8_t>(verifier, VT_EXTRACT_CASE_FEATURE) &&
+           VerifyField<uint8_t>(verifier, VT_EXTRACT_SELECTION_MASK_FEATURE) &&
+           VerifyOffset(verifier, VT_REGEXP_FEATURE) &&
+           verifier.Verify(regexp_feature()) &&
+           verifier.VerifyVectorOfStrings(regexp_feature()) &&
+           VerifyField<uint8_t>(verifier, VT_REMAP_DIGITS) &&
+           VerifyField<uint8_t>(verifier, VT_LOWERCASE_TOKENS) &&
+           VerifyField<uint8_t>(verifier, VT_SELECTION_REDUCED_OUTPUT_SPACE) &&
+           VerifyOffset(verifier, VT_COLLECTIONS) &&
+           verifier.Verify(collections()) &&
+           verifier.VerifyVectorOfStrings(collections()) &&
+           VerifyField<int32_t>(verifier, VT_DEFAULT_COLLECTION) &&
+           VerifyField<uint8_t>(verifier, VT_ONLY_USE_LINE_WITH_CLICK) &&
+           VerifyField<uint8_t>(verifier, VT_SPLIT_TOKENS_ON_SELECTION_BOUNDARIES) &&
+           VerifyOffset(verifier, VT_TOKENIZATION_CODEPOINT_CONFIG) &&
+           verifier.Verify(tokenization_codepoint_config()) &&
+           verifier.VerifyVectorOfTables(tokenization_codepoint_config()) &&
+           VerifyField<int32_t>(verifier, VT_CENTER_TOKEN_SELECTION_METHOD) &&
+           VerifyField<uint8_t>(verifier, VT_SNAP_LABEL_SPAN_BOUNDARIES_TO_CONTAINING_TOKENS) &&
+           VerifyOffset(verifier, VT_SUPPORTED_CODEPOINT_RANGES) &&
+           verifier.Verify(supported_codepoint_ranges()) &&
+           verifier.VerifyVectorOfTables(supported_codepoint_ranges()) &&
+           VerifyOffset(verifier, VT_INTERNAL_TOKENIZER_CODEPOINT_RANGES) &&
+           verifier.Verify(internal_tokenizer_codepoint_ranges()) &&
+           verifier.VerifyVectorOfTables(internal_tokenizer_codepoint_ranges()) &&
+           VerifyField<float>(verifier, VT_MIN_SUPPORTED_CODEPOINT_RATIO) &&
+           VerifyField<int32_t>(verifier, VT_FEATURE_VERSION) &&
+           VerifyField<int32_t>(verifier, VT_TOKENIZATION_TYPE) &&
+           VerifyField<uint8_t>(verifier, VT_ICU_PRESERVE_WHITESPACE_TOKENS) &&
+           VerifyOffset(verifier, VT_IGNORED_SPAN_BOUNDARY_CODEPOINTS) &&
+           verifier.Verify(ignored_span_boundary_codepoints()) &&
+           VerifyField<uint8_t>(verifier, VT_CLICK_RANDOM_TOKEN_IN_SELECTION) &&
+           VerifyOffset(verifier, VT_ALTERNATIVE_COLLECTION_MAP) &&
+           verifier.Verify(alternative_collection_map()) &&
+           verifier.VerifyVectorOfTables(alternative_collection_map()) &&
+           VerifyOffset(verifier, VT_BOUNDS_SENSITIVE_FEATURES) &&
+           verifier.VerifyTable(bounds_sensitive_features()) &&
+           VerifyField<uint8_t>(verifier, VT_SPLIT_SELECTION_CANDIDATES) &&
+           VerifyOffset(verifier, VT_ALLOWED_CHARGRAMS) &&
+           verifier.Verify(allowed_chargrams()) &&
+           verifier.VerifyVectorOfStrings(allowed_chargrams()) &&
+           VerifyField<uint8_t>(verifier, VT_TOKENIZE_ON_SCRIPT_CHANGE) &&
+           verifier.EndTable();
+  }
+  FeatureProcessorOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(FeatureProcessorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<FeatureProcessorOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const FeatureProcessorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct FeatureProcessorOptionsBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  void add_num_buckets(int32_t num_buckets) {
+    fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_NUM_BUCKETS, num_buckets, -1);
+  }
+  void add_embedding_size(int32_t embedding_size) {
+    fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_EMBEDDING_SIZE, embedding_size, -1);
+  }
+  void add_context_size(int32_t context_size) {
+    fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_CONTEXT_SIZE, context_size, -1);
+  }
+  void add_max_selection_span(int32_t max_selection_span) {
+    fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_MAX_SELECTION_SPAN, max_selection_span, -1);
+  }
+  void add_chargram_orders(flatbuffers::Offset<flatbuffers::Vector<int32_t>> chargram_orders) {
+    fbb_.AddOffset(FeatureProcessorOptions::VT_CHARGRAM_ORDERS, chargram_orders);
+  }
+  void add_max_word_length(int32_t max_word_length) {
+    fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_MAX_WORD_LENGTH, max_word_length, 20);
+  }
+  void add_unicode_aware_features(bool unicode_aware_features) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_UNICODE_AWARE_FEATURES, static_cast<uint8_t>(unicode_aware_features), 0);
+  }
+  void add_extract_case_feature(bool extract_case_feature) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_EXTRACT_CASE_FEATURE, static_cast<uint8_t>(extract_case_feature), 0);
+  }
+  void add_extract_selection_mask_feature(bool extract_selection_mask_feature) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_EXTRACT_SELECTION_MASK_FEATURE, static_cast<uint8_t>(extract_selection_mask_feature), 0);
+  }
+  void add_regexp_feature(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> regexp_feature) {
+    fbb_.AddOffset(FeatureProcessorOptions::VT_REGEXP_FEATURE, regexp_feature);
+  }
+  void add_remap_digits(bool remap_digits) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_REMAP_DIGITS, static_cast<uint8_t>(remap_digits), 0);
+  }
+  void add_lowercase_tokens(bool lowercase_tokens) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_LOWERCASE_TOKENS, static_cast<uint8_t>(lowercase_tokens), 0);
+  }
+  void add_selection_reduced_output_space(bool selection_reduced_output_space) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_SELECTION_REDUCED_OUTPUT_SPACE, static_cast<uint8_t>(selection_reduced_output_space), 0);
+  }
+  void add_collections(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> collections) {
+    fbb_.AddOffset(FeatureProcessorOptions::VT_COLLECTIONS, collections);
+  }
+  void add_default_collection(int32_t default_collection) {
+    fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_DEFAULT_COLLECTION, default_collection, -1);
+  }
+  void add_only_use_line_with_click(bool only_use_line_with_click) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_ONLY_USE_LINE_WITH_CLICK, static_cast<uint8_t>(only_use_line_with_click), 0);
+  }
+  void add_split_tokens_on_selection_boundaries(bool split_tokens_on_selection_boundaries) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_SPLIT_TOKENS_ON_SELECTION_BOUNDARIES, static_cast<uint8_t>(split_tokens_on_selection_boundaries), 0);
+  }
+  void add_tokenization_codepoint_config(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<TokenizationCodepointRange>>> tokenization_codepoint_config) {
+    fbb_.AddOffset(FeatureProcessorOptions::VT_TOKENIZATION_CODEPOINT_CONFIG, tokenization_codepoint_config);
+  }
+  void add_center_token_selection_method(libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod center_token_selection_method) {
+    fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_CENTER_TOKEN_SELECTION_METHOD, static_cast<int32_t>(center_token_selection_method), 0);
+  }
+  void add_snap_label_span_boundaries_to_containing_tokens(bool snap_label_span_boundaries_to_containing_tokens) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_SNAP_LABEL_SPAN_BOUNDARIES_TO_CONTAINING_TOKENS, static_cast<uint8_t>(snap_label_span_boundaries_to_containing_tokens), 0);
+  }
+  void add_supported_codepoint_ranges(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>>> supported_codepoint_ranges) {
+    fbb_.AddOffset(FeatureProcessorOptions::VT_SUPPORTED_CODEPOINT_RANGES, supported_codepoint_ranges);
+  }
+  void add_internal_tokenizer_codepoint_ranges(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>>> internal_tokenizer_codepoint_ranges) {
+    fbb_.AddOffset(FeatureProcessorOptions::VT_INTERNAL_TOKENIZER_CODEPOINT_RANGES, internal_tokenizer_codepoint_ranges);
+  }
+  void add_min_supported_codepoint_ratio(float min_supported_codepoint_ratio) {
+    fbb_.AddElement<float>(FeatureProcessorOptions::VT_MIN_SUPPORTED_CODEPOINT_RATIO, min_supported_codepoint_ratio, 0.0f);
+  }
+  void add_feature_version(int32_t feature_version) {
+    fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_FEATURE_VERSION, feature_version, 0);
+  }
+  void add_tokenization_type(libtextclassifier2::FeatureProcessorOptions_::TokenizationType tokenization_type) {
+    fbb_.AddElement<int32_t>(FeatureProcessorOptions::VT_TOKENIZATION_TYPE, static_cast<int32_t>(tokenization_type), 0);
+  }
+  void add_icu_preserve_whitespace_tokens(bool icu_preserve_whitespace_tokens) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_ICU_PRESERVE_WHITESPACE_TOKENS, static_cast<uint8_t>(icu_preserve_whitespace_tokens), 0);
+  }
+  void add_ignored_span_boundary_codepoints(flatbuffers::Offset<flatbuffers::Vector<int32_t>> ignored_span_boundary_codepoints) {
+    fbb_.AddOffset(FeatureProcessorOptions::VT_IGNORED_SPAN_BOUNDARY_CODEPOINTS, ignored_span_boundary_codepoints);
+  }
+  void add_click_random_token_in_selection(bool click_random_token_in_selection) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_CLICK_RANDOM_TOKEN_IN_SELECTION, static_cast<uint8_t>(click_random_token_in_selection), 0);
+  }
+  void add_alternative_collection_map(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CollectionMapEntry>>> alternative_collection_map) {
+    fbb_.AddOffset(FeatureProcessorOptions::VT_ALTERNATIVE_COLLECTION_MAP, alternative_collection_map);
+  }
+  void add_bounds_sensitive_features(flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeatures> bounds_sensitive_features) {
+    fbb_.AddOffset(FeatureProcessorOptions::VT_BOUNDS_SENSITIVE_FEATURES, bounds_sensitive_features);
+  }
+  void add_split_selection_candidates(bool split_selection_candidates) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_SPLIT_SELECTION_CANDIDATES, static_cast<uint8_t>(split_selection_candidates), 0);
+  }
+  void add_allowed_chargrams(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> allowed_chargrams) {
+    fbb_.AddOffset(FeatureProcessorOptions::VT_ALLOWED_CHARGRAMS, allowed_chargrams);
+  }
+  void add_tokenize_on_script_change(bool tokenize_on_script_change) {
+    fbb_.AddElement<uint8_t>(FeatureProcessorOptions::VT_TOKENIZE_ON_SCRIPT_CHANGE, static_cast<uint8_t>(tokenize_on_script_change), 0);
+  }
+  explicit FeatureProcessorOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  FeatureProcessorOptionsBuilder &operator=(const FeatureProcessorOptionsBuilder &);
+  flatbuffers::Offset<FeatureProcessorOptions> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<FeatureProcessorOptions>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<FeatureProcessorOptions> CreateFeatureProcessorOptions(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    int32_t num_buckets = -1,
+    int32_t embedding_size = -1,
+    int32_t context_size = -1,
+    int32_t max_selection_span = -1,
+    flatbuffers::Offset<flatbuffers::Vector<int32_t>> chargram_orders = 0,
+    int32_t max_word_length = 20,
+    bool unicode_aware_features = false,
+    bool extract_case_feature = false,
+    bool extract_selection_mask_feature = false,
+    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> regexp_feature = 0,
+    bool remap_digits = false,
+    bool lowercase_tokens = false,
+    bool selection_reduced_output_space = false,
+    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> collections = 0,
+    int32_t default_collection = -1,
+    bool only_use_line_with_click = false,
+    bool split_tokens_on_selection_boundaries = false,
+    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<TokenizationCodepointRange>>> tokenization_codepoint_config = 0,
+    libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod center_token_selection_method = libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD,
+    bool snap_label_span_boundaries_to_containing_tokens = false,
+    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>>> supported_codepoint_ranges = 0,
+    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>>> internal_tokenizer_codepoint_ranges = 0,
+    float min_supported_codepoint_ratio = 0.0f,
+    int32_t feature_version = 0,
+    libtextclassifier2::FeatureProcessorOptions_::TokenizationType tokenization_type = libtextclassifier2::FeatureProcessorOptions_::TokenizationType_INVALID_TOKENIZATION_TYPE,
+    bool icu_preserve_whitespace_tokens = false,
+    flatbuffers::Offset<flatbuffers::Vector<int32_t>> ignored_span_boundary_codepoints = 0,
+    bool click_random_token_in_selection = false,
+    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CollectionMapEntry>>> alternative_collection_map = 0,
+    flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeatures> bounds_sensitive_features = 0,
+    bool split_selection_candidates = false,
+    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<flatbuffers::String>>> allowed_chargrams = 0,
+    bool tokenize_on_script_change = false) {
+  FeatureProcessorOptionsBuilder builder_(_fbb);
+  builder_.add_allowed_chargrams(allowed_chargrams);
+  builder_.add_bounds_sensitive_features(bounds_sensitive_features);
+  builder_.add_alternative_collection_map(alternative_collection_map);
+  builder_.add_ignored_span_boundary_codepoints(ignored_span_boundary_codepoints);
+  builder_.add_tokenization_type(tokenization_type);
+  builder_.add_feature_version(feature_version);
+  builder_.add_min_supported_codepoint_ratio(min_supported_codepoint_ratio);
+  builder_.add_internal_tokenizer_codepoint_ranges(internal_tokenizer_codepoint_ranges);
+  builder_.add_supported_codepoint_ranges(supported_codepoint_ranges);
+  builder_.add_center_token_selection_method(center_token_selection_method);
+  builder_.add_tokenization_codepoint_config(tokenization_codepoint_config);
+  builder_.add_default_collection(default_collection);
+  builder_.add_collections(collections);
+  builder_.add_regexp_feature(regexp_feature);
+  builder_.add_max_word_length(max_word_length);
+  builder_.add_chargram_orders(chargram_orders);
+  builder_.add_max_selection_span(max_selection_span);
+  builder_.add_context_size(context_size);
+  builder_.add_embedding_size(embedding_size);
+  builder_.add_num_buckets(num_buckets);
+  builder_.add_tokenize_on_script_change(tokenize_on_script_change);
+  builder_.add_split_selection_candidates(split_selection_candidates);
+  builder_.add_click_random_token_in_selection(click_random_token_in_selection);
+  builder_.add_icu_preserve_whitespace_tokens(icu_preserve_whitespace_tokens);
+  builder_.add_snap_label_span_boundaries_to_containing_tokens(snap_label_span_boundaries_to_containing_tokens);
+  builder_.add_split_tokens_on_selection_boundaries(split_tokens_on_selection_boundaries);
+  builder_.add_only_use_line_with_click(only_use_line_with_click);
+  builder_.add_selection_reduced_output_space(selection_reduced_output_space);
+  builder_.add_lowercase_tokens(lowercase_tokens);
+  builder_.add_remap_digits(remap_digits);
+  builder_.add_extract_selection_mask_feature(extract_selection_mask_feature);
+  builder_.add_extract_case_feature(extract_case_feature);
+  builder_.add_unicode_aware_features(unicode_aware_features);
+  return builder_.Finish();
+}
+
+inline flatbuffers::Offset<FeatureProcessorOptions> CreateFeatureProcessorOptionsDirect(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    int32_t num_buckets = -1,
+    int32_t embedding_size = -1,
+    int32_t context_size = -1,
+    int32_t max_selection_span = -1,
+    const std::vector<int32_t> *chargram_orders = nullptr,
+    int32_t max_word_length = 20,
+    bool unicode_aware_features = false,
+    bool extract_case_feature = false,
+    bool extract_selection_mask_feature = false,
+    const std::vector<flatbuffers::Offset<flatbuffers::String>> *regexp_feature = nullptr,
+    bool remap_digits = false,
+    bool lowercase_tokens = false,
+    bool selection_reduced_output_space = false,
+    const std::vector<flatbuffers::Offset<flatbuffers::String>> *collections = nullptr,
+    int32_t default_collection = -1,
+    bool only_use_line_with_click = false,
+    bool split_tokens_on_selection_boundaries = false,
+    const std::vector<flatbuffers::Offset<TokenizationCodepointRange>> *tokenization_codepoint_config = nullptr,
+    libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod center_token_selection_method = libtextclassifier2::FeatureProcessorOptions_::CenterTokenSelectionMethod_DEFAULT_CENTER_TOKEN_METHOD,
+    bool snap_label_span_boundaries_to_containing_tokens = false,
+    const std::vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> *supported_codepoint_ranges = nullptr,
+    const std::vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> *internal_tokenizer_codepoint_ranges = nullptr,
+    float min_supported_codepoint_ratio = 0.0f,
+    int32_t feature_version = 0,
+    libtextclassifier2::FeatureProcessorOptions_::TokenizationType tokenization_type = libtextclassifier2::FeatureProcessorOptions_::TokenizationType_INVALID_TOKENIZATION_TYPE,
+    bool icu_preserve_whitespace_tokens = false,
+    const std::vector<int32_t> *ignored_span_boundary_codepoints = nullptr,
+    bool click_random_token_in_selection = false,
+    const std::vector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CollectionMapEntry>> *alternative_collection_map = nullptr,
+    flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeatures> bounds_sensitive_features = 0,
+    bool split_selection_candidates = false,
+    const std::vector<flatbuffers::Offset<flatbuffers::String>> *allowed_chargrams = nullptr,
+    bool tokenize_on_script_change = false) {
+  return libtextclassifier2::CreateFeatureProcessorOptions(
+      _fbb,
+      num_buckets,
+      embedding_size,
+      context_size,
+      max_selection_span,
+      chargram_orders ? _fbb.CreateVector<int32_t>(*chargram_orders) : 0,
+      max_word_length,
+      unicode_aware_features,
+      extract_case_feature,
+      extract_selection_mask_feature,
+      regexp_feature ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*regexp_feature) : 0,
+      remap_digits,
+      lowercase_tokens,
+      selection_reduced_output_space,
+      collections ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*collections) : 0,
+      default_collection,
+      only_use_line_with_click,
+      split_tokens_on_selection_boundaries,
+      tokenization_codepoint_config ? _fbb.CreateVector<flatbuffers::Offset<TokenizationCodepointRange>>(*tokenization_codepoint_config) : 0,
+      center_token_selection_method,
+      snap_label_span_boundaries_to_containing_tokens,
+      supported_codepoint_ranges ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>>(*supported_codepoint_ranges) : 0,
+      internal_tokenizer_codepoint_ranges ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>>(*internal_tokenizer_codepoint_ranges) : 0,
+      min_supported_codepoint_ratio,
+      feature_version,
+      tokenization_type,
+      icu_preserve_whitespace_tokens,
+      ignored_span_boundary_codepoints ? _fbb.CreateVector<int32_t>(*ignored_span_boundary_codepoints) : 0,
+      click_random_token_in_selection,
+      alternative_collection_map ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CollectionMapEntry>>(*alternative_collection_map) : 0,
+      bounds_sensitive_features,
+      split_selection_candidates,
+      allowed_chargrams ? _fbb.CreateVector<flatbuffers::Offset<flatbuffers::String>>(*allowed_chargrams) : 0,
+      tokenize_on_script_change);
+}
+
+flatbuffers::Offset<FeatureProcessorOptions> CreateFeatureProcessorOptions(flatbuffers::FlatBufferBuilder &_fbb, const FeatureProcessorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+namespace FeatureProcessorOptions_ {
+
+struct CodepointRangeT : public flatbuffers::NativeTable {
+  typedef CodepointRange TableType;
+  int32_t start;
+  int32_t end;
+  CodepointRangeT()
+      : start(0),
+        end(0) {
+  }
+};
+
+struct CodepointRange FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef CodepointRangeT NativeTableType;
+  enum {
+    VT_START = 4,
+    VT_END = 6
+  };
+  int32_t start() const {
+    return GetField<int32_t>(VT_START, 0);
+  }
+  int32_t end() const {
+    return GetField<int32_t>(VT_END, 0);
+  }
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           VerifyField<int32_t>(verifier, VT_START) &&
+           VerifyField<int32_t>(verifier, VT_END) &&
+           verifier.EndTable();
+  }
+  CodepointRangeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(CodepointRangeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<CodepointRange> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CodepointRangeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct CodepointRangeBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  void add_start(int32_t start) {
+    fbb_.AddElement<int32_t>(CodepointRange::VT_START, start, 0);
+  }
+  void add_end(int32_t end) {
+    fbb_.AddElement<int32_t>(CodepointRange::VT_END, end, 0);
+  }
+  explicit CodepointRangeBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  CodepointRangeBuilder &operator=(const CodepointRangeBuilder &);
+  flatbuffers::Offset<CodepointRange> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<CodepointRange>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<CodepointRange> CreateCodepointRange(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    int32_t start = 0,
+    int32_t end = 0) {
+  CodepointRangeBuilder builder_(_fbb);
+  builder_.add_end(end);
+  builder_.add_start(start);
+  return builder_.Finish();
+}
+
+flatbuffers::Offset<CodepointRange> CreateCodepointRange(flatbuffers::FlatBufferBuilder &_fbb, const CodepointRangeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct CollectionMapEntryT : public flatbuffers::NativeTable {
+  typedef CollectionMapEntry TableType;
+  std::string key;
+  std::string value;
+  CollectionMapEntryT() {
+  }
+};
+
+struct CollectionMapEntry FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef CollectionMapEntryT NativeTableType;
+  enum {
+    VT_KEY = 4,
+    VT_VALUE = 6
+  };
+  const flatbuffers::String *key() const {
+    return GetPointer<const flatbuffers::String *>(VT_KEY);
+  }
+  const flatbuffers::String *value() const {
+    return GetPointer<const flatbuffers::String *>(VT_VALUE);
+  }
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           VerifyOffset(verifier, VT_KEY) &&
+           verifier.Verify(key()) &&
+           VerifyOffset(verifier, VT_VALUE) &&
+           verifier.Verify(value()) &&
+           verifier.EndTable();
+  }
+  CollectionMapEntryT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(CollectionMapEntryT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<CollectionMapEntry> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CollectionMapEntryT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct CollectionMapEntryBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  void add_key(flatbuffers::Offset<flatbuffers::String> key) {
+    fbb_.AddOffset(CollectionMapEntry::VT_KEY, key);
+  }
+  void add_value(flatbuffers::Offset<flatbuffers::String> value) {
+    fbb_.AddOffset(CollectionMapEntry::VT_VALUE, value);
+  }
+  explicit CollectionMapEntryBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  CollectionMapEntryBuilder &operator=(const CollectionMapEntryBuilder &);
+  flatbuffers::Offset<CollectionMapEntry> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<CollectionMapEntry>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<CollectionMapEntry> CreateCollectionMapEntry(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    flatbuffers::Offset<flatbuffers::String> key = 0,
+    flatbuffers::Offset<flatbuffers::String> value = 0) {
+  CollectionMapEntryBuilder builder_(_fbb);
+  builder_.add_value(value);
+  builder_.add_key(key);
+  return builder_.Finish();
+}
+
+inline flatbuffers::Offset<CollectionMapEntry> CreateCollectionMapEntryDirect(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    const char *key = nullptr,
+    const char *value = nullptr) {
+  return libtextclassifier2::FeatureProcessorOptions_::CreateCollectionMapEntry(
+      _fbb,
+      key ? _fbb.CreateString(key) : 0,
+      value ? _fbb.CreateString(value) : 0);
+}
+
+flatbuffers::Offset<CollectionMapEntry> CreateCollectionMapEntry(flatbuffers::FlatBufferBuilder &_fbb, const CollectionMapEntryT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+struct BoundsSensitiveFeaturesT : public flatbuffers::NativeTable {
+  typedef BoundsSensitiveFeatures TableType;
+  bool enabled;
+  int32_t num_tokens_before;
+  int32_t num_tokens_inside_left;
+  int32_t num_tokens_inside_right;
+  int32_t num_tokens_after;
+  bool include_inside_bag;
+  bool include_inside_length;
+  BoundsSensitiveFeaturesT()
+      : enabled(false),
+        num_tokens_before(0),
+        num_tokens_inside_left(0),
+        num_tokens_inside_right(0),
+        num_tokens_after(0),
+        include_inside_bag(false),
+        include_inside_length(false) {
+  }
+};
+
+struct BoundsSensitiveFeatures FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
+  typedef BoundsSensitiveFeaturesT NativeTableType;
+  enum {
+    VT_ENABLED = 4,
+    VT_NUM_TOKENS_BEFORE = 6,
+    VT_NUM_TOKENS_INSIDE_LEFT = 8,
+    VT_NUM_TOKENS_INSIDE_RIGHT = 10,
+    VT_NUM_TOKENS_AFTER = 12,
+    VT_INCLUDE_INSIDE_BAG = 14,
+    VT_INCLUDE_INSIDE_LENGTH = 16
+  };
+  bool enabled() const {
+    return GetField<uint8_t>(VT_ENABLED, 0) != 0;
+  }
+  int32_t num_tokens_before() const {
+    return GetField<int32_t>(VT_NUM_TOKENS_BEFORE, 0);
+  }
+  int32_t num_tokens_inside_left() const {
+    return GetField<int32_t>(VT_NUM_TOKENS_INSIDE_LEFT, 0);
+  }
+  int32_t num_tokens_inside_right() const {
+    return GetField<int32_t>(VT_NUM_TOKENS_INSIDE_RIGHT, 0);
+  }
+  int32_t num_tokens_after() const {
+    return GetField<int32_t>(VT_NUM_TOKENS_AFTER, 0);
+  }
+  bool include_inside_bag() const {
+    return GetField<uint8_t>(VT_INCLUDE_INSIDE_BAG, 0) != 0;
+  }
+  bool include_inside_length() const {
+    return GetField<uint8_t>(VT_INCLUDE_INSIDE_LENGTH, 0) != 0;
+  }
+  bool Verify(flatbuffers::Verifier &verifier) const {
+    return VerifyTableStart(verifier) &&
+           VerifyField<uint8_t>(verifier, VT_ENABLED) &&
+           VerifyField<int32_t>(verifier, VT_NUM_TOKENS_BEFORE) &&
+           VerifyField<int32_t>(verifier, VT_NUM_TOKENS_INSIDE_LEFT) &&
+           VerifyField<int32_t>(verifier, VT_NUM_TOKENS_INSIDE_RIGHT) &&
+           VerifyField<int32_t>(verifier, VT_NUM_TOKENS_AFTER) &&
+           VerifyField<uint8_t>(verifier, VT_INCLUDE_INSIDE_BAG) &&
+           VerifyField<uint8_t>(verifier, VT_INCLUDE_INSIDE_LENGTH) &&
+           verifier.EndTable();
+  }
+  BoundsSensitiveFeaturesT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  void UnPackTo(BoundsSensitiveFeaturesT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
+  static flatbuffers::Offset<BoundsSensitiveFeatures> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BoundsSensitiveFeaturesT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+};
+
+struct BoundsSensitiveFeaturesBuilder {
+  flatbuffers::FlatBufferBuilder &fbb_;
+  flatbuffers::uoffset_t start_;
+  void add_enabled(bool enabled) {
+    fbb_.AddElement<uint8_t>(BoundsSensitiveFeatures::VT_ENABLED, static_cast<uint8_t>(enabled), 0);
+  }
+  void add_num_tokens_before(int32_t num_tokens_before) {
+    fbb_.AddElement<int32_t>(BoundsSensitiveFeatures::VT_NUM_TOKENS_BEFORE, num_tokens_before, 0);
+  }
+  void add_num_tokens_inside_left(int32_t num_tokens_inside_left) {
+    fbb_.AddElement<int32_t>(BoundsSensitiveFeatures::VT_NUM_TOKENS_INSIDE_LEFT, num_tokens_inside_left, 0);
+  }
+  void add_num_tokens_inside_right(int32_t num_tokens_inside_right) {
+    fbb_.AddElement<int32_t>(BoundsSensitiveFeatures::VT_NUM_TOKENS_INSIDE_RIGHT, num_tokens_inside_right, 0);
+  }
+  void add_num_tokens_after(int32_t num_tokens_after) {
+    fbb_.AddElement<int32_t>(BoundsSensitiveFeatures::VT_NUM_TOKENS_AFTER, num_tokens_after, 0);
+  }
+  void add_include_inside_bag(bool include_inside_bag) {
+    fbb_.AddElement<uint8_t>(BoundsSensitiveFeatures::VT_INCLUDE_INSIDE_BAG, static_cast<uint8_t>(include_inside_bag), 0);
+  }
+  void add_include_inside_length(bool include_inside_length) {
+    fbb_.AddElement<uint8_t>(BoundsSensitiveFeatures::VT_INCLUDE_INSIDE_LENGTH, static_cast<uint8_t>(include_inside_length), 0);
+  }
+  explicit BoundsSensitiveFeaturesBuilder(flatbuffers::FlatBufferBuilder &_fbb)
+        : fbb_(_fbb) {
+    start_ = fbb_.StartTable();
+  }
+  BoundsSensitiveFeaturesBuilder &operator=(const BoundsSensitiveFeaturesBuilder &);
+  flatbuffers::Offset<BoundsSensitiveFeatures> Finish() {
+    const auto end = fbb_.EndTable(start_);
+    auto o = flatbuffers::Offset<BoundsSensitiveFeatures>(end);
+    return o;
+  }
+};
+
+inline flatbuffers::Offset<BoundsSensitiveFeatures> CreateBoundsSensitiveFeatures(
+    flatbuffers::FlatBufferBuilder &_fbb,
+    bool enabled = false,
+    int32_t num_tokens_before = 0,
+    int32_t num_tokens_inside_left = 0,
+    int32_t num_tokens_inside_right = 0,
+    int32_t num_tokens_after = 0,
+    bool include_inside_bag = false,
+    bool include_inside_length = false) {
+  BoundsSensitiveFeaturesBuilder builder_(_fbb);
+  builder_.add_num_tokens_after(num_tokens_after);
+  builder_.add_num_tokens_inside_right(num_tokens_inside_right);
+  builder_.add_num_tokens_inside_left(num_tokens_inside_left);
+  builder_.add_num_tokens_before(num_tokens_before);
+  builder_.add_include_inside_length(include_inside_length);
+  builder_.add_include_inside_bag(include_inside_bag);
+  builder_.add_enabled(enabled);
+  return builder_.Finish();
+}
+
+flatbuffers::Offset<BoundsSensitiveFeatures> CreateBoundsSensitiveFeatures(flatbuffers::FlatBufferBuilder &_fbb, const BoundsSensitiveFeaturesT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
+
+}  // namespace FeatureProcessorOptions_
+
+inline SelectionModelOptionsT *SelectionModelOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new SelectionModelOptionsT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void SelectionModelOptions::UnPackTo(SelectionModelOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+  { auto _e = strip_unpaired_brackets(); _o->strip_unpaired_brackets = _e; };
+  { auto _e = symmetry_context_size(); _o->symmetry_context_size = _e; };
+}
+
+inline flatbuffers::Offset<SelectionModelOptions> SelectionModelOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectionModelOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateSelectionModelOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<SelectionModelOptions> CreateSelectionModelOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectionModelOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectionModelOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  auto _strip_unpaired_brackets = _o->strip_unpaired_brackets;
+  auto _symmetry_context_size = _o->symmetry_context_size;
+  return libtextclassifier2::CreateSelectionModelOptions(
+      _fbb,
+      _strip_unpaired_brackets,
+      _symmetry_context_size);
+}
+
+inline ClassificationModelOptionsT *ClassificationModelOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new ClassificationModelOptionsT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void ClassificationModelOptions::UnPackTo(ClassificationModelOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+  { auto _e = phone_min_num_digits(); _o->phone_min_num_digits = _e; };
+  { auto _e = phone_max_num_digits(); _o->phone_max_num_digits = _e; };
+}
+
+inline flatbuffers::Offset<ClassificationModelOptions> ClassificationModelOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ClassificationModelOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateClassificationModelOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<ClassificationModelOptions> CreateClassificationModelOptions(flatbuffers::FlatBufferBuilder &_fbb, const ClassificationModelOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ClassificationModelOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  auto _phone_min_num_digits = _o->phone_min_num_digits;
+  auto _phone_max_num_digits = _o->phone_max_num_digits;
+  return libtextclassifier2::CreateClassificationModelOptions(
+      _fbb,
+      _phone_min_num_digits,
+      _phone_max_num_digits);
+}
+
+inline RegexModelOptionsT *RegexModelOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new RegexModelOptionsT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void RegexModelOptions::UnPackTo(RegexModelOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+  { auto _e = patterns(); if (_e) { _o->patterns.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->patterns[_i] = std::unique_ptr<libtextclassifier2::RegexModelOptions_::PatternT>(_e->Get(_i)->UnPack(_resolver)); } } };
+}
+
+inline flatbuffers::Offset<RegexModelOptions> RegexModelOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RegexModelOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateRegexModelOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<RegexModelOptions> CreateRegexModelOptions(flatbuffers::FlatBufferBuilder &_fbb, const RegexModelOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RegexModelOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  auto _patterns = _o->patterns.size() ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::RegexModelOptions_::Pattern>> (_o->patterns.size(), [](size_t i, _VectorArgs *__va) { return CreatePattern(*__va->__fbb, __va->__o->patterns[i].get(), __va->__rehasher); }, &_va ) : 0;
+  return libtextclassifier2::CreateRegexModelOptions(
+      _fbb,
+      _patterns);
+}
+
+namespace RegexModelOptions_ {
+
+inline PatternT *Pattern::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new PatternT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void Pattern::UnPackTo(PatternT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+  { auto _e = collection_name(); if (_e) _o->collection_name = _e->str(); };
+  { auto _e = pattern(); if (_e) _o->pattern = _e->str(); };
+}
+
+inline flatbuffers::Offset<Pattern> Pattern::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PatternT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreatePattern(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<Pattern> CreatePattern(flatbuffers::FlatBufferBuilder &_fbb, const PatternT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PatternT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  auto _collection_name = _o->collection_name.empty() ? 0 : _fbb.CreateString(_o->collection_name);
+  auto _pattern = _o->pattern.empty() ? 0 : _fbb.CreateString(_o->pattern);
+  return libtextclassifier2::RegexModelOptions_::CreatePattern(
+      _fbb,
+      _collection_name,
+      _pattern);
+}
+
+}  // namespace RegexModelOptions_
+
+inline StructuredRegexModelT *StructuredRegexModel::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new StructuredRegexModelT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void StructuredRegexModel::UnPackTo(StructuredRegexModelT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+  { auto _e = patterns(); if (_e) { _o->patterns.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->patterns[_i] = std::unique_ptr<libtextclassifier2::StructuredRegexModel_::StructuredPatternT>(_e->Get(_i)->UnPack(_resolver)); } } };
+}
+
+inline flatbuffers::Offset<StructuredRegexModel> StructuredRegexModel::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StructuredRegexModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateStructuredRegexModel(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<StructuredRegexModel> CreateStructuredRegexModel(flatbuffers::FlatBufferBuilder &_fbb, const StructuredRegexModelT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StructuredRegexModelT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  auto _patterns = _o->patterns.size() ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::StructuredRegexModel_::StructuredPattern>> (_o->patterns.size(), [](size_t i, _VectorArgs *__va) { return CreateStructuredPattern(*__va->__fbb, __va->__o->patterns[i].get(), __va->__rehasher); }, &_va ) : 0;
+  return libtextclassifier2::CreateStructuredRegexModel(
+      _fbb,
+      _patterns);
+}
+
+namespace StructuredRegexModel_ {
+
+inline StructuredPatternT *StructuredPattern::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new StructuredPatternT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void StructuredPattern::UnPackTo(StructuredPatternT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+  { auto _e = pattern(); if (_e) _o->pattern = _e->str(); };
+  { auto _e = node_names(); if (_e) { _o->node_names.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->node_names[_i] = _e->Get(_i)->str(); } } };
+}
+
+inline flatbuffers::Offset<StructuredPattern> StructuredPattern::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StructuredPatternT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateStructuredPattern(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<StructuredPattern> CreateStructuredPattern(flatbuffers::FlatBufferBuilder &_fbb, const StructuredPatternT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StructuredPatternT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  auto _pattern = _o->pattern.empty() ? 0 : _fbb.CreateString(_o->pattern);
+  auto _node_names = _o->node_names.size() ? _fbb.CreateVectorOfStrings(_o->node_names) : 0;
+  return libtextclassifier2::StructuredRegexModel_::CreateStructuredPattern(
+      _fbb,
+      _pattern,
+      _node_names);
+}
+
+}  // namespace StructuredRegexModel_
+
+inline ModelT *Model::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new ModelT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void Model::UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+  { auto _e = language(); if (_e) _o->language = _e->str(); };
+  { auto _e = version(); _o->version = _e; };
+  { auto _e = selection_feature_options(); if (_e) _o->selection_feature_options = std::unique_ptr<FeatureProcessorOptionsT>(_e->UnPack(_resolver)); };
+  { auto _e = classification_feature_options(); if (_e) _o->classification_feature_options = std::unique_ptr<FeatureProcessorOptionsT>(_e->UnPack(_resolver)); };
+  { auto _e = selection_model(); if (_e) { _o->selection_model.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->selection_model[_i] = _e->Get(_i); } } };
+  { auto _e = classification_model(); if (_e) { _o->classification_model.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->classification_model[_i] = _e->Get(_i); } } };
+  { auto _e = embedding_model(); if (_e) { _o->embedding_model.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->embedding_model[_i] = _e->Get(_i); } } };
+  { auto _e = regex_options(); if (_e) _o->regex_options = std::unique_ptr<RegexModelOptionsT>(_e->UnPack(_resolver)); };
+  { auto _e = selection_options(); if (_e) _o->selection_options = std::unique_ptr<SelectionModelOptionsT>(_e->UnPack(_resolver)); };
+  { auto _e = classification_options(); if (_e) _o->classification_options = std::unique_ptr<ClassificationModelOptionsT>(_e->UnPack(_resolver)); };
+  { auto _e = regex_model(); if (_e) _o->regex_model = std::unique_ptr<StructuredRegexModelT>(_e->UnPack(_resolver)); };
+}
+
+inline flatbuffers::Offset<Model> Model::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateModel(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<Model> CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ModelT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  auto _language = _o->language.empty() ? 0 : _fbb.CreateString(_o->language);
+  auto _version = _o->version;
+  auto _selection_feature_options = _o->selection_feature_options ? CreateFeatureProcessorOptions(_fbb, _o->selection_feature_options.get(), _rehasher) : 0;
+  auto _classification_feature_options = _o->classification_feature_options ? CreateFeatureProcessorOptions(_fbb, _o->classification_feature_options.get(), _rehasher) : 0;
+  auto _selection_model = _o->selection_model.size() ? _fbb.CreateVector(_o->selection_model) : 0;
+  auto _classification_model = _o->classification_model.size() ? _fbb.CreateVector(_o->classification_model) : 0;
+  auto _embedding_model = _o->embedding_model.size() ? _fbb.CreateVector(_o->embedding_model) : 0;
+  auto _regex_options = _o->regex_options ? CreateRegexModelOptions(_fbb, _o->regex_options.get(), _rehasher) : 0;
+  auto _selection_options = _o->selection_options ? CreateSelectionModelOptions(_fbb, _o->selection_options.get(), _rehasher) : 0;
+  auto _classification_options = _o->classification_options ? CreateClassificationModelOptions(_fbb, _o->classification_options.get(), _rehasher) : 0;
+  auto _regex_model = _o->regex_model ? CreateStructuredRegexModel(_fbb, _o->regex_model.get(), _rehasher) : 0;
+  return libtextclassifier2::CreateModel(
+      _fbb,
+      _language,
+      _version,
+      _selection_feature_options,
+      _classification_feature_options,
+      _selection_model,
+      _classification_model,
+      _embedding_model,
+      _regex_options,
+      _selection_options,
+      _classification_options,
+      _regex_model);
+}
+
+inline TokenizationCodepointRangeT *TokenizationCodepointRange::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new TokenizationCodepointRangeT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void TokenizationCodepointRange::UnPackTo(TokenizationCodepointRangeT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+  { auto _e = start(); _o->start = _e; };
+  { auto _e = end(); _o->end = _e; };
+  { auto _e = role(); _o->role = _e; };
+  { auto _e = script_id(); _o->script_id = _e; };
+}
+
+inline flatbuffers::Offset<TokenizationCodepointRange> TokenizationCodepointRange::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TokenizationCodepointRangeT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateTokenizationCodepointRange(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<TokenizationCodepointRange> CreateTokenizationCodepointRange(flatbuffers::FlatBufferBuilder &_fbb, const TokenizationCodepointRangeT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TokenizationCodepointRangeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  auto _start = _o->start;
+  auto _end = _o->end;
+  auto _role = _o->role;
+  auto _script_id = _o->script_id;
+  return libtextclassifier2::CreateTokenizationCodepointRange(
+      _fbb,
+      _start,
+      _end,
+      _role,
+      _script_id);
+}
+
+inline FeatureProcessorOptionsT *FeatureProcessorOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new FeatureProcessorOptionsT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void FeatureProcessorOptions::UnPackTo(FeatureProcessorOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+  { auto _e = num_buckets(); _o->num_buckets = _e; };
+  { auto _e = embedding_size(); _o->embedding_size = _e; };
+  { auto _e = context_size(); _o->context_size = _e; };
+  { auto _e = max_selection_span(); _o->max_selection_span = _e; };
+  { auto _e = chargram_orders(); if (_e) { _o->chargram_orders.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->chargram_orders[_i] = _e->Get(_i); } } };
+  { auto _e = max_word_length(); _o->max_word_length = _e; };
+  { auto _e = unicode_aware_features(); _o->unicode_aware_features = _e; };
+  { auto _e = extract_case_feature(); _o->extract_case_feature = _e; };
+  { auto _e = extract_selection_mask_feature(); _o->extract_selection_mask_feature = _e; };
+  { auto _e = regexp_feature(); if (_e) { _o->regexp_feature.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->regexp_feature[_i] = _e->Get(_i)->str(); } } };
+  { auto _e = remap_digits(); _o->remap_digits = _e; };
+  { auto _e = lowercase_tokens(); _o->lowercase_tokens = _e; };
+  { auto _e = selection_reduced_output_space(); _o->selection_reduced_output_space = _e; };
+  { auto _e = collections(); if (_e) { _o->collections.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->collections[_i] = _e->Get(_i)->str(); } } };
+  { auto _e = default_collection(); _o->default_collection = _e; };
+  { auto _e = only_use_line_with_click(); _o->only_use_line_with_click = _e; };
+  { auto _e = split_tokens_on_selection_boundaries(); _o->split_tokens_on_selection_boundaries = _e; };
+  { auto _e = tokenization_codepoint_config(); if (_e) { _o->tokenization_codepoint_config.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->tokenization_codepoint_config[_i] = std::unique_ptr<TokenizationCodepointRangeT>(_e->Get(_i)->UnPack(_resolver)); } } };
+  { auto _e = center_token_selection_method(); _o->center_token_selection_method = _e; };
+  { auto _e = snap_label_span_boundaries_to_containing_tokens(); _o->snap_label_span_boundaries_to_containing_tokens = _e; };
+  { auto _e = supported_codepoint_ranges(); if (_e) { _o->supported_codepoint_ranges.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->supported_codepoint_ranges[_i] = std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::CodepointRangeT>(_e->Get(_i)->UnPack(_resolver)); } } };
+  { auto _e = internal_tokenizer_codepoint_ranges(); if (_e) { _o->internal_tokenizer_codepoint_ranges.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->internal_tokenizer_codepoint_ranges[_i] = std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::CodepointRangeT>(_e->Get(_i)->UnPack(_resolver)); } } };
+  { auto _e = min_supported_codepoint_ratio(); _o->min_supported_codepoint_ratio = _e; };
+  { auto _e = feature_version(); _o->feature_version = _e; };
+  { auto _e = tokenization_type(); _o->tokenization_type = _e; };
+  { auto _e = icu_preserve_whitespace_tokens(); _o->icu_preserve_whitespace_tokens = _e; };
+  { auto _e = ignored_span_boundary_codepoints(); if (_e) { _o->ignored_span_boundary_codepoints.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->ignored_span_boundary_codepoints[_i] = _e->Get(_i); } } };
+  { auto _e = click_random_token_in_selection(); _o->click_random_token_in_selection = _e; };
+  { auto _e = alternative_collection_map(); if (_e) { _o->alternative_collection_map.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->alternative_collection_map[_i] = std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::CollectionMapEntryT>(_e->Get(_i)->UnPack(_resolver)); } } };
+  { auto _e = bounds_sensitive_features(); if (_e) _o->bounds_sensitive_features = std::unique_ptr<libtextclassifier2::FeatureProcessorOptions_::BoundsSensitiveFeaturesT>(_e->UnPack(_resolver)); };
+  { auto _e = split_selection_candidates(); _o->split_selection_candidates = _e; };
+  { auto _e = allowed_chargrams(); if (_e) { _o->allowed_chargrams.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->allowed_chargrams[_i] = _e->Get(_i)->str(); } } };
+  { auto _e = tokenize_on_script_change(); _o->tokenize_on_script_change = _e; };
+}
+
+inline flatbuffers::Offset<FeatureProcessorOptions> FeatureProcessorOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FeatureProcessorOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateFeatureProcessorOptions(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<FeatureProcessorOptions> CreateFeatureProcessorOptions(flatbuffers::FlatBufferBuilder &_fbb, const FeatureProcessorOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FeatureProcessorOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  auto _num_buckets = _o->num_buckets;
+  auto _embedding_size = _o->embedding_size;
+  auto _context_size = _o->context_size;
+  auto _max_selection_span = _o->max_selection_span;
+  auto _chargram_orders = _o->chargram_orders.size() ? _fbb.CreateVector(_o->chargram_orders) : 0;
+  auto _max_word_length = _o->max_word_length;
+  auto _unicode_aware_features = _o->unicode_aware_features;
+  auto _extract_case_feature = _o->extract_case_feature;
+  auto _extract_selection_mask_feature = _o->extract_selection_mask_feature;
+  auto _regexp_feature = _o->regexp_feature.size() ? _fbb.CreateVectorOfStrings(_o->regexp_feature) : 0;
+  auto _remap_digits = _o->remap_digits;
+  auto _lowercase_tokens = _o->lowercase_tokens;
+  auto _selection_reduced_output_space = _o->selection_reduced_output_space;
+  auto _collections = _o->collections.size() ? _fbb.CreateVectorOfStrings(_o->collections) : 0;
+  auto _default_collection = _o->default_collection;
+  auto _only_use_line_with_click = _o->only_use_line_with_click;
+  auto _split_tokens_on_selection_boundaries = _o->split_tokens_on_selection_boundaries;
+  auto _tokenization_codepoint_config = _o->tokenization_codepoint_config.size() ? _fbb.CreateVector<flatbuffers::Offset<TokenizationCodepointRange>> (_o->tokenization_codepoint_config.size(), [](size_t i, _VectorArgs *__va) { return CreateTokenizationCodepointRange(*__va->__fbb, __va->__o->tokenization_codepoint_config[i].get(), __va->__rehasher); }, &_va ) : 0;
+  auto _center_token_selection_method = _o->center_token_selection_method;
+  auto _snap_label_span_boundaries_to_containing_tokens = _o->snap_label_span_boundaries_to_containing_tokens;
+  auto _supported_codepoint_ranges = _o->supported_codepoint_ranges.size() ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> (_o->supported_codepoint_ranges.size(), [](size_t i, _VectorArgs *__va) { return CreateCodepointRange(*__va->__fbb, __va->__o->supported_codepoint_ranges[i].get(), __va->__rehasher); }, &_va ) : 0;
+  auto _internal_tokenizer_codepoint_ranges = _o->internal_tokenizer_codepoint_ranges.size() ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CodepointRange>> (_o->internal_tokenizer_codepoint_ranges.size(), [](size_t i, _VectorArgs *__va) { return CreateCodepointRange(*__va->__fbb, __va->__o->internal_tokenizer_codepoint_ranges[i].get(), __va->__rehasher); }, &_va ) : 0;
+  auto _min_supported_codepoint_ratio = _o->min_supported_codepoint_ratio;
+  auto _feature_version = _o->feature_version;
+  auto _tokenization_type = _o->tokenization_type;
+  auto _icu_preserve_whitespace_tokens = _o->icu_preserve_whitespace_tokens;
+  auto _ignored_span_boundary_codepoints = _o->ignored_span_boundary_codepoints.size() ? _fbb.CreateVector(_o->ignored_span_boundary_codepoints) : 0;
+  auto _click_random_token_in_selection = _o->click_random_token_in_selection;
+  auto _alternative_collection_map = _o->alternative_collection_map.size() ? _fbb.CreateVector<flatbuffers::Offset<libtextclassifier2::FeatureProcessorOptions_::CollectionMapEntry>> (_o->alternative_collection_map.size(), [](size_t i, _VectorArgs *__va) { return CreateCollectionMapEntry(*__va->__fbb, __va->__o->alternative_collection_map[i].get(), __va->__rehasher); }, &_va ) : 0;
+  auto _bounds_sensitive_features = _o->bounds_sensitive_features ? CreateBoundsSensitiveFeatures(_fbb, _o->bounds_sensitive_features.get(), _rehasher) : 0;
+  auto _split_selection_candidates = _o->split_selection_candidates;
+  auto _allowed_chargrams = _o->allowed_chargrams.size() ? _fbb.CreateVectorOfStrings(_o->allowed_chargrams) : 0;
+  auto _tokenize_on_script_change = _o->tokenize_on_script_change;
+  return libtextclassifier2::CreateFeatureProcessorOptions(
+      _fbb,
+      _num_buckets,
+      _embedding_size,
+      _context_size,
+      _max_selection_span,
+      _chargram_orders,
+      _max_word_length,
+      _unicode_aware_features,
+      _extract_case_feature,
+      _extract_selection_mask_feature,
+      _regexp_feature,
+      _remap_digits,
+      _lowercase_tokens,
+      _selection_reduced_output_space,
+      _collections,
+      _default_collection,
+      _only_use_line_with_click,
+      _split_tokens_on_selection_boundaries,
+      _tokenization_codepoint_config,
+      _center_token_selection_method,
+      _snap_label_span_boundaries_to_containing_tokens,
+      _supported_codepoint_ranges,
+      _internal_tokenizer_codepoint_ranges,
+      _min_supported_codepoint_ratio,
+      _feature_version,
+      _tokenization_type,
+      _icu_preserve_whitespace_tokens,
+      _ignored_span_boundary_codepoints,
+      _click_random_token_in_selection,
+      _alternative_collection_map,
+      _bounds_sensitive_features,
+      _split_selection_candidates,
+      _allowed_chargrams,
+      _tokenize_on_script_change);
+}
+
+namespace FeatureProcessorOptions_ {
+
+inline CodepointRangeT *CodepointRange::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new CodepointRangeT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void CodepointRange::UnPackTo(CodepointRangeT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+  { auto _e = start(); _o->start = _e; };
+  { auto _e = end(); _o->end = _e; };
+}
+
+inline flatbuffers::Offset<CodepointRange> CodepointRange::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CodepointRangeT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateCodepointRange(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<CodepointRange> CreateCodepointRange(flatbuffers::FlatBufferBuilder &_fbb, const CodepointRangeT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CodepointRangeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  auto _start = _o->start;
+  auto _end = _o->end;
+  return libtextclassifier2::FeatureProcessorOptions_::CreateCodepointRange(
+      _fbb,
+      _start,
+      _end);
+}
+
+inline CollectionMapEntryT *CollectionMapEntry::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new CollectionMapEntryT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void CollectionMapEntry::UnPackTo(CollectionMapEntryT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+  { auto _e = key(); if (_e) _o->key = _e->str(); };
+  { auto _e = value(); if (_e) _o->value = _e->str(); };
+}
+
+inline flatbuffers::Offset<CollectionMapEntry> CollectionMapEntry::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CollectionMapEntryT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateCollectionMapEntry(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<CollectionMapEntry> CreateCollectionMapEntry(flatbuffers::FlatBufferBuilder &_fbb, const CollectionMapEntryT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CollectionMapEntryT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  auto _key = _o->key.empty() ? 0 : _fbb.CreateString(_o->key);
+  auto _value = _o->value.empty() ? 0 : _fbb.CreateString(_o->value);
+  return libtextclassifier2::FeatureProcessorOptions_::CreateCollectionMapEntry(
+      _fbb,
+      _key,
+      _value);
+}
+
+inline BoundsSensitiveFeaturesT *BoundsSensitiveFeatures::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
+  auto _o = new BoundsSensitiveFeaturesT();
+  UnPackTo(_o, _resolver);
+  return _o;
+}
+
+inline void BoundsSensitiveFeatures::UnPackTo(BoundsSensitiveFeaturesT *_o, const flatbuffers::resolver_function_t *_resolver) const {
+  (void)_o;
+  (void)_resolver;
+  { auto _e = enabled(); _o->enabled = _e; };
+  { auto _e = num_tokens_before(); _o->num_tokens_before = _e; };
+  { auto _e = num_tokens_inside_left(); _o->num_tokens_inside_left = _e; };
+  { auto _e = num_tokens_inside_right(); _o->num_tokens_inside_right = _e; };
+  { auto _e = num_tokens_after(); _o->num_tokens_after = _e; };
+  { auto _e = include_inside_bag(); _o->include_inside_bag = _e; };
+  { auto _e = include_inside_length(); _o->include_inside_length = _e; };
+}
+
+inline flatbuffers::Offset<BoundsSensitiveFeatures> BoundsSensitiveFeatures::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BoundsSensitiveFeaturesT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
+  return CreateBoundsSensitiveFeatures(_fbb, _o, _rehasher);
+}
+
+inline flatbuffers::Offset<BoundsSensitiveFeatures> CreateBoundsSensitiveFeatures(flatbuffers::FlatBufferBuilder &_fbb, const BoundsSensitiveFeaturesT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
+  (void)_rehasher;
+  (void)_o;
+  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BoundsSensitiveFeaturesT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
+  auto _enabled = _o->enabled;
+  auto _num_tokens_before = _o->num_tokens_before;
+  auto _num_tokens_inside_left = _o->num_tokens_inside_left;
+  auto _num_tokens_inside_right = _o->num_tokens_inside_right;
+  auto _num_tokens_after = _o->num_tokens_after;
+  auto _include_inside_bag = _o->include_inside_bag;
+  auto _include_inside_length = _o->include_inside_length;
+  return libtextclassifier2::FeatureProcessorOptions_::CreateBoundsSensitiveFeatures(
+      _fbb,
+      _enabled,
+      _num_tokens_before,
+      _num_tokens_inside_left,
+      _num_tokens_inside_right,
+      _num_tokens_after,
+      _include_inside_bag,
+      _include_inside_length);
+}
+
+}  // namespace FeatureProcessorOptions_
+}  // namespace libtextclassifier2
+
+#endif  // FLATBUFFERS_GENERATED_MODEL_LIBTEXTCLASSIFIER2_FEATUREPROCESSOROPTIONS__H_
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diff --git a/regex-base.cc b/regex-base.cc
new file mode 100644
index 0000000..790e453
--- /dev/null
+++ b/regex-base.cc
@@ -0,0 +1,61 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "regex-base.h"
+
+namespace libtextclassifier2 {
+
+Rules::Rules(const std::string &locale) : locale_(locale) {}
+
+FlatBufferRules::FlatBufferRules(const std::string &locale, const Model *model)
+    : Rules(locale), model_(model), rule_cache_() {
+  for (int i = 0; i < model_->regex_model()->patterns()->Length(); i++) {
+    auto regex_model = model_->regex_model()->patterns()->Get(i);
+    for (int j = 0; j < regex_model->node_names()->Length(); j++) {
+      std::string name = regex_model->node_names()->Get(j)->str();
+      rule_cache_[name].push_back(regex_model->pattern());
+    }
+  }
+}
+
+bool FlatBufferRules::RuleForName(const std::string &name,
+                                  std::string *out) const {
+  const auto match = rule_cache_.find(name);
+  if (match != rule_cache_.end()) {
+    if (match->second.size() != 1) {
+      TC_LOG(ERROR) << "Found " << match->second.size()
+                    << " rule where only 1 was expected.";
+      return false;
+    }
+    *out = match->second[0]->str();
+    return true;
+  }
+  return false;
+}
+
+const std::vector<std::string> FlatBufferRules::RulesForName(
+    const std::string &name) const {
+  std::vector<std::string> results;
+  const auto match = rule_cache_.find(name);
+  if (match != rule_cache_.end()) {
+    for (auto &s : match->second) {
+      results.push_back(s->str());
+    }
+  }
+  return results;
+}
+
+}  // namespace libtextclassifier2
diff --git a/regex-base.h b/regex-base.h
new file mode 100644
index 0000000..5856eba
--- /dev/null
+++ b/regex-base.h
@@ -0,0 +1,472 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_REGEX_BASE_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_REGEX_BASE_H_
+
+#include <iostream>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "model_generated.h"
+#include "util/base/logging.h"
+#include "util/memory/mmap.h"
+#include "unicode/regex.h"
+#include "unicode/uchar.h"
+
+namespace libtextclassifier2 {
+
+// Encapsulates the start and end of a region of a string of a entity that
+// has been mapping to an element of type T
+template <class T>
+class SpanResult {
+ private:
+  const T data_;
+  const int start_;
+  const int end_;
+
+ public:
+  SpanResult(int start, int end, T data)
+      : data_(data), start_(start), end_(end) {}
+
+  const T &Data() const { return data_; }
+
+  int Start() const { return start_; }
+
+  int End() const { return end_; }
+};
+
+// Interface supplying class that provides a protocol for encapsulating a unit
+// of text processing that can match against strings and also extract values of
+// SpanResults of type T.  Implemenations are expected to be thread-safe.
+template <class T>
+class Node {
+ public:
+  typedef SpanResult<T> Result;
+  typedef std::vector<Result> Results;
+
+  // Returns a boolean value if Node can find a region of the string which
+  // matches it's logic.
+  virtual bool Matches(const std::string &input) const = 0;
+
+  // Populates the supplied Results vector with the values obtained by
+  // extracted on any matching elements of the string.
+  // Returns true if the processing yielded no error.
+  // Returns false if their was an error processing the string.
+  virtual bool Extract(const std::string &input, Results *result) const = 0;
+
+  virtual ~Node() = default;
+};
+
+class Rules {
+ public:
+  explicit Rules(const std::string &locale);
+  virtual ~Rules() = default;
+  virtual const std::vector<std::string> RulesForName(
+      const std::string &name) const = 0;
+  virtual bool RuleForName(const std::string &name, std::string *out) const = 0;
+
+ protected:
+  const std::string locale_;
+};
+
+class FlatBufferRules : public Rules {
+ public:
+  FlatBufferRules(const std::string &locale, const Model *model);
+  const std::vector<std::string> RulesForName(
+      const std::string &name) const override;
+  bool RuleForName(const std::string &name, std::string *out) const override;
+
+ protected:
+  const Model *model_;
+  std::unordered_map<std::string, std::vector<const flatbuffers::String *>>
+      rule_cache_;
+};
+
+// Abstract supplying class that provides a protocol for encapsulating a unit
+// of regular expression processing that can match against strings and also
+// extract values of SpanResults of type T.  Implemenations are expected to be
+// thread-safe.
+// Implementors of this class are expected to call Init() prior to calling
+// Extract() or Match().
+template <class T>
+class RegexNode : public Node<T> {
+ public:
+  typedef typename Node<T>::Result Result;
+  typedef typename Node<T>::Results Results;
+
+  RegexNode() : pattern_() {}
+
+  // A string representation of the regular expression used in this processing.
+  const std::string pattern() const {
+    std::string regex;
+    pattern_->pattern().toUTF8String(regex);
+    return regex;
+  }
+
+  bool Matches(const std::string &input) const override {
+    UErrorCode status = U_ZERO_ERROR;
+    const icu::UnicodeString unicode_context(input.c_str(), input.size(),
+                                             "utf-8");
+    std::unique_ptr<icu::RegexMatcher> matcher(
+        pattern_->matcher(unicode_context, status));
+
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to load regex '" << pattern()
+                    << "': " << u_errorName(status);
+      return false;
+    }
+    const bool res = matcher->find(status);
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to find with regex '" << pattern()
+                    << "': " << u_errorName(status);
+      return false;
+    }
+    return res;
+  }
+
+  bool Extract(const std::string &input, Results *result) const override = 0;
+  ~RegexNode() override {}
+
+ protected:
+  bool Init(const std::string &regex) {
+    UErrorCode status = U_ZERO_ERROR;
+    pattern_ = std::unique_ptr<icu::RegexPattern>(
+        icu::RegexPattern::compile(regex.c_str(), UREGEX_MULTILINE, status));
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to compile regex '" << pattern()
+                    << "': " << u_errorName(status);
+      return false;
+    }
+    return true;
+  }
+
+  std::unique_ptr<icu::RegexPattern> pattern_;
+};
+
+// Class that encapsulates complex object matching and extract values of
+// from multiple sub-patterns, each of which is mapped to a string describing
+// the value.
+// Thread-safe.
+template <class T>
+class CompoundNode : public RegexNode<std::unordered_map<std::string, T>> {
+ public:
+  typedef std::unordered_map<std::string, std::unique_ptr<const Node<T>>>
+      Extractors;
+  typedef typename RegexNode<std::unordered_map<std::string, T>>::Result Result;
+  typedef
+      typename RegexNode<std::unordered_map<std::string, T>>::Results Results;
+
+  static std::unique_ptr<CompoundNode<T>> Instance(const std::string &rule,
+                                                   const Extractors &extractors,
+                                                   const Rules &rules) {
+    std::unique_ptr<CompoundNode<T>> node(new CompoundNode(extractors));
+    if (!node->Init(rule, rules)) {
+      return nullptr;
+    }
+    return node;
+  }
+
+  bool Extract(const std::string &input, Results *result) const override {
+    UErrorCode status = U_ZERO_ERROR;
+    const icu::UnicodeString unicode_context(input.c_str(), input.size(),
+                                             "utf-8");
+    std::unique_ptr<icu::RegexMatcher> matcher(
+        RegexNode<std::unordered_map<std::string, T>>::pattern_->matcher(
+            unicode_context, status));
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "error loading regex '"
+                    << RegexNode<std::unordered_map<std::string, T>>::pattern()
+                    << "'" << u_errorName(status);
+      return false;
+    }
+    while (matcher->find() && U_SUCCESS(status)) {
+      const int start = matcher->start(status);
+      if (U_FAILURE(status)) {
+        TC_LOG(ERROR)
+            << "failed to demarshall start '"
+            << RegexNode<std::unordered_map<std::string, T>>::pattern() << "'"
+            << u_errorName(status);
+        return false;
+      }
+
+      const int end = matcher->end(status);
+      if (U_FAILURE(status)) {
+        TC_LOG(ERROR)
+            << "failed to demarshall end '"
+            << RegexNode<std::unordered_map<std::string, T>>::pattern() << "'"
+            << u_errorName(status);
+        return false;
+      }
+
+      std::unordered_map<std::string, T> extraction;
+      for (auto name : groupnames_) {
+        const int group_number = matcher->pattern().groupNumberFromName(
+            icu::UnicodeString(name.c_str(), name.size(), "utf-8"), status);
+        if (U_FAILURE(status)) {
+          // We expect this to happen for optional named groups.
+          continue;
+        }
+
+        std::string capture;
+        matcher->group(group_number, status).toUTF8String(capture);
+        std::vector<SpanResult<T>> sub_result;
+
+        if (!extractors_->find(name)->second->Extract(capture, &sub_result)) {
+          return false;
+        }
+        if (!sub_result.empty()) {
+          extraction[name] = sub_result[0].Data();
+        }
+      }
+      result->push_back(Result(start, end, extraction));
+    }
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to extract '"
+                    << RegexNode<std::unordered_map<std::string, T>>::pattern()
+                    << "':" << u_errorName(status);
+      return false;
+    }
+    return true;
+  }
+
+ private:
+  const Extractors *extractors_;
+  std::vector<std::string> groupnames_;
+
+  explicit CompoundNode(const Extractors &extractors)
+      : extractors_(&extractors), groupnames_() {}
+
+  bool Init(const std::string &rule, const Rules &rules) {
+    static const icu::RegexPattern *pattern = []() {
+      UErrorCode status = U_ZERO_ERROR;
+      return icu::RegexPattern::compile("[?]<([A-Z_]+)>", UREGEX_MULTILINE,
+                                        status);
+    }();
+    if (!pattern) {
+      return false;
+    }
+    std::string source = rule;
+    UErrorCode status = U_ZERO_ERROR;
+    const icu::UnicodeString unicode_context(source.c_str(), source.size(),
+                                             "utf-8");
+    std::unique_ptr<icu::RegexMatcher> matcher(
+        pattern->matcher(unicode_context, status));
+
+    std::unordered_map<std::string, std::string> swaps;
+    while (matcher->find(status)) {
+      std::string name;
+      matcher->group(1, status).toUTF8String(name);
+      if (U_FAILURE(status)) {
+        TC_LOG(ERROR) << "failed to demarshall name " << u_errorName(status);
+        return false;
+      }
+      groupnames_.push_back(name);
+    }
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to execute the regex properly"
+                    << u_errorName(status);
+      return false;
+    }
+    for (auto swap : swaps) {
+      std::string::size_type n = 0;
+      while ((n = source.find(swap.first, n)) != std::string::npos) {
+        source.replace(n, swap.first.size(), swap.second);
+        n += swap.second.size();
+      }
+    }
+    return RegexNode<std::unordered_map<std::string, T>>::Init(source);
+  }
+};
+
+// Class that managed multiple alternate Nodes that all yield the same result
+// and can be used interchangeably. It returns reults only from the first
+// successfully matching child node - this Nodes can be given to in in
+// order of precedence.
+// Thread-safe.
+template <class T>
+class OrNode : public Node<T> {
+ public:
+  typedef typename Node<T>::Result Result;
+  typedef typename Node<T>::Results Results;
+
+  explicit OrNode(std::vector<std::unique_ptr<const Node<T>>> alternatives)
+      : alternatives_(std::move(alternatives)) {}
+
+  bool Extract(const std::string &input, Results *result) const override {
+    for (auto &alternative : alternatives_) {
+      typename Node<T>::Results alternative_result;
+      // NOTE: We are explicitly choosing to fall through errors on these
+      // alternatives to try a lesser match instead of bailing on the user.
+      if (alternative->Extract(input, &alternative_result)) {
+        if (!alternative_result.empty()) {
+          for (auto &s : alternative_result) {
+            result->push_back(s);
+          }
+          return true;
+        }
+      }
+    }
+    return true;
+  }
+
+  bool Matches(const std::string &input) const override {
+    for (auto &alternative : alternatives_) {
+      if (alternative->Matches(input)) {
+        return true;
+      }
+    }
+    return false;
+  }
+
+ private:
+  std::vector<std::unique_ptr<const Node<T>>> alternatives_;
+};
+
+// Class that managed multiple alternate RegexNodes that all yield the same
+// result and can be used interchangeably. It returns reults only from the first
+// successfully matching child node - this Nodes can be given to in in
+// order of precedence.
+// Thread-safe.
+template <class T>
+class OrRegexNode : public RegexNode<T> {
+ public:
+  typedef typename RegexNode<T>::Result Result;
+  typedef typename RegexNode<T>::Results Results;
+
+  bool Extract(const std::string &input, Results *result) const override {
+    for (auto &alternative : alternatives_) {
+      typename RegexNode<T>::Results alternative_result;
+      // NOTE: we are explicitly choosing to fall through errors on these
+      // alternatives to try a lesser match instead of bailing on the user
+      if (alternative->Extract(input, &alternative_result)) {
+        if (!alternative_result.empty()) {
+          for (typename RegexNode<T>::Result &s : alternative_result) {
+            result->push_back(s);
+          }
+          return true;
+        }
+      }
+    }
+    return true;
+  }
+
+ protected:
+  std::vector<std::unique_ptr<const RegexNode<T>>> alternatives_;
+
+  explicit OrRegexNode(
+      std::vector<std::unique_ptr<const RegexNode<T>>> alternatives)
+      : alternatives_(std::move(alternatives)) {}
+
+  bool Init() {
+    std::string pattern;
+    for (int i = 0; i < alternatives_.size(); i++) {
+      if (i == 0) {
+        pattern = alternatives_[i]->pattern();
+      } else {
+        pattern += "|";
+        pattern += alternatives_[i]->pattern();
+      }
+    }
+    return RegexNode<T>::Init(pattern);
+  }
+};
+
+// Class that yields a constant value for any string that matches the input
+// Thread-safe.
+template <class T>
+class MappingNode : public RegexNode<T> {
+ public:
+  typedef RegexNode<T> Parent;
+  typedef typename Parent::Result Result;
+  typedef typename Parent::Results Results;
+
+  static std::unique_ptr<MappingNode<T>> Instance(const std::string &name,
+                                                  const T &value,
+                                                  const Rules &rules) {
+    std::unique_ptr<MappingNode<T>> node(new MappingNode<T>(value));
+    if (!node->Init(name, rules)) {
+      return nullptr;
+    }
+    return node;
+  }
+
+  bool Extract(const std::string &input, Results *result) const override {
+    UErrorCode status = U_ZERO_ERROR;
+
+    const icu::UnicodeString unicode_context(input.c_str(), input.size(),
+                                             "utf-8");
+    std::unique_ptr<icu::RegexMatcher> matcher(
+        Parent::pattern_->matcher(unicode_context, status));
+
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "error loading regex ";
+      return false;
+    }
+
+    while (matcher->find() && U_SUCCESS(status)) {
+      const int start = matcher->start(status);
+      if (U_FAILURE(status)) {
+        TC_LOG(ERROR) << "failed to demarshall start " << u_errorName(status);
+        return false;
+      }
+      const int end = matcher->end(status);
+      if (U_FAILURE(status)) {
+        TC_LOG(ERROR) << "failed to demarshall end " << u_errorName(status);
+        return false;
+      }
+      result->push_back(Result(start, end, value_));
+    }
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to demarshall end " << u_errorName(status);
+      return false;
+    }
+    return true;
+  }
+
+ private:
+  explicit MappingNode(const T value) : value_(value) {}
+
+  const T value_;
+
+  bool Init(const std::string &name, const Rules &rules) {
+    std::string pattern;
+    if (!rules.RuleForName(name, &pattern)) {
+      TC_LOG(ERROR) << "failed to load rule for name '" << name << "'";
+      return false;
+    }
+    return RegexNode<T>::Init(pattern);
+  }
+};
+
+template <class T>
+bool BuildMappings(const Rules &rules,
+                   const std::vector<std::pair<std::string, T>> &pairs,
+                   std::vector<std::unique_ptr<const RegexNode<T>>> *mappings) {
+  for (auto &p : pairs) {
+    if (std::unique_ptr<RegexNode<T>> node =
+            MappingNode<T>::Instance(p.first, p.second, rules)) {
+      mappings->emplace_back(std::move(node));
+    } else {
+      return false;
+    }
+  }
+  return true;
+}
+
+}  // namespace libtextclassifier2
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_REGEX_BASE_H_
diff --git a/regex-number.cc b/regex-number.cc
new file mode 100644
index 0000000..ed5a899
--- /dev/null
+++ b/regex-number.cc
@@ -0,0 +1,254 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "regex-number.h"
+
+namespace libtextclassifier2 {
+
+std::unique_ptr<DigitNode> DigitNode::Instance(const Rules& rules) {
+  std::unique_ptr<DigitNode> node(new DigitNode());
+  if (!node->Init(rules)) {
+    return nullptr;
+  }
+  return node;
+}
+
+DigitNode::DigitNode() {}
+
+constexpr const char* kDigits = "DIGITS";
+
+bool DigitNode::Init(const Rules& rules) {
+  std::string pattern;
+  if (!rules.RuleForName(kDigits, &pattern)) {
+    TC_LOG(ERROR) << "failed to load pattern";
+    return false;
+  }
+  return RegexNode<int>::Init(pattern);
+}
+
+bool DigitNode::Extract(const std::string& input, Results* result) const {
+  UErrorCode status = U_ZERO_ERROR;
+
+  const icu::UnicodeString unicode_context(input.c_str(), input.size(),
+                                           "utf-8");
+  const std::unique_ptr<icu::RegexMatcher> matcher(
+      pattern_->matcher(unicode_context, status));
+  if (U_FAILURE(status)) {
+    TC_LOG(ERROR) << "failed to compile regex: " << u_errorName(status);
+    return false;
+  }
+
+  while (matcher->find() && U_SUCCESS(status)) {
+    const int start = matcher->start(status);
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to bind start: " << u_errorName(status);
+      return false;
+    }
+    const int end = matcher->end(status);
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to bind end: " << u_errorName(status);
+      return false;
+    }
+    std::string digit;
+    matcher->group(status).toUTF8String(digit);
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to bind digit std::string: "
+                    << u_errorName(status);
+      return false;
+    }
+    result->push_back(Result(start, end, stoi(digit)));
+  }
+  return true;
+}
+
+constexpr const char* kSignedDigits = "SIGNEDDIGITS";
+
+std::unique_ptr<SignedDigitNode> SignedDigitNode::Instance(const Rules& rules) {
+  std::unique_ptr<SignedDigitNode> node(new SignedDigitNode());
+  if (!node->Init(rules)) {
+    return nullptr;
+  }
+  return node;
+}
+
+SignedDigitNode::SignedDigitNode() {}
+
+bool SignedDigitNode::Init(const Rules& rules) {
+  std::string pattern;
+  if (!rules.RuleForName(kSignedDigits, &pattern)) {
+    TC_LOG(ERROR) << "failed to load pattern";
+    return false;
+  }
+  return RegexNode<int>::Init(pattern);
+}
+
+bool SignedDigitNode::Extract(const std::string& input, Results* result) const {
+  UErrorCode status = U_ZERO_ERROR;
+
+  const icu::UnicodeString unicode_context(input.c_str(), input.size(),
+                                           "utf-8");
+  const std::unique_ptr<icu::RegexMatcher> matcher(
+      pattern_->matcher(unicode_context, status));
+  if (U_FAILURE(status)) {
+    TC_LOG(ERROR) << "failed to compile regex: " << u_errorName(status);
+    return false;
+  }
+
+  while (matcher->find() && U_SUCCESS(status)) {
+    const int start = matcher->start(status);
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to bind start: " << u_errorName(status);
+      return false;
+    }
+    const int end = matcher->end(status);
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to bind end: " << u_errorName(status);
+      return false;
+    }
+    std::string digit;
+    matcher->group(status).toUTF8String(digit);
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to bind digit std::string: "
+                    << u_errorName(status);
+      return false;
+    }
+    result->push_back(Result(start, end, stoi(digit)));
+  }
+  return true;
+}
+
+constexpr const char* kZero = "ZERO";
+constexpr const char* kOne = "ONE";
+constexpr const char* kTwo = "TWO";
+constexpr const char* kThree = "THREE";
+constexpr const char* kFour = "FOUR";
+constexpr const char* kFive = "FIVE";
+constexpr const char* kSix = "SIX";
+constexpr const char* kSeven = "SEVEN";
+constexpr const char* kEight = "EIGHT";
+constexpr const char* kNine = "NINE";
+constexpr const char* kTen = "TEN";
+constexpr const char* kEleven = "ELEVEN";
+constexpr const char* kTwelve = "TWELVE";
+constexpr const char* kThirteen = "THIRTEEN";
+constexpr const char* kFourteen = "FOURTEEN";
+constexpr const char* kFifteen = "FIFTEEN";
+constexpr const char* kSixteen = "SIXTEEN";
+constexpr const char* kSeventeen = "SEVENTEEN";
+constexpr const char* kEighteen = "EIGHTEEN";
+constexpr const char* kNineteen = "NINETEEN";
+constexpr const char* kTwenty = "TWENTY";
+constexpr const char* kThirty = "THIRTY";
+constexpr const char* kForty = "FORTY";
+constexpr const char* kFifty = "FIFTY";
+constexpr const char* kSixty = "SIXTY";
+constexpr const char* kSeventy = "SEVENTY";
+constexpr const char* kEighty = "EIGHTY";
+constexpr const char* kNinety = "NINETY";
+constexpr const char* kHundred = "HUNDRED";
+constexpr const char* kThousand = "THOUSAND";
+
+std::unique_ptr<NumberNode> NumberNode::Instance(const Rules& rules) {
+  const std::vector<std::pair<std::string, int>> name_values = {
+      {kZero, 0},      {kOne, 1},         {kTwo, 2},       {kThree, 3},
+      {kFour, 4},      {kFive, 5},        {kSix, 6},       {kSeven, 7},
+      {kEight, 8},     {kNine, 9},        {kTen, 10},      {kEleven, 11},
+      {kTwelve, 12},   {kThirteen, 13},   {kFourteen, 14}, {kFifteen, 15},
+      {kSixteen, 16},  {kSeventeen, 17},  {kEighteen, 18}, {kNineteen, 19},
+      {kTwenty, 20},   {kThirty, 30},     {kForty, 40},    {kFifty, 50},
+      {kSixty, 60},    {kSeventy, 70},    {kEighty, 80},   {kNinety, 90},
+      {kHundred, 100}, {kThousand, 1000},
+  };
+  std::vector<std::unique_ptr<const RegexNode<int>>> alternatives;
+  if (!BuildMappings<int>(rules, name_values, &alternatives)) {
+    return nullptr;
+  }
+  std::unique_ptr<NumberNode> node(new NumberNode(std::move(alternatives)));
+  if (!node->Init()) {
+    return nullptr;
+  }
+  return node;
+}  // namespace libtextclassifier2
+
+bool NumberNode::Init() { return OrRegexNode<int>::Init(); }
+
+NumberNode::NumberNode(
+    std::vector<std::unique_ptr<const RegexNode<int>>> alternatives)
+    : OrRegexNode<int>(std::move(alternatives)) {}
+
+bool NumberNode::Extract(const std::string& input, Results* result) const {
+  UErrorCode status = U_ZERO_ERROR;
+
+  const icu::UnicodeString unicode_context(input.c_str(), input.size(),
+                                           "utf-8");
+  const std::unique_ptr<icu::RegexMatcher> matcher(
+      RegexNode<int>::pattern_->matcher(unicode_context, status));
+
+  OrRegexNode<int>::Results parts;
+  int start = 0;
+  int end = 0;
+  while (matcher->find() && U_SUCCESS(status)) {
+    std::string group;
+    matcher->group(0, status).toUTF8String(group);
+    int span_start = matcher->start(status);
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to demarshall start " << u_errorName(status);
+      return false;
+    }
+    int span_end = matcher->end(status);
+    if (U_FAILURE(status)) {
+      TC_LOG(ERROR) << "failed to demarshall end " << u_errorName(status);
+    }
+    if (span_start < start) {
+      start = span_start;
+    }
+    if (span_end < end) {
+      end = span_end;
+    }
+
+    for (auto& child : alternatives_) {
+      if (child->Matches(group)) {
+        OrRegexNode<int>::Results group_results;
+        if (!child->Extract(group, &group_results)) {
+          return false;
+        }
+        for (OrRegexNode<int>::Result span : group_results) {
+          parts.push_back(span);
+        }
+      }
+    }
+  }
+  int sum = 0;
+  int running_value = -1;
+  // Simple math to make sure we handle written numerical modifiers correctly
+  // so that :="fifty one  thousand and one" maps to 51001 and not 50 1 1000 1.
+  for (OrRegexNode<int>::Result part : parts) {
+    if (running_value >= 0) {
+      if (running_value > part.Data()) {
+        sum += running_value;
+        running_value = part.Data();
+      } else {
+        running_value *= part.Data();
+      }
+    } else {
+      running_value = part.Data();
+    }
+  }
+  sum += running_value;
+  result->push_back(Result(start, end, sum));
+  return true;
+}
+}  // namespace libtextclassifier2
diff --git a/regex-number.h b/regex-number.h
new file mode 100644
index 0000000..286e57d
--- /dev/null
+++ b/regex-number.h
@@ -0,0 +1,110 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_REGEX_NUMBER_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_REGEX_NUMBER_H_
+
+#include "regex-base.h"
+
+namespace libtextclassifier2 {
+
+extern const char* const kZero;
+extern const char* const kOne;
+extern const char* const kTwo;
+extern const char* const kThree;
+extern const char* const kFour;
+extern const char* const kFive;
+extern const char* const kSix;
+extern const char* const kSeven;
+extern const char* const kEight;
+extern const char* const kNine;
+extern const char* const kTen;
+extern const char* const kEleven;
+extern const char* const kTwelve;
+extern const char* const kThirteen;
+extern const char* const kFourteen;
+extern const char* const kFifteen;
+extern const char* const kSixteen;
+extern const char* const kSeventeen;
+extern const char* const kEighteen;
+extern const char* const kNineteen;
+extern const char* const kTwenty;
+extern const char* const kThirty;
+extern const char* const kForty;
+extern const char* const kFifty;
+extern const char* const kSixty;
+extern const char* const kSeventy;
+extern const char* const kEighty;
+extern const char* const kNinety;
+extern const char* const kHundred;
+extern const char* const kThousand;
+
+extern const char* const kDigits;
+extern const char* const kSignedDigits;
+
+// Class that encapsulates a unsigned integer matching and extract values of
+// SpanResults of type int.
+// Thread-safe.
+class DigitNode : public RegexNode<int> {
+ public:
+  typedef typename RegexNode<int>::Result Result;
+  typedef typename RegexNode<int>::Results Results;
+
+  // Factory method for yielding a pointer to a DigitNode implementation.
+  static std::unique_ptr<DigitNode> Instance(const Rules& rules);
+  bool Extract(const std::string& input, Results* result) const override;
+
+ protected:
+  DigitNode();
+  bool Init(const Rules& rules);
+};
+
+// Class that encapsulates a signed integer matching and extract values of
+// SpanResults of type int.
+// Thread-safe.
+class SignedDigitNode : public RegexNode<int> {
+ public:
+  typedef typename RegexNode<int>::Result Result;
+  typedef typename RegexNode<int>::Results Results;
+
+  // Factory method for yielding a pointer to a DigitNode implementation.
+  static std::unique_ptr<SignedDigitNode> Instance(const Rules& rules);
+  bool Extract(const std::string& input, Results* result) const override;
+
+ protected:
+  SignedDigitNode();
+  bool Init(const Rules& rules);
+};
+
+// Class that encapsulates a simple natural language integer matching and
+// extract values of SpanResults of type int.
+// Thread-safe.
+class NumberNode : public OrRegexNode<int> {
+ public:
+  typedef typename OrRegexNode<int>::Result Result;
+  typedef typename OrRegexNode<int>::Results Results;
+
+  static std::unique_ptr<NumberNode> Instance(const Rules& rules);
+  bool Extract(const std::string& input, Results* result) const override;
+
+ protected:
+  explicit NumberNode(
+      std::vector<std::unique_ptr<const RegexNode<int>>> alternatives);
+  bool Init();
+};
+
+}  // namespace libtextclassifier2
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_REGEX_NUMBER_H_
diff --git a/smartselect/cached-features.cc b/smartselect/cached-features.cc
deleted file mode 100644
index c249db9..0000000
--- a/smartselect/cached-features.cc
+++ /dev/null
@@ -1,82 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/cached-features.h"
-#include "util/base/logging.h"
-
-namespace libtextclassifier {
-
-void CachedFeatures::Extract(
-    const std::vector<std::vector<int>>& sparse_features,
-    const std::vector<std::vector<float>>& dense_features,
-    const std::function<bool(const std::vector<int>&, const std::vector<float>&,
-                             float*)>& feature_vector_fn) {
-  features_.resize(feature_vector_size_ * tokens_.size());
-  for (int i = 0; i < tokens_.size(); ++i) {
-    feature_vector_fn(sparse_features[i], dense_features[i],
-                      features_.data() + i * feature_vector_size_);
-  }
-}
-
-bool CachedFeatures::Get(int click_pos, VectorSpan<float>* features,
-                         VectorSpan<Token>* output_tokens) {
-  const int token_start = click_pos - context_size_;
-  const int token_end = click_pos + context_size_ + 1;
-  if (token_start < 0 || token_end > tokens_.size()) {
-    TC_LOG(ERROR) << "Tokens out of range: " << token_start << " " << token_end;
-    return false;
-  }
-
-  *features =
-      VectorSpan<float>(features_.begin() + token_start * feature_vector_size_,
-                        features_.begin() + token_end * feature_vector_size_);
-  *output_tokens = VectorSpan<Token>(tokens_.begin() + token_start,
-                                     tokens_.begin() + token_end);
-  if (remap_v0_feature_vector_) {
-    RemapV0FeatureVector(features);
-  }
-
-  return true;
-}
-
-void CachedFeatures::RemapV0FeatureVector(VectorSpan<float>* features) {
-  if (!remap_v0_feature_vector_) {
-    return;
-  }
-
-  auto it = features->begin();
-  int num_suffix_features =
-      feature_vector_size_ - remap_v0_chargram_embedding_size_;
-  int num_tokens = context_size_ * 2 + 1;
-  for (int t = 0; t < num_tokens; ++t) {
-    for (int i = 0; i < remap_v0_chargram_embedding_size_; ++i) {
-      v0_feature_storage_[t * remap_v0_chargram_embedding_size_ + i] = *it;
-      ++it;
-    }
-    // Rest of the features are the dense features that come to the end.
-    for (int i = 0; i < num_suffix_features; ++i) {
-      // clang-format off
-      v0_feature_storage_[num_tokens * remap_v0_chargram_embedding_size_
-                      + t * num_suffix_features
-                      + i] = *it;
-      // clang-format on
-      ++it;
-    }
-  }
-  *features = VectorSpan<float>(v0_feature_storage_);
-}
-
-}  // namespace libtextclassifier
diff --git a/smartselect/cached-features.h b/smartselect/cached-features.h
deleted file mode 100644
index 990233c..0000000
--- a/smartselect/cached-features.h
+++ /dev/null
@@ -1,105 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_CACHED_FEATURES_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_CACHED_FEATURES_H_
-
-#include <memory>
-#include <vector>
-
-#include "common/vector-span.h"
-#include "smartselect/types.h"
-
-namespace libtextclassifier {
-
-// Holds state for extracting features across multiple calls and reusing them.
-// Assumes that features for each Token are independent.
-class CachedFeatures {
- public:
-  // Extracts the features for the given sequence of tokens.
-  //  - context_size: Specifies how many tokens to the left, and how many
-  //                   tokens to the right spans the context.
-  //  - sparse_features, dense_features: Extracted features for each token.
-  //  - feature_vector_fn: Writes features for given Token to the specified
-  //                       storage.
-  //                       NOTE: The function can assume that the underlying
-  //                       storage is initialized to all zeros.
-  //  - feature_vector_size: Size of a feature vector for one Token.
-  CachedFeatures(VectorSpan<Token> tokens, int context_size,
-                 const std::vector<std::vector<int>>& sparse_features,
-                 const std::vector<std::vector<float>>& dense_features,
-                 const std::function<bool(const std::vector<int>&,
-                                          const std::vector<float>&, float*)>&
-                     feature_vector_fn,
-                 int feature_vector_size)
-      : tokens_(tokens),
-        context_size_(context_size),
-        feature_vector_size_(feature_vector_size),
-        remap_v0_feature_vector_(false),
-        remap_v0_chargram_embedding_size_(-1) {
-    Extract(sparse_features, dense_features, feature_vector_fn);
-  }
-
-  // Gets a VectorSpan with the features for given click position.
-  bool Get(int click_pos, VectorSpan<float>* features,
-           VectorSpan<Token>* output_tokens);
-
-  // Turns on a compatibility mode, which re-maps the extracted features to the
-  // v0 feature format (where the dense features were at the end).
-  // WARNING: Internally v0_feature_storage_ is used as a backing buffer for
-  // VectorSpan<float>, so the output of Extract is valid only until the next
-  // call or destruction of the current CachedFeatures object.
-  // TODO(zilka): Remove when we'll have retrained models.
-  void SetV0FeatureMode(int chargram_embedding_size) {
-    remap_v0_feature_vector_ = true;
-    remap_v0_chargram_embedding_size_ = chargram_embedding_size;
-    v0_feature_storage_.resize(feature_vector_size_ * (context_size_ * 2 + 1));
-  }
-
- protected:
-  // Extracts features for all tokens and stores them for later retrieval.
-  void Extract(const std::vector<std::vector<int>>& sparse_features,
-               const std::vector<std::vector<float>>& dense_features,
-               const std::function<bool(const std::vector<int>&,
-                                        const std::vector<float>&, float*)>&
-                   feature_vector_fn);
-
-  // Remaps extracted features to V0 feature format. The mapping is using
-  // the v0_feature_storage_ as the backing storage for the mapped features.
-  // For each token the features consist of:
-  //  - chargram embeddings
-  //  - dense features
-  // They are concatenated together as [chargram embeddings; dense features]
-  // for each token independently.
-  // The V0 features require that the chargram embeddings for tokens are
-  // concatenated first together, and at the end, the dense features for the
-  // tokens are concatenated to it.
-  void RemapV0FeatureVector(VectorSpan<float>* features);
-
- private:
-  const VectorSpan<Token> tokens_;
-  const int context_size_;
-  const int feature_vector_size_;
-  bool remap_v0_feature_vector_;
-  int remap_v0_chargram_embedding_size_;
-
-  std::vector<float> features_;
-  std::vector<float> v0_feature_storage_;
-};
-
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_SMARTSELECT_CACHED_FEATURES_H_
diff --git a/smartselect/cached-features_test.cc b/smartselect/cached-features_test.cc
deleted file mode 100644
index b456816..0000000
--- a/smartselect/cached-features_test.cc
+++ /dev/null
@@ -1,149 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/cached-features.h"
-
-#include "gmock/gmock.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace {
-
-class TestingCachedFeatures : public CachedFeatures {
- public:
-  using CachedFeatures::CachedFeatures;
-  using CachedFeatures::RemapV0FeatureVector;
-};
-
-TEST(CachedFeaturesTest, Simple) {
-  std::vector<Token> tokens;
-  tokens.push_back(Token());
-  tokens.push_back(Token());
-  tokens.push_back(Token("Hello", 0, 1));
-  tokens.push_back(Token("World", 1, 2));
-  tokens.push_back(Token("today!", 2, 3));
-  tokens.push_back(Token());
-  tokens.push_back(Token());
-
-  std::vector<std::vector<int>> sparse_features(tokens.size());
-  for (int i = 0; i < sparse_features.size(); ++i) {
-    sparse_features[i].push_back(i);
-  }
-  std::vector<std::vector<float>> dense_features(tokens.size());
-  for (int i = 0; i < dense_features.size(); ++i) {
-    dense_features[i].push_back(-i);
-  }
-
-  TestingCachedFeatures feature_extractor(
-      tokens, /*context_size=*/2, sparse_features, dense_features,
-      [](const std::vector<int>& sparse_features,
-         const std::vector<float>& dense_features, float* features) {
-        features[0] = sparse_features[0];
-        features[1] = sparse_features[0];
-        features[2] = dense_features[0];
-        features[3] = dense_features[0];
-        features[4] = 123;
-        return true;
-      },
-      5);
-
-  VectorSpan<float> features;
-  VectorSpan<Token> output_tokens;
-  EXPECT_TRUE(feature_extractor.Get(2, &features, &output_tokens));
-  for (int i = 0; i < 5; i++) {
-    EXPECT_EQ(features[i * 5 + 0], i) << "Feature " << i;
-    EXPECT_EQ(features[i * 5 + 1], i) << "Feature " << i;
-    EXPECT_EQ(features[i * 5 + 2], -i) << "Feature " << i;
-    EXPECT_EQ(features[i * 5 + 3], -i) << "Feature " << i;
-    EXPECT_EQ(features[i * 5 + 4], 123) << "Feature " << i;
-  }
-}
-
-TEST(CachedFeaturesTest, InvalidInput) {
-  std::vector<Token> tokens;
-  tokens.push_back(Token());
-  tokens.push_back(Token());
-  tokens.push_back(Token("Hello", 0, 1));
-  tokens.push_back(Token("World", 1, 2));
-  tokens.push_back(Token("today!", 2, 3));
-  tokens.push_back(Token());
-  tokens.push_back(Token());
-
-  std::vector<std::vector<int>> sparse_features(tokens.size());
-  std::vector<std::vector<float>> dense_features(tokens.size());
-
-  TestingCachedFeatures feature_extractor(
-      tokens, /*context_size=*/2, sparse_features, dense_features,
-      [](const std::vector<int>& sparse_features,
-         const std::vector<float>& dense_features,
-         float* features) { return true; },
-      /*feature_vector_size=*/5);
-
-  VectorSpan<float> features;
-  VectorSpan<Token> output_tokens;
-  EXPECT_FALSE(feature_extractor.Get(-1000, &features, &output_tokens));
-  EXPECT_FALSE(feature_extractor.Get(-1, &features, &output_tokens));
-  EXPECT_FALSE(feature_extractor.Get(0, &features, &output_tokens));
-  EXPECT_TRUE(feature_extractor.Get(2, &features, &output_tokens));
-  EXPECT_TRUE(feature_extractor.Get(4, &features, &output_tokens));
-  EXPECT_FALSE(feature_extractor.Get(5, &features, &output_tokens));
-  EXPECT_FALSE(feature_extractor.Get(500, &features, &output_tokens));
-}
-
-TEST(CachedFeaturesTest, RemapV0FeatureVector) {
-  std::vector<Token> tokens;
-  tokens.push_back(Token());
-  tokens.push_back(Token());
-  tokens.push_back(Token("Hello", 0, 1));
-  tokens.push_back(Token("World", 1, 2));
-  tokens.push_back(Token("today!", 2, 3));
-  tokens.push_back(Token());
-  tokens.push_back(Token());
-
-  std::vector<std::vector<int>> sparse_features(tokens.size());
-  std::vector<std::vector<float>> dense_features(tokens.size());
-
-  TestingCachedFeatures feature_extractor(
-      tokens, /*context_size=*/2, sparse_features, dense_features,
-      [](const std::vector<int>& sparse_features,
-         const std::vector<float>& dense_features,
-         float* features) { return true; },
-      /*feature_vector_size=*/5);
-
-  std::vector<float> features_orig(5 * 5);
-  for (int i = 0; i < features_orig.size(); i++) {
-    features_orig[i] = i;
-  }
-  VectorSpan<float> features;
-
-  feature_extractor.SetV0FeatureMode(0);
-  features = VectorSpan<float>(features_orig);
-  feature_extractor.RemapV0FeatureVector(&features);
-  EXPECT_EQ(
-      std::vector<float>({0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12,
-                          13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24}),
-      std::vector<float>(features.begin(), features.end()));
-
-  feature_extractor.SetV0FeatureMode(2);
-  features = VectorSpan<float>(features_orig);
-  feature_extractor.RemapV0FeatureVector(&features);
-  EXPECT_EQ(std::vector<float>({0, 1, 5, 6,  10, 11, 15, 16, 20, 21, 2,  3, 4,
-                                7, 8, 9, 12, 13, 14, 17, 18, 19, 22, 23, 24}),
-            std::vector<float>(features.begin(), features.end()));
-}
-
-}  // namespace
-}  // namespace libtextclassifier
diff --git a/smartselect/model-params.cc b/smartselect/model-params.cc
deleted file mode 100644
index 65c4f93..0000000
--- a/smartselect/model-params.cc
+++ /dev/null
@@ -1,108 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/model-params.h"
-
-#include "common/memory_image/memory-image-reader.h"
-
-namespace libtextclassifier {
-
-using nlp_core::EmbeddingNetworkProto;
-using nlp_core::MemoryImageReader;
-
-ModelParams* ModelParamsBuilder(
-    const void* start, uint64 num_bytes,
-    std::shared_ptr<EmbeddingParams> external_embedding_params) {
-  MemoryImageReader<EmbeddingNetworkProto> reader(start, num_bytes);
-
-  ModelOptions model_options;
-  auto model_options_extension_id = model_options_in_embedding_network_proto;
-  if (reader.trimmed_proto().HasExtension(model_options_extension_id)) {
-    model_options =
-        reader.trimmed_proto().GetExtension(model_options_extension_id);
-  }
-
-  FeatureProcessorOptions feature_processor_options;
-  auto feature_processor_extension_id =
-      feature_processor_options_in_embedding_network_proto;
-  if (reader.trimmed_proto().HasExtension(feature_processor_extension_id)) {
-    feature_processor_options =
-        reader.trimmed_proto().GetExtension(feature_processor_extension_id);
-
-    // If no tokenization codepoint config is present, tokenize on space.
-    // TODO(zilka): Remove the default config.
-    if (feature_processor_options.tokenization_codepoint_config_size() == 0) {
-      TokenizationCodepointRange* config;
-      // New line character.
-      config = feature_processor_options.add_tokenization_codepoint_config();
-      config->set_start(10);
-      config->set_end(11);
-      config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
-
-      // Space character.
-      config = feature_processor_options.add_tokenization_codepoint_config();
-      config->set_start(32);
-      config->set_end(33);
-      config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
-    }
-  } else {
-    return nullptr;
-  }
-
-  SelectionModelOptions selection_options;
-  auto selection_options_extension_id =
-      selection_model_options_in_embedding_network_proto;
-  if (reader.trimmed_proto().HasExtension(selection_options_extension_id)) {
-    selection_options =
-        reader.trimmed_proto().GetExtension(selection_options_extension_id);
-
-    // For backward compatibility with the current models.
-    if (!feature_processor_options.ignored_span_boundary_codepoints_size()) {
-      *feature_processor_options.mutable_ignored_span_boundary_codepoints() =
-          selection_options.deprecated_punctuation_to_strip();
-    }
-  } else {
-    selection_options.set_enforce_symmetry(true);
-    selection_options.set_symmetry_context_size(
-        feature_processor_options.context_size() * 2);
-  }
-
-  SharingModelOptions sharing_options;
-  auto sharing_options_extension_id =
-      sharing_model_options_in_embedding_network_proto;
-  if (reader.trimmed_proto().HasExtension(sharing_options_extension_id)) {
-    sharing_options =
-        reader.trimmed_proto().GetExtension(sharing_options_extension_id);
-  } else {
-    // Default values when SharingModelOptions is not present.
-    sharing_options.set_always_accept_url_hint(true);
-    sharing_options.set_always_accept_email_hint(true);
-  }
-
-  if (!model_options.use_shared_embeddings()) {
-    std::shared_ptr<EmbeddingParams> embedding_params(new EmbeddingParams(
-        start, num_bytes, feature_processor_options.context_size()));
-    return new ModelParams(start, num_bytes, embedding_params,
-                           selection_options, sharing_options,
-                           feature_processor_options);
-  } else {
-    return new ModelParams(
-        start, num_bytes, std::move(external_embedding_params),
-        selection_options, sharing_options, feature_processor_options);
-  }
-}
-
-}  // namespace libtextclassifier
diff --git a/smartselect/model-params.h b/smartselect/model-params.h
deleted file mode 100644
index a0d39e6..0000000
--- a/smartselect/model-params.h
+++ /dev/null
@@ -1,152 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Model parameter loading.
-
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_MODEL_PARAMS_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_MODEL_PARAMS_H_
-
-#include "common/embedding-network.h"
-#include "common/memory_image/embedding-network-params-from-image.h"
-#include "smartselect/text-classification-model.pb.h"
-
-namespace libtextclassifier {
-
-class EmbeddingParams : public nlp_core::EmbeddingNetworkParamsFromImage {
- public:
-  EmbeddingParams(const void* start, uint64 num_bytes, int context_size)
-      : EmbeddingNetworkParamsFromImage(start, num_bytes),
-        context_size_(context_size) {}
-
-  int embeddings_size() const override { return context_size_ * 2 + 1; }
-
-  int embedding_num_features_size() const override {
-    return context_size_ * 2 + 1;
-  }
-
-  int embedding_num_features(int i) const override { return 1; }
-
-  int embeddings_num_rows(int i) const override {
-    return EmbeddingNetworkParamsFromImage::embeddings_num_rows(0);
-  };
-
-  int embeddings_num_cols(int i) const override {
-    return EmbeddingNetworkParamsFromImage::embeddings_num_cols(0);
-  };
-
-  const void* embeddings_weights(int i) const override {
-    return EmbeddingNetworkParamsFromImage::embeddings_weights(0);
-  };
-
-  nlp_core::QuantizationType embeddings_quant_type(int i) const override {
-    return EmbeddingNetworkParamsFromImage::embeddings_quant_type(0);
-  }
-
-  const nlp_core::float16* embeddings_quant_scales(int i) const override {
-    return EmbeddingNetworkParamsFromImage::embeddings_quant_scales(0);
-  }
-
- private:
-  int context_size_;
-};
-
-// Loads and holds the parameters of the inference network.
-//
-// This class overrides a couple of methods of EmbeddingNetworkParamsFromImage
-// because we only have one embedding matrix for all positions of context,
-// whereas the original class would have a separate one for each.
-class ModelParams : public nlp_core::EmbeddingNetworkParamsFromImage {
- public:
-  const FeatureProcessorOptions& GetFeatureProcessorOptions() const {
-    return feature_processor_options_;
-  }
-
-  const SelectionModelOptions& GetSelectionModelOptions() const {
-    return selection_options_;
-  }
-
-  const SharingModelOptions& GetSharingModelOptions() const {
-    return sharing_options_;
-  }
-
-  std::shared_ptr<EmbeddingParams> GetEmbeddingParams() const {
-    return embedding_params_;
-  }
-
- protected:
-  int embeddings_size() const override {
-    return embedding_params_->embeddings_size();
-  }
-
-  int embedding_num_features_size() const override {
-    return embedding_params_->embedding_num_features_size();
-  }
-
-  int embedding_num_features(int i) const override {
-    return embedding_params_->embedding_num_features(i);
-  }
-
-  int embeddings_num_rows(int i) const override {
-    return embedding_params_->embeddings_num_rows(i);
-  };
-
-  int embeddings_num_cols(int i) const override {
-    return embedding_params_->embeddings_num_cols(i);
-  };
-
-  const void* embeddings_weights(int i) const override {
-    return embedding_params_->embeddings_weights(i);
-  };
-
-  nlp_core::QuantizationType embeddings_quant_type(int i) const override {
-    return embedding_params_->embeddings_quant_type(i);
-  }
-
-  const nlp_core::float16* embeddings_quant_scales(int i) const override {
-    return embedding_params_->embeddings_quant_scales(i);
-  }
-
- private:
-  friend ModelParams* ModelParamsBuilder(
-      const void* start, uint64 num_bytes,
-      std::shared_ptr<EmbeddingParams> external_embedding_params);
-
-  ModelParams(const void* start, uint64 num_bytes,
-              std::shared_ptr<EmbeddingParams> embedding_params,
-              const SelectionModelOptions& selection_options,
-              const SharingModelOptions& sharing_options,
-              const FeatureProcessorOptions& feature_processor_options)
-      : EmbeddingNetworkParamsFromImage(start, num_bytes),
-        selection_options_(selection_options),
-        sharing_options_(sharing_options),
-        feature_processor_options_(feature_processor_options),
-        context_size_(feature_processor_options_.context_size()),
-        embedding_params_(std::move(embedding_params)) {}
-
-  SelectionModelOptions selection_options_;
-  SharingModelOptions sharing_options_;
-  FeatureProcessorOptions feature_processor_options_;
-  int context_size_;
-  std::shared_ptr<EmbeddingParams> embedding_params_;
-};
-
-ModelParams* ModelParamsBuilder(
-    const void* start, uint64 num_bytes,
-    std::shared_ptr<EmbeddingParams> external_embedding_params);
-
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_SMARTSELECT_MODEL_PARAMS_H_
diff --git a/smartselect/model-parser.cc b/smartselect/model-parser.cc
deleted file mode 100644
index 0cf05e3..0000000
--- a/smartselect/model-parser.cc
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/model-parser.h"
-#include "util/base/endian.h"
-
-namespace libtextclassifier {
-namespace {
-
-// Small helper class for parsing the merged model format.
-// The merged model consists of interleaved <int32 data_size, char* data>
-// segments.
-class MergedModelParser {
- public:
-  MergedModelParser(const void* addr, const int size)
-      : addr_(reinterpret_cast<const char*>(addr)), size_(size), pos_(addr_) {}
-
-  bool ReadBytesAndAdvance(int num_bytes, const char** result) {
-    const char* read_addr = pos_;
-    if (Advance(num_bytes)) {
-      *result = read_addr;
-      return true;
-    } else {
-      return false;
-    }
-  }
-
-  bool ReadInt32AndAdvance(int* result) {
-    const char* read_addr = pos_;
-    if (Advance(sizeof(int))) {
-      *result =
-          LittleEndian::ToHost32(*reinterpret_cast<const uint32*>(read_addr));
-      return true;
-    } else {
-      return false;
-    }
-  }
-
-  bool IsDone() { return pos_ == addr_ + size_; }
-
- private:
-  bool Advance(int num_bytes) {
-    pos_ += num_bytes;
-    return pos_ <= addr_ + size_;
-  }
-
-  const char* addr_;
-  const int size_;
-  const char* pos_;
-};
-
-}  // namespace
-
-bool ParseMergedModel(const void* addr, const int size,
-                      const char** selection_model, int* selection_model_length,
-                      const char** sharing_model, int* sharing_model_length) {
-  MergedModelParser parser(addr, size);
-
-  if (!parser.ReadInt32AndAdvance(selection_model_length)) {
-    return false;
-  }
-
-  if (!parser.ReadBytesAndAdvance(*selection_model_length, selection_model)) {
-    return false;
-  }
-
-  if (!parser.ReadInt32AndAdvance(sharing_model_length)) {
-    return false;
-  }
-
-  if (!parser.ReadBytesAndAdvance(*sharing_model_length, sharing_model)) {
-    return false;
-  }
-
-  return parser.IsDone();
-}
-
-}  // namespace libtextclassifier
diff --git a/smartselect/model-parser.h b/smartselect/model-parser.h
deleted file mode 100644
index 801262f..0000000
--- a/smartselect/model-parser.h
+++ /dev/null
@@ -1,29 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_MODEL_PARSER_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_MODEL_PARSER_H_
-
-namespace libtextclassifier {
-
-// Parse a merged model image.
-bool ParseMergedModel(const void* addr, const int size,
-                      const char** selection_model, int* selection_model_length,
-                      const char** sharing_model, int* sharing_model_length);
-
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_SMARTSELECT_MODEL_PARSER_H_
diff --git a/smartselect/text-classification-model.cc b/smartselect/text-classification-model.cc
deleted file mode 100644
index e7ae09c..0000000
--- a/smartselect/text-classification-model.cc
+++ /dev/null
@@ -1,741 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/text-classification-model.h"
-
-#include <cctype>
-#include <cmath>
-#include <iterator>
-#include <numeric>
-
-#include "common/embedding-network.h"
-#include "common/feature-extractor.h"
-#include "common/memory_image/embedding-network-params-from-image.h"
-#include "common/memory_image/memory-image-reader.h"
-#include "common/mmap.h"
-#include "common/softmax.h"
-#include "smartselect/model-parser.h"
-#include "smartselect/text-classification-model.pb.h"
-#include "util/base/logging.h"
-#include "util/utf8/unicodetext.h"
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-#include "unicode/regex.h"
-#include "unicode/uchar.h"
-#endif
-
-namespace libtextclassifier {
-
-using nlp_core::EmbeddingNetwork;
-using nlp_core::EmbeddingNetworkProto;
-using nlp_core::FeatureVector;
-using nlp_core::MemoryImageReader;
-using nlp_core::MmapFile;
-using nlp_core::MmapHandle;
-using nlp_core::ScopedMmap;
-
-namespace {
-
-int CountDigits(const std::string& str, CodepointSpan selection_indices) {
-  int count = 0;
-  int i = 0;
-  const UnicodeText unicode_str = UTF8ToUnicodeText(str, /*do_copy=*/false);
-  for (auto it = unicode_str.begin(); it != unicode_str.end(); ++it, ++i) {
-    if (i >= selection_indices.first && i < selection_indices.second &&
-        isdigit(*it)) {
-      ++count;
-    }
-  }
-  return count;
-}
-
-std::string ExtractSelection(const std::string& context,
-                             CodepointSpan selection_indices) {
-  const UnicodeText context_unicode =
-      UTF8ToUnicodeText(context, /*do_copy=*/false);
-  auto selection_begin = context_unicode.begin();
-  std::advance(selection_begin, selection_indices.first);
-  auto selection_end = context_unicode.begin();
-  std::advance(selection_end, selection_indices.second);
-  return UnicodeText::UTF8Substring(selection_begin, selection_end);
-}
-
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-bool MatchesRegex(const icu::RegexPattern* regex, const std::string& context) {
-  const icu::UnicodeString unicode_context(context.c_str(), context.size(),
-                                           "utf-8");
-  UErrorCode status = U_ZERO_ERROR;
-  std::unique_ptr<icu::RegexMatcher> matcher(
-      regex->matcher(unicode_context, status));
-  return matcher->matches(0 /* start */, status);
-}
-#endif
-
-}  // namespace
-
-TextClassificationModel::TextClassificationModel(const std::string& path)
-    : mmap_(new nlp_core::ScopedMmap(path)) {
-  InitFromMmap();
-}
-
-TextClassificationModel::TextClassificationModel(int fd)
-    : mmap_(new nlp_core::ScopedMmap(fd)) {
-  InitFromMmap();
-}
-
-TextClassificationModel::TextClassificationModel(int fd, int offset, int size)
-    : mmap_(new nlp_core::ScopedMmap(fd, offset, size)) {
-  InitFromMmap();
-}
-
-TextClassificationModel::TextClassificationModel(const void* addr, int size) {
-  initialized_ = LoadModels(addr, size);
-  if (!initialized_) {
-    TC_LOG(ERROR) << "Failed to load models";
-    return;
-  }
-}
-
-void TextClassificationModel::InitFromMmap() {
-  if (!mmap_->handle().ok()) {
-    return;
-  }
-
-  initialized_ =
-      LoadModels(mmap_->handle().start(), mmap_->handle().num_bytes());
-  if (!initialized_) {
-    TC_LOG(ERROR) << "Failed to load models";
-    return;
-  }
-}
-
-namespace {
-
-// Converts sparse features vector to nlp_core::FeatureVector.
-void SparseFeaturesToFeatureVector(
-    const std::vector<int> sparse_features,
-    const nlp_core::NumericFeatureType& feature_type,
-    nlp_core::FeatureVector* result) {
-  for (int feature_id : sparse_features) {
-    const int64 feature_value =
-        nlp_core::FloatFeatureValue(feature_id, 1.0 / sparse_features.size())
-            .discrete_value;
-    result->add(const_cast<nlp_core::NumericFeatureType*>(&feature_type),
-                feature_value);
-  }
-}
-
-// Returns a function that can be used for mapping sparse and dense features
-// to a float feature vector.
-// NOTE: The network object needs to be available at the time when the returned
-// function object is used.
-FeatureVectorFn CreateFeatureVectorFn(const EmbeddingNetwork& network,
-                                      int sparse_embedding_size) {
-  const nlp_core::NumericFeatureType feature_type("chargram_continuous", 0);
-  return [&network, sparse_embedding_size, feature_type](
-             const std::vector<int>& sparse_features,
-             const std::vector<float>& dense_features, float* embedding) {
-    nlp_core::FeatureVector feature_vector;
-    SparseFeaturesToFeatureVector(sparse_features, feature_type,
-                                  &feature_vector);
-
-    if (network.GetEmbedding(feature_vector, 0, embedding)) {
-      for (int i = 0; i < dense_features.size(); i++) {
-        embedding[sparse_embedding_size + i] = dense_features[i];
-      }
-      return true;
-    } else {
-      return false;
-    }
-  };
-}
-
-}  // namespace
-
-void TextClassificationModel::InitializeSharingRegexPatterns(
-    const std::vector<SharingModelOptions::RegexPattern>& patterns) {
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-  // Initialize pattern recognizers.
-  for (const auto& regex_pattern : patterns) {
-    UErrorCode status = U_ZERO_ERROR;
-    std::unique_ptr<icu::RegexPattern> compiled_pattern(
-        icu::RegexPattern::compile(
-            icu::UnicodeString(regex_pattern.pattern().c_str(),
-                               regex_pattern.pattern().size(), "utf-8"),
-            0 /* flags */, status));
-    if (U_FAILURE(status)) {
-      TC_LOG(WARNING) << "Failed to load pattern" << regex_pattern.pattern();
-    } else {
-      regex_patterns_.push_back(
-          {regex_pattern.collection_name(), std::move(compiled_pattern)});
-    }
-  }
-#else
-  if (!patterns.empty()) {
-    TC_LOG(WARNING) << "ICU not supported regexp matchers ignored.";
-  }
-#endif
-}
-
-bool TextClassificationModel::LoadModels(const void* addr, int size) {
-  const char *selection_model, *sharing_model;
-  int selection_model_length, sharing_model_length;
-  if (!ParseMergedModel(addr, size, &selection_model, &selection_model_length,
-                        &sharing_model, &sharing_model_length)) {
-    TC_LOG(ERROR) << "Couldn't parse the model.";
-    return false;
-  }
-
-  selection_params_.reset(
-      ModelParamsBuilder(selection_model, selection_model_length, nullptr));
-  if (!selection_params_.get()) {
-    return false;
-  }
-  selection_options_ = selection_params_->GetSelectionModelOptions();
-  selection_network_.reset(new EmbeddingNetwork(selection_params_.get()));
-  selection_feature_processor_.reset(
-      new FeatureProcessor(selection_params_->GetFeatureProcessorOptions()));
-  selection_feature_fn_ = CreateFeatureVectorFn(
-      *selection_network_, selection_network_->EmbeddingSize(0));
-
-  sharing_params_.reset(
-      ModelParamsBuilder(sharing_model, sharing_model_length,
-                         selection_params_->GetEmbeddingParams()));
-  if (!sharing_params_.get()) {
-    return false;
-  }
-  sharing_options_ = selection_params_->GetSharingModelOptions();
-  sharing_network_.reset(new EmbeddingNetwork(sharing_params_.get()));
-  sharing_feature_processor_.reset(
-      new FeatureProcessor(sharing_params_->GetFeatureProcessorOptions()));
-  sharing_feature_fn_ = CreateFeatureVectorFn(
-      *sharing_network_, sharing_network_->EmbeddingSize(0));
-
-  InitializeSharingRegexPatterns(std::vector<SharingModelOptions::RegexPattern>(
-      sharing_options_.regex_pattern().begin(),
-      sharing_options_.regex_pattern().end()));
-
-  return true;
-}
-
-bool ReadSelectionModelOptions(int fd, ModelOptions* model_options) {
-  ScopedMmap mmap = ScopedMmap(fd);
-  if (!mmap.handle().ok()) {
-    TC_LOG(ERROR) << "Can't mmap.";
-    return false;
-  }
-
-  const char *selection_model, *sharing_model;
-  int selection_model_length, sharing_model_length;
-  if (!ParseMergedModel(mmap.handle().start(), mmap.handle().num_bytes(),
-                        &selection_model, &selection_model_length,
-                        &sharing_model, &sharing_model_length)) {
-    TC_LOG(ERROR) << "Couldn't parse merged model.";
-    return false;
-  }
-
-  MemoryImageReader<EmbeddingNetworkProto> reader(selection_model,
-                                                  selection_model_length);
-
-  auto model_options_extension_id = model_options_in_embedding_network_proto;
-  if (reader.trimmed_proto().HasExtension(model_options_extension_id)) {
-    *model_options =
-        reader.trimmed_proto().GetExtension(model_options_extension_id);
-    return true;
-  } else {
-    return false;
-  }
-}
-
-EmbeddingNetwork::Vector TextClassificationModel::InferInternal(
-    const std::string& context, CodepointSpan span,
-    const FeatureProcessor& feature_processor, const EmbeddingNetwork& network,
-    const FeatureVectorFn& feature_vector_fn,
-    std::vector<CodepointSpan>* selection_label_spans) const {
-  std::vector<Token> tokens;
-  int click_pos;
-  std::unique_ptr<CachedFeatures> cached_features;
-  const int embedding_size = network.EmbeddingSize(0);
-  if (!feature_processor.ExtractFeatures(
-          context, span, /*relative_click_span=*/{0, 0},
-          CreateFeatureVectorFn(network, embedding_size),
-          embedding_size + feature_processor.DenseFeaturesCount(), &tokens,
-          &click_pos, &cached_features)) {
-    TC_VLOG(1) << "Could not extract features.";
-    return {};
-  }
-
-  VectorSpan<float> features;
-  VectorSpan<Token> output_tokens;
-  if (!cached_features->Get(click_pos, &features, &output_tokens)) {
-    TC_VLOG(1) << "Could not extract features.";
-    return {};
-  }
-
-  if (selection_label_spans != nullptr) {
-    if (!feature_processor.SelectionLabelSpans(output_tokens,
-                                               selection_label_spans)) {
-      TC_LOG(ERROR) << "Could not get spans for selection labels.";
-      return {};
-    }
-  }
-
-  std::vector<float> scores;
-  network.ComputeLogits(features, &scores);
-  return scores;
-}
-
-namespace {
-
-// Returns true if given codepoint is contained in the given span in context.
-bool IsCodepointInSpan(const char32 codepoint, const std::string& context,
-                       const CodepointSpan span) {
-  const UnicodeText context_unicode =
-      UTF8ToUnicodeText(context, /*do_copy=*/false);
-
-  auto begin_it = context_unicode.begin();
-  std::advance(begin_it, span.first);
-  auto end_it = context_unicode.begin();
-  std::advance(end_it, span.second);
-
-  return std::find(begin_it, end_it, codepoint) != end_it;
-}
-
-// Returns the first codepoint of the span.
-char32 FirstSpanCodepoint(const std::string& context,
-                          const CodepointSpan span) {
-  const UnicodeText context_unicode =
-      UTF8ToUnicodeText(context, /*do_copy=*/false);
-
-  auto it = context_unicode.begin();
-  std::advance(it, span.first);
-  return *it;
-}
-
-// Returns the last codepoint of the span.
-char32 LastSpanCodepoint(const std::string& context, const CodepointSpan span) {
-  const UnicodeText context_unicode =
-      UTF8ToUnicodeText(context, /*do_copy=*/false);
-
-  auto it = context_unicode.begin();
-  std::advance(it, span.second - 1);
-  return *it;
-}
-
-}  // namespace
-
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-
-namespace {
-
-bool IsOpenBracket(const char32 codepoint) {
-  return u_getIntPropertyValue(codepoint, UCHAR_BIDI_PAIRED_BRACKET_TYPE) ==
-         U_BPT_OPEN;
-}
-
-bool IsClosingBracket(const char32 codepoint) {
-  return u_getIntPropertyValue(codepoint, UCHAR_BIDI_PAIRED_BRACKET_TYPE) ==
-         U_BPT_CLOSE;
-}
-
-}  // namespace
-
-// If the first or the last codepoint of the given span is a bracket, the
-// bracket is stripped if the span does not contain its corresponding paired
-// version.
-CodepointSpan StripUnpairedBrackets(const std::string& context,
-                                    CodepointSpan span) {
-  if (context.empty()) {
-    return span;
-  }
-
-  const char32 begin_char = FirstSpanCodepoint(context, span);
-
-  const char32 paired_begin_char = u_getBidiPairedBracket(begin_char);
-  if (paired_begin_char != begin_char) {
-    if (!IsOpenBracket(begin_char) ||
-        !IsCodepointInSpan(paired_begin_char, context, span)) {
-      ++span.first;
-    }
-  }
-
-  if (span.first == span.second) {
-    return span;
-  }
-
-  const char32 end_char = LastSpanCodepoint(context, span);
-  const char32 paired_end_char = u_getBidiPairedBracket(end_char);
-  if (paired_end_char != end_char) {
-    if (!IsClosingBracket(end_char) ||
-        !IsCodepointInSpan(paired_end_char, context, span)) {
-      --span.second;
-    }
-  }
-
-  // Should not happen, but let's make sure.
-  if (span.first > span.second) {
-    TC_LOG(WARNING) << "Inverse indices result: " << span.first << ", "
-                    << span.second;
-    span.second = span.first;
-  }
-
-  return span;
-}
-#endif
-
-CodepointSpan TextClassificationModel::SuggestSelection(
-    const std::string& context, CodepointSpan click_indices) const {
-  if (!initialized_) {
-    TC_LOG(ERROR) << "Not initialized";
-    return click_indices;
-  }
-
-  const int context_codepoint_size =
-      UTF8ToUnicodeText(context, /*do_copy=*/false).size();
-
-  if (click_indices.first < 0 || click_indices.second < 0 ||
-      click_indices.first >= context_codepoint_size ||
-      click_indices.second > context_codepoint_size ||
-      click_indices.first >= click_indices.second) {
-    TC_VLOG(1) << "Trying to run SuggestSelection with invalid indices: "
-               << click_indices.first << " " << click_indices.second;
-    return click_indices;
-  }
-
-  CodepointSpan result;
-  if (selection_options_.enforce_symmetry()) {
-    result = SuggestSelectionSymmetrical(context, click_indices);
-  } else {
-    float score;
-    std::tie(result, score) = SuggestSelectionInternal(context, click_indices);
-  }
-
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-  if (selection_options_.strip_unpaired_brackets()) {
-    const CodepointSpan stripped_result =
-        StripUnpairedBrackets(context, result);
-    if (stripped_result.first != stripped_result.second) {
-      result = stripped_result;
-    }
-  }
-#endif
-
-  return result;
-}
-
-namespace {
-
-int BestPrediction(const std::vector<float>& scores) {
-  if (!scores.empty()) {
-    const int prediction =
-        std::max_element(scores.begin(), scores.end()) - scores.begin();
-    return prediction;
-  } else {
-    return kInvalidLabel;
-  }
-}
-
-std::pair<CodepointSpan, float> BestSelectionSpan(
-    CodepointSpan original_click_indices, const std::vector<float>& scores,
-    const std::vector<CodepointSpan>& selection_label_spans) {
-  const int prediction = BestPrediction(scores);
-  if (prediction != kInvalidLabel) {
-    std::pair<CodepointIndex, CodepointIndex> selection =
-        selection_label_spans[prediction];
-
-    if (selection.first == kInvalidIndex || selection.second == kInvalidIndex) {
-      TC_VLOG(1) << "Invalid indices predicted, returning input: " << prediction
-                 << " " << selection.first << " " << selection.second;
-      return {original_click_indices, -1.0};
-    }
-
-    return {{selection.first, selection.second}, scores[prediction]};
-  } else {
-    TC_LOG(ERROR) << "Returning default selection: scores.size() = "
-                  << scores.size();
-    return {original_click_indices, -1.0};
-  }
-}
-
-}  // namespace
-
-std::pair<CodepointSpan, float>
-TextClassificationModel::SuggestSelectionInternal(
-    const std::string& context, CodepointSpan click_indices) const {
-  if (!initialized_) {
-    TC_LOG(ERROR) << "Not initialized";
-    return {click_indices, -1.0};
-  }
-
-  std::vector<CodepointSpan> selection_label_spans;
-  EmbeddingNetwork::Vector scores = InferInternal(
-      context, click_indices, *selection_feature_processor_,
-      *selection_network_, selection_feature_fn_, &selection_label_spans);
-  scores = nlp_core::ComputeSoftmax(scores);
-
-  return BestSelectionSpan(click_indices, scores, selection_label_spans);
-}
-
-// Implements a greedy-search-like algorithm for making selections symmetric.
-//
-// Steps:
-// 1. Get a set of selection proposals from places around the clicked word.
-// 2. For each proposal (going from highest-scoring), check if the tokens that
-//    the proposal selects are still free, in which case it claims them, if a
-//    proposal that contains the clicked token is found, it is returned as the
-//    suggestion.
-//
-// This algorithm should ensure that if a selection is proposed, it does not
-// matter which word of it was tapped - all of them will lead to the same
-// selection.
-CodepointSpan TextClassificationModel::SuggestSelectionSymmetrical(
-    const std::string& context, CodepointSpan click_indices) const {
-  const int symmetry_context_size = selection_options_.symmetry_context_size();
-  std::vector<CodepointSpan> chunks = Chunk(
-      context, click_indices, {symmetry_context_size, symmetry_context_size});
-  for (const CodepointSpan& chunk : chunks) {
-    // If chunk and click indices have an overlap, return the chunk.
-    if (!(click_indices.first >= chunk.second ||
-          click_indices.second <= chunk.first)) {
-      return chunk;
-    }
-  }
-
-  return click_indices;
-}
-
-std::vector<std::pair<std::string, float>>
-TextClassificationModel::ClassifyText(const std::string& context,
-                                      CodepointSpan selection_indices,
-                                      int hint_flags) const {
-  if (!initialized_) {
-    TC_LOG(ERROR) << "Not initialized";
-    return {};
-  }
-
-  if (std::get<0>(selection_indices) >= std::get<1>(selection_indices)) {
-    TC_VLOG(1) << "Trying to run ClassifyText with invalid indices: "
-               << std::get<0>(selection_indices) << " "
-               << std::get<1>(selection_indices);
-    return {};
-  }
-
-  if (hint_flags & SELECTION_IS_URL &&
-      sharing_options_.always_accept_url_hint()) {
-    return {{kUrlHintCollection, 1.0}};
-  }
-
-  if (hint_flags & SELECTION_IS_EMAIL &&
-      sharing_options_.always_accept_email_hint()) {
-    return {{kEmailHintCollection, 1.0}};
-  }
-
-  // Check whether any of the regular expressions match.
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-  const std::string selection_text =
-      ExtractSelection(context, selection_indices);
-  for (const CompiledRegexPattern& regex_pattern : regex_patterns_) {
-    if (MatchesRegex(regex_pattern.pattern.get(), selection_text)) {
-      return {{regex_pattern.collection_name, 1.0}};
-    }
-  }
-#endif
-
-  EmbeddingNetwork::Vector scores =
-      InferInternal(context, selection_indices, *sharing_feature_processor_,
-                    *sharing_network_, sharing_feature_fn_, nullptr);
-  if (scores.empty() ||
-      scores.size() != sharing_feature_processor_->NumCollections()) {
-    TC_VLOG(1) << "Using default class: scores.size() = " << scores.size();
-    return {};
-  }
-
-  scores = nlp_core::ComputeSoftmax(scores);
-
-  std::vector<std::pair<std::string, float>> result(scores.size());
-  for (int i = 0; i < scores.size(); i++) {
-    result[i] = {sharing_feature_processor_->LabelToCollection(i), scores[i]};
-  }
-  std::sort(result.begin(), result.end(),
-            [](const std::pair<std::string, float>& a,
-               const std::pair<std::string, float>& b) {
-              return a.second > b.second;
-            });
-
-  // Phone class sanity check.
-  if (result.begin()->first == kPhoneCollection) {
-    const int digit_count = CountDigits(context, selection_indices);
-    if (digit_count < sharing_options_.phone_min_num_digits() ||
-        digit_count > sharing_options_.phone_max_num_digits()) {
-      return {{kOtherCollection, 1.0}};
-    }
-  }
-
-  return result;
-}
-
-std::vector<CodepointSpan> TextClassificationModel::Chunk(
-    const std::string& context, CodepointSpan click_span,
-    TokenSpan relative_click_span) const {
-  std::unique_ptr<CachedFeatures> cached_features;
-  std::vector<Token> tokens;
-  int click_index;
-  int embedding_size = selection_network_->EmbeddingSize(0);
-  if (!selection_feature_processor_->ExtractFeatures(
-          context, click_span, relative_click_span, selection_feature_fn_,
-          embedding_size + selection_feature_processor_->DenseFeaturesCount(),
-          &tokens, &click_index, &cached_features)) {
-    TC_VLOG(1) << "Couldn't ExtractFeatures.";
-    return {};
-  }
-
-  int first_token;
-  int last_token;
-  if (relative_click_span.first == kInvalidIndex ||
-      relative_click_span.second == kInvalidIndex) {
-    first_token = 0;
-    last_token = tokens.size();
-  } else {
-    first_token = click_index - relative_click_span.first;
-    last_token = click_index + relative_click_span.second + 1;
-  }
-
-  struct SelectionProposal {
-    int label;
-    int token_index;
-    CodepointSpan span;
-    float score;
-  };
-
-  // Scan in the symmetry context for selection span proposals.
-  std::vector<SelectionProposal> proposals;
-  for (int token_index = first_token; token_index < last_token; ++token_index) {
-    if (token_index < 0 || token_index >= tokens.size() ||
-        tokens[token_index].is_padding) {
-      continue;
-    }
-
-    float score;
-    VectorSpan<float> features;
-    VectorSpan<Token> output_tokens;
-    std::vector<CodepointSpan> selection_label_spans;
-    CodepointSpan span;
-    if (cached_features->Get(token_index, &features, &output_tokens) &&
-        selection_feature_processor_->SelectionLabelSpans(
-            output_tokens, &selection_label_spans)) {
-      // Add an implicit proposal for each token to be by itself. Every
-      // token should be now represented in the results.
-      proposals.push_back(
-          SelectionProposal{0, token_index, selection_label_spans[0], 0.0});
-
-      std::vector<float> scores;
-      selection_network_->ComputeLogits(features, &scores);
-
-      scores = nlp_core::ComputeSoftmax(scores);
-      std::tie(span, score) = BestSelectionSpan({kInvalidIndex, kInvalidIndex},
-                                                scores, selection_label_spans);
-      if (span.first != kInvalidIndex && span.second != kInvalidIndex &&
-          score >= 0) {
-        const int prediction = BestPrediction(scores);
-        proposals.push_back(
-            SelectionProposal{prediction, token_index, span, score});
-      }
-    } else {
-      // Add an implicit proposal for each token to be by itself. Every token
-      // should be now represented in the results.
-      proposals.push_back(SelectionProposal{
-          0,
-          token_index,
-          {tokens[token_index].start, tokens[token_index].end},
-          0.0});
-    }
-  }
-
-  // Sort selection span proposals by their respective probabilities.
-  std::sort(proposals.begin(), proposals.end(),
-            [](const SelectionProposal& a, const SelectionProposal& b) {
-              return a.score > b.score;
-            });
-
-  // Go from the highest-scoring proposal and claim tokens. Tokens are marked as
-  // claimed by the higher-scoring selection proposals, so that the
-  // lower-scoring ones cannot use them. Returns the selection proposal if it
-  // contains the clicked token.
-  std::vector<CodepointSpan> result;
-  std::vector<bool> token_used(tokens.size(), false);
-  for (const SelectionProposal& proposal : proposals) {
-    const int predicted_label = proposal.label;
-    TokenSpan relative_span;
-    if (!selection_feature_processor_->LabelToTokenSpan(predicted_label,
-                                                        &relative_span)) {
-      continue;
-    }
-    TokenSpan span;
-    span.first = proposal.token_index - relative_span.first;
-    span.second = proposal.token_index + relative_span.second + 1;
-
-    if (span.first != kInvalidIndex && span.second != kInvalidIndex) {
-      bool feasible = true;
-      for (int i = span.first; i < span.second; i++) {
-        if (token_used[i]) {
-          feasible = false;
-          break;
-        }
-      }
-
-      if (feasible) {
-        result.push_back(proposal.span);
-        for (int i = span.first; i < span.second; i++) {
-          token_used[i] = true;
-        }
-      }
-    }
-  }
-
-  std::sort(result.begin(), result.end(),
-            [](const CodepointSpan& a, const CodepointSpan& b) {
-              return a.first < b.first;
-            });
-
-  return result;
-}
-
-std::vector<TextClassificationModel::AnnotatedSpan>
-TextClassificationModel::Annotate(const std::string& context) const {
-  std::vector<CodepointSpan> chunks;
-  const UnicodeText context_unicode = UTF8ToUnicodeText(context,
-                                                        /*do_copy=*/false);
-  for (const UnicodeTextRange& line :
-       selection_feature_processor_->SplitContext(context_unicode)) {
-    const std::vector<CodepointSpan> local_chunks =
-        Chunk(UnicodeText::UTF8Substring(line.first, line.second),
-              /*click_span=*/{kInvalidIndex, kInvalidIndex},
-              /*relative_click_span=*/{kInvalidIndex, kInvalidIndex});
-    const int offset = std::distance(context_unicode.begin(), line.first);
-    for (CodepointSpan chunk : local_chunks) {
-      chunks.push_back({chunk.first + offset, chunk.second + offset});
-    }
-  }
-
-  std::vector<TextClassificationModel::AnnotatedSpan> result;
-  for (const CodepointSpan& chunk : chunks) {
-    result.emplace_back();
-    result.back().span = chunk;
-    result.back().classification = ClassifyText(context, chunk);
-  }
-  return result;
-}
-
-}  // namespace libtextclassifier
diff --git a/smartselect/text-classification-model.h b/smartselect/text-classification-model.h
deleted file mode 100644
index d0df193..0000000
--- a/smartselect/text-classification-model.h
+++ /dev/null
@@ -1,196 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// Inference code for the feed-forward text classification models.
-
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_TEXT_CLASSIFICATION_MODEL_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_TEXT_CLASSIFICATION_MODEL_H_
-
-#include <memory>
-#include <set>
-#include <string>
-
-#include "common/embedding-network.h"
-#include "common/feature-extractor.h"
-#include "common/memory_image/embedding-network-params-from-image.h"
-#include "common/mmap.h"
-#include "smartselect/feature-processor.h"
-#include "smartselect/model-params.h"
-#include "smartselect/text-classification-model.pb.h"
-#include "smartselect/types.h"
-
-namespace libtextclassifier {
-
-// SmartSelection/Sharing feed-forward model.
-class TextClassificationModel {
- public:
-  // Represents a result of Annotate call.
-  struct AnnotatedSpan {
-    // Unicode codepoint indices in the input string.
-    CodepointSpan span = {kInvalidIndex, kInvalidIndex};
-
-    // Classification result for the span.
-    std::vector<std::pair<std::string, float>> classification;
-  };
-
-  // Loads TextClassificationModel from given file given by an int
-  // file descriptor.
-  // Offset is byte a position in the file to the beginning of the model data.
-  TextClassificationModel(int fd, int offset, int size);
-
-  // Same as above but the whole file is mapped and it is assumed the model
-  // starts at offset 0.
-  explicit TextClassificationModel(int fd);
-
-  // Loads TextClassificationModel from given file.
-  explicit TextClassificationModel(const std::string& path);
-
-  // Loads TextClassificationModel from given location in memory.
-  TextClassificationModel(const void* addr, int size);
-
-  // Returns true if the model is ready for use.
-  bool IsInitialized() { return initialized_; }
-
-  // Bit flags for the input selection.
-  enum SelectionInputFlags { SELECTION_IS_URL = 0x1, SELECTION_IS_EMAIL = 0x2 };
-
-  // Runs inference for given a context and current selection (i.e. index
-  // of the first and one past last selected characters (utf8 codepoint
-  // offsets)). Returns the indices (utf8 codepoint offsets) of the selection
-  // beginning character and one past selection end character.
-  // Returns the original click_indices if an error occurs.
-  // NOTE: The selection indices are passed in and returned in terms of
-  // UTF8 codepoints (not bytes).
-  // Requires that the model is a smart selection model.
-  CodepointSpan SuggestSelection(const std::string& context,
-                                 CodepointSpan click_indices) const;
-
-  // Classifies the selected text given the context string.
-  // Requires that the model is a smart sharing model.
-  // Returns an empty result if an error occurs.
-  std::vector<std::pair<std::string, float>> ClassifyText(
-      const std::string& context, CodepointSpan click_indices,
-      int input_flags = 0) const;
-
-  // Annotates given input text. The annotations should cover the whole input
-  // context except for whitespaces, and are sorted by their position in the
-  // context string.
-  std::vector<AnnotatedSpan> Annotate(const std::string& context) const;
-
- protected:
-  // Initializes the model from mmap_ file.
-  void InitFromMmap();
-
-  // Extracts chunks from the context. The extraction proceeds from the center
-  // token determined by click_span and looks at relative_click_span tokens
-  // left and right around the click position.
-  // If relative_click_span == {kInvalidIndex, kInvalidIndex} then the whole
-  // context is considered, regardless of the click_span.
-  // Returns the chunks sorted by their position in the context string.
-  std::vector<CodepointSpan> Chunk(const std::string& context,
-                                   CodepointSpan click_span,
-                                   TokenSpan relative_click_span) const;
-
-  // During evaluation we need access to the feature processor.
-  FeatureProcessor* SelectionFeatureProcessor() const {
-    return selection_feature_processor_.get();
-  }
-
-  void InitializeSharingRegexPatterns(
-      const std::vector<SharingModelOptions::RegexPattern>& patterns);
-
-  // Collection name when url hint is accepted.
-  const std::string kUrlHintCollection = "url";
-
-  // Collection name when email hint is accepted.
-  const std::string kEmailHintCollection = "email";
-
-  // Collection name for other.
-  const std::string kOtherCollection = "other";
-
-  // Collection name for phone.
-  const std::string kPhoneCollection = "phone";
-
-  SelectionModelOptions selection_options_;
-  SharingModelOptions sharing_options_;
-
- private:
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-  struct CompiledRegexPattern {
-    std::string collection_name;
-    std::unique_ptr<icu::RegexPattern> pattern;
-  };
-#endif
-
-  bool LoadModels(const void* addr, int size);
-
-  nlp_core::EmbeddingNetwork::Vector InferInternal(
-      const std::string& context, CodepointSpan span,
-      const FeatureProcessor& feature_processor,
-      const nlp_core::EmbeddingNetwork& network,
-      const FeatureVectorFn& feature_vector_fn,
-      std::vector<CodepointSpan>* selection_label_spans) const;
-
-  // Returns a selection suggestion with a score.
-  std::pair<CodepointSpan, float> SuggestSelectionInternal(
-      const std::string& context, CodepointSpan click_indices) const;
-
-  // Returns a selection suggestion and makes sure it's symmetric. Internally
-  // runs several times SuggestSelectionInternal.
-  CodepointSpan SuggestSelectionSymmetrical(const std::string& context,
-                                            CodepointSpan click_indices) const;
-
-  bool initialized_ = false;
-  std::unique_ptr<nlp_core::ScopedMmap> mmap_;
-  std::unique_ptr<ModelParams> selection_params_;
-  std::unique_ptr<FeatureProcessor> selection_feature_processor_;
-  std::unique_ptr<nlp_core::EmbeddingNetwork> selection_network_;
-  FeatureVectorFn selection_feature_fn_;
-  std::unique_ptr<FeatureProcessor> sharing_feature_processor_;
-  std::unique_ptr<ModelParams> sharing_params_;
-  std::unique_ptr<nlp_core::EmbeddingNetwork> sharing_network_;
-  FeatureVectorFn sharing_feature_fn_;
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-  std::vector<CompiledRegexPattern> regex_patterns_;
-#endif
-};
-
-// If the first or the last codepoint of the given span is a bracket, the
-// bracket is stripped if the span does not contain its corresponding paired
-// version.
-CodepointSpan StripUnpairedBrackets(const std::string& context,
-                                    CodepointSpan span);
-
-// Parses the merged image given as a file descriptor, and reads
-// the ModelOptions proto from the selection model.
-bool ReadSelectionModelOptions(int fd, ModelOptions* model_options);
-
-// Pretty-printing function for TextClassificationModel::AnnotatedSpan.
-inline std::ostream& operator<<(
-    std::ostream& os, const TextClassificationModel::AnnotatedSpan& span) {
-  std::string best_class;
-  float best_score = -1;
-  if (!span.classification.empty()) {
-    best_class = span.classification[0].first;
-    best_score = span.classification[0].second;
-  }
-  return os << "Span(" << span.span.first << ", " << span.span.second << ", "
-            << best_class << ", " << best_score << ")";
-}
-
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_SMARTSELECT_TEXT_CLASSIFICATION_MODEL_H_
diff --git a/smartselect/text-classification-model.proto b/smartselect/text-classification-model.proto
deleted file mode 100644
index 315e849..0000000
--- a/smartselect/text-classification-model.proto
+++ /dev/null
@@ -1,234 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// Text classification model configuration.
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-import "external/libtextclassifier/common/embedding-network.proto";
-import "external/libtextclassifier/smartselect/tokenizer.proto";
-
-package libtextclassifier;
-
-// Generic options of a model, non-specific to selection or sharing.
-message ModelOptions {
-  // If true, will use embeddings from a different model. This is mainly useful
-  // for the Sharing model using the embeddings from the Selection model.
-  optional bool use_shared_embeddings = 1;
-
-  // Language of the model.
-  optional string language = 2;
-
-  // Version of the model.
-  optional int32 version = 3;
-}
-
-message SelectionModelOptions {
-  // A list of Unicode codepoints to strip from predicted selections.
-  repeated int32 deprecated_punctuation_to_strip = 1;
-
-  // Enforce symmetrical selections.
-  optional bool enforce_symmetry = 3;
-
-  // Number of inferences made around the click position (to one side), for
-  // enforcing symmetry.
-  optional int32 symmetry_context_size = 4;
-
-  // If true, before the selection is returned, the unpaired brackets contained
-  // in the predicted selection are stripped from the both selection ends.
-  // The bracket codepoints are defined in the Unicode standard:
-  // http://www.unicode.org/Public/UNIDATA/BidiBrackets.txt
-  optional bool strip_unpaired_brackets = 5 [default = true];
-
-  reserved 2;
-}
-
-message SharingModelOptions {
-  // If true, will always return "url" when the url hint is passed in.
-  optional bool always_accept_url_hint = 1;
-
-  // If true, will always return "email" when the e-mail hint is passed in.
-  optional bool always_accept_email_hint = 2;
-
-  // Limits for phone numbers.
-  optional int32 phone_min_num_digits = 3 [default = 7];
-  optional int32 phone_max_num_digits = 4 [default = 15];
-
-  // List of regular expression matchers to check.
-  message RegexPattern {
-    // The name of the collection of a match.
-    optional string collection_name = 1;
-
-    // The pattern to check.
-    optional string pattern = 2;
-  }
-  repeated RegexPattern regex_pattern = 5;
-}
-
-// Next ID: 41
-message FeatureProcessorOptions {
-  // Number of buckets used for hashing charactergrams.
-  optional int32 num_buckets = 1 [default = -1];
-
-  // Context size defines the number of words to the left and to the right of
-  // the selected word to be used as context. For example, if context size is
-  // N, then we take N words to the left and N words to the right of the
-  // selected word as its context.
-  optional int32 context_size = 2 [default = -1];
-
-  // Maximum number of words of the context to select in total.
-  optional int32 max_selection_span = 3 [default = -1];
-
-  // Orders of charactergrams to extract. E.g., 2 means character bigrams, 3
-  // character trigrams etc.
-  repeated int32 chargram_orders = 4;
-
-  // Maximum length of a word, in codepoints.
-  optional int32 max_word_length = 21 [default = 20];
-
-  // If true, will use the unicode-aware functionality for extracting features.
-  optional bool unicode_aware_features = 19 [default = false];
-
-  // Whether to extract the token case feature.
-  optional bool extract_case_feature = 5 [default = false];
-
-  // Whether to extract the selection mask feature.
-  optional bool extract_selection_mask_feature = 6 [default = false];
-
-  // List of regexps to run over each token. For each regexp, if there is a
-  // match, a dense feature of 1.0 is emitted. Otherwise -1.0 is used.
-  repeated string regexp_feature = 22;
-
-  // Whether to remap all digits to a single number.
-  optional bool remap_digits = 20 [default = false];
-
-  // Whether to lower-case each token before generating hashgrams.
-  optional bool lowercase_tokens = 33;
-
-  // If true, the selection classifier output will contain only the selections
-  // that are feasible (e.g., those that are shorter than max_selection_span),
-  // if false, the output will be a complete cross-product of possible
-  // selections to the left and posible selections to the right, including the
-  // infeasible ones.
-  // NOTE: Exists mainly for compatibility with older models that were trained
-  // with the non-reduced output space.
-  optional bool selection_reduced_output_space = 8 [default = true];
-
-  // Collection names.
-  repeated string collections = 9;
-
-  // An index of collection in collections to be used if a collection name can't
-  // be mapped to an id.
-  optional int32 default_collection = 10 [default = -1];
-
-  // If true, will split the input by lines, and only use the line that contains
-  // the clicked token.
-  optional bool only_use_line_with_click = 13 [default = false];
-
-  // If true, will split tokens that contain the selection boundary, at the
-  // position of the boundary.
-  // E.g. "foo{bar}@google.com" -> "foo", "bar", "@google.com"
-  optional bool split_tokens_on_selection_boundaries = 14 [default = false];
-
-  // Codepoint ranges that determine how different codepoints are tokenized.
-  // The ranges must not overlap.
-  repeated TokenizationCodepointRange tokenization_codepoint_config = 15;
-
-  // Method for selecting the center token.
-  enum CenterTokenSelectionMethod {
-    DEFAULT_CENTER_TOKEN_METHOD = 0;  // Invalid option.
-
-    // Use click indices to determine the center token.
-    CENTER_TOKEN_FROM_CLICK = 1;
-
-    // Use selection indices to get a token range, and select the middle of it
-    // as the center token.
-    CENTER_TOKEN_MIDDLE_OF_SELECTION = 2;
-  }
-  optional CenterTokenSelectionMethod center_token_selection_method = 16;
-
-  // If true, span boundaries will be snapped to containing tokens and not
-  // required to exactly match token boundaries.
-  optional bool snap_label_span_boundaries_to_containing_tokens = 18;
-
-  // Range of codepoints start - end, where end is exclusive.
-  message CodepointRange {
-    optional int32 start = 1;
-    optional int32 end = 2;
-  }
-
-  // A set of codepoint ranges supported by the model.
-  repeated CodepointRange supported_codepoint_ranges = 23;
-
-  // A set of codepoint ranges to use in the mixed tokenization mode to identify
-  // stretches of tokens to re-tokenize using the internal tokenizer.
-  repeated CodepointRange internal_tokenizer_codepoint_ranges = 34;
-
-  // Minimum ratio of supported codepoints in the input context. If the ratio
-  // is lower than this, the feature computation will fail.
-  optional float min_supported_codepoint_ratio = 24 [default = 0.0];
-
-  // Used for versioning the format of features the model expects.
-  //  - feature_version == 0:
-  //      For each token the features consist of:
-  //       - chargram embeddings
-  //       - dense features
-  //      Chargram embeddings for tokens are concatenated first together,
-  //      and at the end, the dense features for the tokens are concatenated
-  //      to it. So the resulting feature vector has two regions.
-  optional int32 feature_version = 25 [default = 0];
-
-  // Controls the type of tokenization the model will use for the input text.
-  enum TokenizationType {
-    INVALID_TOKENIZATION_TYPE = 0;
-
-    // Use the internal tokenizer for tokenization.
-    INTERNAL_TOKENIZER = 1;
-
-    // Use ICU for tokenization.
-    ICU = 2;
-
-    // First apply ICU tokenization. Then identify stretches of tokens
-    // consisting only of codepoints in internal_tokenizer_codepoint_ranges
-    // and re-tokenize them using the internal tokenizer.
-    MIXED = 3;
-  }
-  optional TokenizationType tokenization_type = 30
-      [default = INTERNAL_TOKENIZER];
-  optional bool icu_preserve_whitespace_tokens = 31 [default = false];
-
-  // List of codepoints that will be stripped from beginning and end of
-  // predicted spans.
-  repeated int32 ignored_span_boundary_codepoints = 36;
-
-  reserved 7, 11, 12, 26, 27, 28, 29, 32, 35, 39, 40;
-
-  // List of allowed charactergrams. The extracted charactergrams are filtered
-  // using this list, and charactergrams that are not present are interpreted as
-  // out-of-vocabulary.
-  // If no allowed_chargrams are specified, all charactergrams are allowed.
-  // The field is typed as bytes type to allow non-UTF8 chargrams.
-  repeated bytes allowed_chargrams = 38;
-};
-
-extend nlp_core.EmbeddingNetworkProto {
-  optional ModelOptions model_options_in_embedding_network_proto = 150063045;
-  optional FeatureProcessorOptions
-      feature_processor_options_in_embedding_network_proto = 146230910;
-  optional SelectionModelOptions
-      selection_model_options_in_embedding_network_proto = 148190899;
-  optional SharingModelOptions
-      sharing_model_options_in_embedding_network_proto = 151445439;
-}
diff --git a/smartselect/text-classification-model_test.cc b/smartselect/text-classification-model_test.cc
deleted file mode 100644
index 5550e53..0000000
--- a/smartselect/text-classification-model_test.cc
+++ /dev/null
@@ -1,440 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/text-classification-model.h"
-
-#include <fcntl.h>
-#include <stdio.h>
-#include <fstream>
-#include <iostream>
-#include <memory>
-#include <string>
-
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace {
-
-std::string ReadFile(const std::string& file_name) {
-  std::ifstream file_stream(file_name);
-  return std::string(std::istreambuf_iterator<char>(file_stream), {});
-}
-
-std::string GetModelPath() {
-  return TEST_DATA_DIR "smartselection.model";
-}
-
-std::string GetURLRegexPath() {
-  return TEST_DATA_DIR "regex_url.txt";
-}
-
-std::string GetEmailRegexPath() {
-  return TEST_DATA_DIR "regex_email.txt";
-}
-
-TEST(TextClassificationModelTest, StripUnpairedBrackets) {
-  // Stripping brackets strip brackets from length 1 bracket only selections.
-  EXPECT_EQ(StripUnpairedBrackets("call me at ) today", {11, 12}),
-            std::make_pair(12, 12));
-  EXPECT_EQ(StripUnpairedBrackets("call me at ( today", {11, 12}),
-            std::make_pair(12, 12));
-}
-
-TEST(TextClassificationModelTest, ReadModelOptions) {
-  const std::string model_path = GetModelPath();
-  int fd = open(model_path.c_str(), O_RDONLY);
-  ModelOptions model_options;
-  ASSERT_TRUE(ReadSelectionModelOptions(fd, &model_options));
-  close(fd);
-
-  EXPECT_EQ("en", model_options.language());
-  EXPECT_GT(model_options.version(), 0);
-}
-
-TEST(TextClassificationModelTest, SuggestSelection) {
-  const std::string model_path = GetModelPath();
-  int fd = open(model_path.c_str(), O_RDONLY);
-  std::unique_ptr<TextClassificationModel> model(
-      new TextClassificationModel(fd));
-  close(fd);
-
-  EXPECT_EQ(model->SuggestSelection(
-                "this afternoon Barack Obama gave a speech at", {15, 21}),
-            std::make_pair(15, 27));
-
-  // Try passing whole string.
-  // If more than 1 token is specified, we should return back what entered.
-  EXPECT_EQ(model->SuggestSelection("350 Third Street, Cambridge", {0, 27}),
-            std::make_pair(0, 27));
-
-  // Single letter.
-  EXPECT_EQ(std::make_pair(0, 1), model->SuggestSelection("a", {0, 1}));
-
-  // Single word.
-  EXPECT_EQ(std::make_pair(0, 4), model->SuggestSelection("asdf", {0, 4}));
-
-  EXPECT_EQ(model->SuggestSelection("call me at 857 225 3556 today", {11, 14}),
-            std::make_pair(11, 23));
-
-  // Unpaired bracket stripping.
-  EXPECT_EQ(
-      model->SuggestSelection("call me at (857) 225 3556 today", {11, 16}),
-      std::make_pair(11, 25));
-  EXPECT_EQ(model->SuggestSelection("call me at (857 225 3556 today", {11, 15}),
-            std::make_pair(12, 24));
-  EXPECT_EQ(model->SuggestSelection("call me at 857 225 3556) today", {11, 14}),
-            std::make_pair(11, 23));
-  EXPECT_EQ(
-      model->SuggestSelection("call me at )857 225 3556( today", {11, 15}),
-      std::make_pair(12, 24));
-
-  // If the resulting selection would be empty, the original span is returned.
-  EXPECT_EQ(model->SuggestSelection("call me at )( today", {11, 13}),
-            std::make_pair(11, 13));
-  EXPECT_EQ(model->SuggestSelection("call me at ( today", {11, 12}),
-            std::make_pair(11, 12));
-  EXPECT_EQ(model->SuggestSelection("call me at ) today", {11, 12}),
-            std::make_pair(11, 12));
-}
-
-TEST(TextClassificationModelTest, SuggestSelectionsAreSymmetric) {
-  const std::string model_path = GetModelPath();
-  int fd = open(model_path.c_str(), O_RDONLY);
-  std::unique_ptr<TextClassificationModel> model(
-      new TextClassificationModel(fd));
-  close(fd);
-
-  EXPECT_EQ(std::make_pair(0, 27),
-            model->SuggestSelection("350 Third Street, Cambridge", {0, 3}));
-  EXPECT_EQ(std::make_pair(0, 27),
-            model->SuggestSelection("350 Third Street, Cambridge", {4, 9}));
-  EXPECT_EQ(std::make_pair(0, 27),
-            model->SuggestSelection("350 Third Street, Cambridge", {10, 16}));
-  EXPECT_EQ(std::make_pair(6, 33),
-            model->SuggestSelection("a\nb\nc\n350 Third Street, Cambridge",
-                                    {16, 22}));
-}
-
-TEST(TextClassificationModelTest, SuggestSelectionWithNewLine) {
-  const std::string model_path = GetModelPath();
-  int fd = open(model_path.c_str(), O_RDONLY);
-  std::unique_ptr<TextClassificationModel> model(
-      new TextClassificationModel(fd));
-  close(fd);
-
-  std::tuple<int, int> selection;
-  selection = model->SuggestSelection("abc\nBarack Obama", {4, 10});
-  EXPECT_EQ(4, std::get<0>(selection));
-  EXPECT_EQ(16, std::get<1>(selection));
-
-  selection = model->SuggestSelection("Barack Obama\nabc", {0, 6});
-  EXPECT_EQ(0, std::get<0>(selection));
-  EXPECT_EQ(12, std::get<1>(selection));
-}
-
-TEST(TextClassificationModelTest, SuggestSelectionWithPunctuation) {
-  const std::string model_path = GetModelPath();
-  int fd = open(model_path.c_str(), O_RDONLY);
-  std::unique_ptr<TextClassificationModel> model(
-      new TextClassificationModel(fd));
-  close(fd);
-
-  std::tuple<int, int> selection;
-
-  // From the right.
-  selection = model->SuggestSelection(
-      "this afternoon Barack Obama, gave a speech at", {15, 21});
-  EXPECT_EQ(15, std::get<0>(selection));
-  EXPECT_EQ(27, std::get<1>(selection));
-
-  // From the right multiple.
-  selection = model->SuggestSelection(
-      "this afternoon Barack Obama,.,.,, gave a speech at", {15, 21});
-  EXPECT_EQ(15, std::get<0>(selection));
-  EXPECT_EQ(27, std::get<1>(selection));
-
-  // From the left multiple.
-  selection = model->SuggestSelection(
-      "this afternoon ,.,.,,Barack Obama gave a speech at", {21, 27});
-  EXPECT_EQ(21, std::get<0>(selection));
-  EXPECT_EQ(27, std::get<1>(selection));
-
-  // From both sides.
-  selection = model->SuggestSelection(
-      "this afternoon !Barack Obama,- gave a speech at", {16, 22});
-  EXPECT_EQ(16, std::get<0>(selection));
-  EXPECT_EQ(28, std::get<1>(selection));
-}
-
-class TestingTextClassificationModel
-    : public libtextclassifier::TextClassificationModel {
- public:
-  explicit TestingTextClassificationModel(int fd)
-      : libtextclassifier::TextClassificationModel(fd) {}
-
-  using TextClassificationModel::InitializeSharingRegexPatterns;
-
-  void DisableClassificationHints() {
-    sharing_options_.set_always_accept_url_hint(false);
-    sharing_options_.set_always_accept_email_hint(false);
-  }
-};
-
-TEST(TextClassificationModelTest, SuggestSelectionNoCrashWithJunk) {
-  const std::string model_path = GetModelPath();
-  int fd = open(model_path.c_str(), O_RDONLY);
-  std::unique_ptr<TextClassificationModel> ff_model(
-      new TextClassificationModel(fd));
-  close(fd);
-
-  std::tuple<int, int> selection;
-
-  // Try passing in bunch of invalid selections.
-  selection = ff_model->SuggestSelection("", {0, 27});
-  // If more than 1 token is specified, we should return back what entered.
-  EXPECT_EQ(0, std::get<0>(selection));
-  EXPECT_EQ(27, std::get<1>(selection));
-
-  selection = ff_model->SuggestSelection("", {-10, 27});
-  // If more than 1 token is specified, we should return back what entered.
-  EXPECT_EQ(-10, std::get<0>(selection));
-  EXPECT_EQ(27, std::get<1>(selection));
-
-  selection = ff_model->SuggestSelection("Word 1 2 3 hello!", {0, 27});
-  // If more than 1 token is specified, we should return back what entered.
-  EXPECT_EQ(0, std::get<0>(selection));
-  EXPECT_EQ(27, std::get<1>(selection));
-
-  selection = ff_model->SuggestSelection("Word 1 2 3 hello!", {-30, 300});
-  // If more than 1 token is specified, we should return back what entered.
-  EXPECT_EQ(-30, std::get<0>(selection));
-  EXPECT_EQ(300, std::get<1>(selection));
-
-  selection = ff_model->SuggestSelection("Word 1 2 3 hello!", {-10, -1});
-  // If more than 1 token is specified, we should return back what entered.
-  EXPECT_EQ(-10, std::get<0>(selection));
-  EXPECT_EQ(-1, std::get<1>(selection));
-
-  selection = ff_model->SuggestSelection("Word 1 2 3 hello!", {100, 17});
-  // If more than 1 token is specified, we should return back what entered.
-  EXPECT_EQ(100, std::get<0>(selection));
-  EXPECT_EQ(17, std::get<1>(selection));
-}
-
-namespace {
-
-std::string FindBestResult(std::vector<std::pair<std::string, float>> results) {
-  if (results.empty()) {
-    return "<INVALID RESULTS>";
-  }
-
-  std::sort(results.begin(), results.end(),
-            [](const std::pair<std::string, float> a,
-               const std::pair<std::string, float> b) {
-              return a.second > b.second;
-            });
-  return results[0].first;
-}
-
-}  // namespace
-
-TEST(TextClassificationModelTest, ClassifyText) {
-  const std::string model_path = GetModelPath();
-  int fd = open(model_path.c_str(), O_RDONLY);
-  std::unique_ptr<TestingTextClassificationModel> model(
-      new TestingTextClassificationModel(fd));
-  close(fd);
-
-  model->DisableClassificationHints();
-  EXPECT_EQ("other",
-            FindBestResult(model->ClassifyText(
-                "this afternoon Barack Obama gave a speech at", {15, 27})));
-  EXPECT_EQ("other",
-            FindBestResult(model->ClassifyText("you@android.com", {0, 15})));
-  EXPECT_EQ("other", FindBestResult(model->ClassifyText(
-                         "Contact me at you@android.com", {14, 29})));
-  EXPECT_EQ("phone", FindBestResult(model->ClassifyText(
-                         "Call me at (800) 123-456 today", {11, 24})));
-  EXPECT_EQ("other", FindBestResult(model->ClassifyText(
-                         "Visit www.google.com every today!", {6, 20})));
-
-  // More lines.
-  EXPECT_EQ("other",
-            FindBestResult(model->ClassifyText(
-                "this afternoon Barack Obama gave a speech at|Visit "
-                "www.google.com every today!|Call me at (800) 123-456 today.",
-                {15, 27})));
-  EXPECT_EQ("other",
-            FindBestResult(model->ClassifyText(
-                "this afternoon Barack Obama gave a speech at|Visit "
-                "www.google.com every today!|Call me at (800) 123-456 today.",
-                {51, 65})));
-  EXPECT_EQ("phone",
-            FindBestResult(model->ClassifyText(
-                "this afternoon Barack Obama gave a speech at|Visit "
-                "www.google.com every today!|Call me at (800) 123-456 today.",
-                {90, 103})));
-
-  // Single word.
-  EXPECT_EQ("other", FindBestResult(model->ClassifyText("obama", {0, 5})));
-  EXPECT_EQ("other", FindBestResult(model->ClassifyText("asdf", {0, 4})));
-  EXPECT_EQ("<INVALID RESULTS>",
-            FindBestResult(model->ClassifyText("asdf", {0, 0})));
-
-  // Junk.
-  EXPECT_EQ("<INVALID RESULTS>",
-            FindBestResult(model->ClassifyText("", {0, 0})));
-  EXPECT_EQ("<INVALID RESULTS>", FindBestResult(model->ClassifyText(
-                                     "a\n\n\n\nx x x\n\n\n\n\n\n", {1, 5})));
-}
-
-TEST(TextClassificationModelTest, ClassifyTextWithHints) {
-  const std::string model_path = GetModelPath();
-  int fd = open(model_path.c_str(), O_RDONLY);
-  std::unique_ptr<TestingTextClassificationModel> model(
-      new TestingTextClassificationModel(fd));
-  close(fd);
-
-  // When EMAIL hint is passed, the result should be email.
-  EXPECT_EQ("email",
-            FindBestResult(model->ClassifyText(
-                "x", {0, 1}, TextClassificationModel::SELECTION_IS_EMAIL)));
-  // When URL hint is passed, the result should be email.
-  EXPECT_EQ("url",
-            FindBestResult(model->ClassifyText(
-                "x", {0, 1}, TextClassificationModel::SELECTION_IS_URL)));
-  // When both hints are passed, the result should be url (as it's probably
-  // better to let Chrome handle this case).
-  EXPECT_EQ("url", FindBestResult(model->ClassifyText(
-                       "x", {0, 1},
-                       TextClassificationModel::SELECTION_IS_EMAIL |
-                           TextClassificationModel::SELECTION_IS_URL)));
-
-  // With disabled hints, we should get the same prediction regardless of the
-  // hint.
-  model->DisableClassificationHints();
-  EXPECT_EQ(model->ClassifyText("x", {0, 1}, 0),
-            model->ClassifyText("x", {0, 1},
-                                TextClassificationModel::SELECTION_IS_EMAIL));
-
-  EXPECT_EQ(model->ClassifyText("x", {0, 1}, 0),
-            model->ClassifyText("x", {0, 1},
-                                TextClassificationModel::SELECTION_IS_URL));
-}
-
-TEST(TextClassificationModelTest, PhoneFiltering) {
-  const std::string model_path = GetModelPath();
-  int fd = open(model_path.c_str(), O_RDONLY);
-  std::unique_ptr<TestingTextClassificationModel> model(
-      new TestingTextClassificationModel(fd));
-  close(fd);
-
-  EXPECT_EQ("phone", FindBestResult(model->ClassifyText("phone: (123) 456 789",
-                                                        {7, 20}, 0)));
-  EXPECT_EQ("phone", FindBestResult(model->ClassifyText(
-                         "phone: (123) 456 789,0001112", {7, 25}, 0)));
-  EXPECT_EQ("other", FindBestResult(model->ClassifyText(
-                         "phone: (123) 456 789,0001112", {7, 28}, 0)));
-}
-
-TEST(TextClassificationModelTest, Annotate) {
-  const std::string model_path = GetModelPath();
-  int fd = open(model_path.c_str(), O_RDONLY);
-  std::unique_ptr<TestingTextClassificationModel> model(
-      new TestingTextClassificationModel(fd));
-  close(fd);
-
-  std::string test_string =
-      "& saw Barak Obama today .. 350 Third Street, Cambridge\nand my phone "
-      "number is 853 225-3556.";
-  std::vector<TextClassificationModel::AnnotatedSpan> result =
-      model->Annotate(test_string);
-
-  std::vector<TextClassificationModel::AnnotatedSpan> expected;
-  expected.emplace_back();
-  expected.back().span = {0, 0};
-  expected.emplace_back();
-  expected.back().span = {2, 5};
-  expected.back().classification.push_back({"other", 1.0});
-  expected.emplace_back();
-  expected.back().span = {6, 17};
-  expected.back().classification.push_back({"other", 1.0});
-  expected.emplace_back();
-  expected.back().span = {18, 23};
-  expected.back().classification.push_back({"other", 1.0});
-  expected.emplace_back();
-  expected.back().span = {24, 24};
-  expected.emplace_back();
-  expected.back().span = {27, 54};
-  expected.back().classification.push_back({"address", 1.0});
-  expected.emplace_back();
-  expected.back().span = {55, 58};
-  expected.back().classification.push_back({"other", 1.0});
-  expected.emplace_back();
-  expected.back().span = {59, 61};
-  expected.back().classification.push_back({"other", 1.0});
-  expected.emplace_back();
-  expected.back().span = {62, 74};
-  expected.back().classification.push_back({"other", 1.0});
-  expected.emplace_back();
-  expected.back().span = {75, 77};
-  expected.back().classification.push_back({"other", 1.0});
-  expected.emplace_back();
-  expected.back().span = {78, 90};
-  expected.back().classification.push_back({"phone", 1.0});
-
-  EXPECT_EQ(result.size(), expected.size());
-  for (int i = 0; i < expected.size(); ++i) {
-    EXPECT_EQ(result[i].span, expected[i].span) << result[i];
-    if (!expected[i].classification.empty()) {
-      EXPECT_GT(result[i].classification.size(), 0);
-      EXPECT_EQ(result[i].classification[0].first,
-                expected[i].classification[0].first)
-          << result[i];
-    }
-  }
-}
-
-TEST(TextClassificationModelTest, URLEmailRegex) {
-  const std::string model_path = GetModelPath();
-  int fd = open(model_path.c_str(), O_RDONLY);
-  std::unique_ptr<TestingTextClassificationModel> model(
-      new TestingTextClassificationModel(fd));
-  close(fd);
-
-  SharingModelOptions options;
-  SharingModelOptions::RegexPattern* email_pattern =
-      options.add_regex_pattern();
-  email_pattern->set_collection_name("email");
-  email_pattern->set_pattern(ReadFile(GetEmailRegexPath()));
-  SharingModelOptions::RegexPattern* url_pattern = options.add_regex_pattern();
-  url_pattern->set_collection_name("url");
-  url_pattern->set_pattern(ReadFile(GetURLRegexPath()));
-
-  // TODO(b/69538802): Modify directly the model image instead.
-  model->InitializeSharingRegexPatterns(
-      {options.regex_pattern().begin(), options.regex_pattern().end()});
-
-  EXPECT_EQ("url", FindBestResult(model->ClassifyText(
-                       "Visit www.google.com every today!", {6, 20})));
-  EXPECT_EQ("email", FindBestResult(model->ClassifyText(
-                         "My email: asdf@something.cz", {10, 27})));
-  EXPECT_EQ("url", FindBestResult(model->ClassifyText(
-                       "Login: http://asdf@something.cz", {7, 31})));
-}
-
-}  // namespace
-}  // namespace libtextclassifier
diff --git a/smartselect/tokenizer.cc b/smartselect/tokenizer.cc
deleted file mode 100644
index 2489a61..0000000
--- a/smartselect/tokenizer.cc
+++ /dev/null
@@ -1,95 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/tokenizer.h"
-
-#include <algorithm>
-
-#include "util/strings/utf8.h"
-#include "util/utf8/unicodetext.h"
-
-namespace libtextclassifier {
-
-Tokenizer::Tokenizer(
-    const std::vector<TokenizationCodepointRange>& codepoint_ranges)
-    : codepoint_ranges_(codepoint_ranges) {
-  std::sort(codepoint_ranges_.begin(), codepoint_ranges_.end(),
-            [](const TokenizationCodepointRange& a,
-               const TokenizationCodepointRange& b) {
-              return a.start() < b.start();
-            });
-}
-
-TokenizationCodepointRange::Role Tokenizer::FindTokenizationRole(
-    int codepoint) const {
-  auto it = std::lower_bound(
-      codepoint_ranges_.begin(), codepoint_ranges_.end(), codepoint,
-      [](const TokenizationCodepointRange& range, int codepoint) {
-        // This function compares range with the codepoint for the purpose of
-        // finding the first greater or equal range. Because of the use of
-        // std::lower_bound it needs to return true when range < codepoint;
-        // the first time it will return false the lower bound is found and
-        // returned.
-        //
-        // It might seem weird that the condition is range.end <= codepoint
-        // here but when codepoint == range.end it means it's actually just
-        // outside of the range, thus the range is less than the codepoint.
-        return range.end() <= codepoint;
-      });
-  if (it != codepoint_ranges_.end() && it->start() <= codepoint &&
-      it->end() > codepoint) {
-    return it->role();
-  } else {
-    return TokenizationCodepointRange::DEFAULT_ROLE;
-  }
-}
-
-std::vector<Token> Tokenizer::Tokenize(const std::string& utf8_text) const {
-  UnicodeText context_unicode = UTF8ToUnicodeText(utf8_text, /*do_copy=*/false);
-
-  std::vector<Token> result;
-  Token new_token("", 0, 0);
-  int codepoint_index = 0;
-  for (auto it = context_unicode.begin(); it != context_unicode.end();
-       ++it, ++codepoint_index) {
-    TokenizationCodepointRange::Role role = FindTokenizationRole(*it);
-    if (role & TokenizationCodepointRange::SPLIT_BEFORE) {
-      if (!new_token.value.empty()) {
-        result.push_back(new_token);
-      }
-      new_token = Token("", codepoint_index, codepoint_index);
-    }
-    if (!(role & TokenizationCodepointRange::DISCARD_CODEPOINT)) {
-      new_token.value += std::string(
-          it.utf8_data(),
-          it.utf8_data() + GetNumBytesForNonZeroUTF8Char(it.utf8_data()));
-      ++new_token.end;
-    }
-    if (role & TokenizationCodepointRange::SPLIT_AFTER) {
-      if (!new_token.value.empty()) {
-        result.push_back(new_token);
-      }
-      new_token = Token("", codepoint_index + 1, codepoint_index + 1);
-    }
-  }
-  if (!new_token.value.empty()) {
-    result.push_back(new_token);
-  }
-
-  return result;
-}
-
-}  // namespace libtextclassifier
diff --git a/smartselect/tokenizer.h b/smartselect/tokenizer.h
deleted file mode 100644
index 4eb78f9..0000000
--- a/smartselect/tokenizer.h
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_TOKENIZER_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_TOKENIZER_H_
-
-#include <string>
-#include <vector>
-
-#include "smartselect/tokenizer.pb.h"
-#include "smartselect/types.h"
-
-namespace libtextclassifier {
-
-// Tokenizer splits the input string into a sequence of tokens, according to the
-// configuration.
-class Tokenizer {
- public:
-  explicit Tokenizer(
-      const std::vector<TokenizationCodepointRange>& codepoint_ranges);
-
-  // Tokenizes the input string using the selected tokenization method.
-  std::vector<Token> Tokenize(const std::string& utf8_text) const;
-
- protected:
-  // Finds the tokenization role for given codepoint.
-  // If the character is not found returns DEFAULT_ROLE.
-  // Internally uses binary search so should be O(log(# of codepoint_ranges)).
-  TokenizationCodepointRange::Role FindTokenizationRole(int codepoint) const;
-
- private:
-  // Codepoint ranges that determine how different codepoints are tokenized.
-  // The ranges must not overlap.
-  std::vector<TokenizationCodepointRange> codepoint_ranges_;
-};
-
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_SMARTSELECT_TOKENIZER_H_
diff --git a/smartselect/tokenizer.proto b/smartselect/tokenizer.proto
deleted file mode 100644
index 8e78970..0000000
--- a/smartselect/tokenizer.proto
+++ /dev/null
@@ -1,48 +0,0 @@
-// Copyright (C) 2017 The Android Open Source Project
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-option optimize_for = LITE_RUNTIME;
-
-package libtextclassifier;
-
-// Represents a codepoint range [start, end) with its role for tokenization.
-message TokenizationCodepointRange {
-  optional int32 start = 1;
-  optional int32 end = 2;
-
-  // Role of the codepoints in the range.
-  enum Role {
-    // Concatenates the codepoint to the current run of codepoints.
-    DEFAULT_ROLE = 0;
-
-    // Splits a run of codepoints before the current codepoint.
-    SPLIT_BEFORE = 0x1;
-
-    // Splits a run of codepoints after the current codepoint.
-    SPLIT_AFTER = 0x2;
-
-    // Discards the codepoint.
-    DISCARD_CODEPOINT = 0x4;
-
-    // Common values:
-    // Splits on the characters and discards them. Good e.g. for the space
-    // character.
-    WHITESPACE_SEPARATOR = 0x7;
-    // Each codepoint will be a separate token. Good e.g. for Chinese
-    // characters.
-    TOKEN_SEPARATOR = 0x3;
-  }
-  optional Role role = 3;
-}
diff --git a/smartselect/tokenizer_test.cc b/smartselect/tokenizer_test.cc
deleted file mode 100644
index cdb90a9..0000000
--- a/smartselect/tokenizer_test.cc
+++ /dev/null
@@ -1,261 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "smartselect/tokenizer.h"
-
-#include "gmock/gmock.h"
-#include "gtest/gtest.h"
-
-namespace libtextclassifier {
-namespace {
-
-using testing::ElementsAreArray;
-
-class TestingTokenizer : public Tokenizer {
- public:
-  explicit TestingTokenizer(
-      const std::vector<TokenizationCodepointRange>& codepoint_range_configs)
-      : Tokenizer(codepoint_range_configs) {}
-
-  TokenizationCodepointRange::Role TestFindTokenizationRole(int c) const {
-    return FindTokenizationRole(c);
-  }
-};
-
-TEST(TokenizerTest, FindTokenizationRole) {
-  std::vector<TokenizationCodepointRange> configs;
-  TokenizationCodepointRange* config;
-
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0);
-  config->set_end(10);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(32);
-  config->set_end(33);
-  config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
-
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(1234);
-  config->set_end(12345);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-
-  TestingTokenizer tokenizer(configs);
-
-  // Test hits to the first group.
-  EXPECT_EQ(tokenizer.TestFindTokenizationRole(0),
-            TokenizationCodepointRange::TOKEN_SEPARATOR);
-  EXPECT_EQ(tokenizer.TestFindTokenizationRole(5),
-            TokenizationCodepointRange::TOKEN_SEPARATOR);
-  EXPECT_EQ(tokenizer.TestFindTokenizationRole(10),
-            TokenizationCodepointRange::DEFAULT_ROLE);
-
-  // Test a hit to the second group.
-  EXPECT_EQ(tokenizer.TestFindTokenizationRole(31),
-            TokenizationCodepointRange::DEFAULT_ROLE);
-  EXPECT_EQ(tokenizer.TestFindTokenizationRole(32),
-            TokenizationCodepointRange::WHITESPACE_SEPARATOR);
-  EXPECT_EQ(tokenizer.TestFindTokenizationRole(33),
-            TokenizationCodepointRange::DEFAULT_ROLE);
-
-  // Test hits to the third group.
-  EXPECT_EQ(tokenizer.TestFindTokenizationRole(1233),
-            TokenizationCodepointRange::DEFAULT_ROLE);
-  EXPECT_EQ(tokenizer.TestFindTokenizationRole(1234),
-            TokenizationCodepointRange::TOKEN_SEPARATOR);
-  EXPECT_EQ(tokenizer.TestFindTokenizationRole(12344),
-            TokenizationCodepointRange::TOKEN_SEPARATOR);
-  EXPECT_EQ(tokenizer.TestFindTokenizationRole(12345),
-            TokenizationCodepointRange::DEFAULT_ROLE);
-
-  // Test a hit outside.
-  EXPECT_EQ(tokenizer.TestFindTokenizationRole(99),
-            TokenizationCodepointRange::DEFAULT_ROLE);
-}
-
-TEST(TokenizerTest, TokenizeOnSpace) {
-  std::vector<TokenizationCodepointRange> configs;
-  TokenizationCodepointRange* config;
-
-  configs.emplace_back();
-  config = &configs.back();
-  // Space character.
-  config->set_start(32);
-  config->set_end(33);
-  config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
-
-  TestingTokenizer tokenizer(configs);
-  std::vector<Token> tokens = tokenizer.Tokenize("Hello world!");
-
-  EXPECT_THAT(tokens,
-              ElementsAreArray({Token("Hello", 0, 5), Token("world!", 6, 12)}));
-}
-
-TEST(TokenizerTest, TokenizeComplex) {
-  std::vector<TokenizationCodepointRange> configs;
-  TokenizationCodepointRange* config;
-
-  // Source: http://www.unicode.org/Public/10.0.0/ucd/Blocks-10.0.0d1.txt
-  // Latin - cyrilic.
-  //   0000..007F; Basic Latin
-  //   0080..00FF; Latin-1 Supplement
-  //   0100..017F; Latin Extended-A
-  //   0180..024F; Latin Extended-B
-  //   0250..02AF; IPA Extensions
-  //   02B0..02FF; Spacing Modifier Letters
-  //   0300..036F; Combining Diacritical Marks
-  //   0370..03FF; Greek and Coptic
-  //   0400..04FF; Cyrillic
-  //   0500..052F; Cyrillic Supplement
-  //   0530..058F; Armenian
-  //   0590..05FF; Hebrew
-  //   0600..06FF; Arabic
-  //   0700..074F; Syriac
-  //   0750..077F; Arabic Supplement
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0);
-  config->set_end(32);
-  config->set_role(TokenizationCodepointRange::DEFAULT_ROLE);
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(32);
-  config->set_end(33);
-  config->set_role(TokenizationCodepointRange::WHITESPACE_SEPARATOR);
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(33);
-  config->set_end(0x77F + 1);
-  config->set_role(TokenizationCodepointRange::DEFAULT_ROLE);
-
-  // CJK
-  // 2E80..2EFF; CJK Radicals Supplement
-  // 3000..303F; CJK Symbols and Punctuation
-  // 3040..309F; Hiragana
-  // 30A0..30FF; Katakana
-  // 3100..312F; Bopomofo
-  // 3130..318F; Hangul Compatibility Jamo
-  // 3190..319F; Kanbun
-  // 31A0..31BF; Bopomofo Extended
-  // 31C0..31EF; CJK Strokes
-  // 31F0..31FF; Katakana Phonetic Extensions
-  // 3200..32FF; Enclosed CJK Letters and Months
-  // 3300..33FF; CJK Compatibility
-  // 3400..4DBF; CJK Unified Ideographs Extension A
-  // 4DC0..4DFF; Yijing Hexagram Symbols
-  // 4E00..9FFF; CJK Unified Ideographs
-  // A000..A48F; Yi Syllables
-  // A490..A4CF; Yi Radicals
-  // A4D0..A4FF; Lisu
-  // A500..A63F; Vai
-  // F900..FAFF; CJK Compatibility Ideographs
-  // FE30..FE4F; CJK Compatibility Forms
-  // 20000..2A6DF; CJK Unified Ideographs Extension B
-  // 2A700..2B73F; CJK Unified Ideographs Extension C
-  // 2B740..2B81F; CJK Unified Ideographs Extension D
-  // 2B820..2CEAF; CJK Unified Ideographs Extension E
-  // 2CEB0..2EBEF; CJK Unified Ideographs Extension F
-  // 2F800..2FA1F; CJK Compatibility Ideographs Supplement
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0x2E80);
-  config->set_end(0x2EFF + 1);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0x3000);
-  config->set_end(0xA63F + 1);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0xF900);
-  config->set_end(0xFAFF + 1);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0xFE30);
-  config->set_end(0xFE4F + 1);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0x20000);
-  config->set_end(0x2A6DF + 1);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0x2A700);
-  config->set_end(0x2B73F + 1);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0x2B740);
-  config->set_end(0x2B81F + 1);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0x2B820);
-  config->set_end(0x2CEAF + 1);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0x2CEB0);
-  config->set_end(0x2EBEF + 1);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0x2F800);
-  config->set_end(0x2FA1F + 1);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-
-  // Thai.
-  // 0E00..0E7F; Thai
-  configs.emplace_back();
-  config = &configs.back();
-  config->set_start(0x0E00);
-  config->set_end(0x0E7F + 1);
-  config->set_role(TokenizationCodepointRange::TOKEN_SEPARATOR);
-
-  Tokenizer tokenizer(configs);
-  std::vector<Token> tokens;
-
-  tokens = tokenizer.Tokenize(
-      "問少目木輸走猶術権自京門録球変。細開括省用掲情結傍走愛明氷。");
-  EXPECT_EQ(tokens.size(), 30);
-
-  tokens = tokenizer.Tokenize("問少目 hello 木輸ยามきゃ");
-  // clang-format off
-  EXPECT_THAT(
-      tokens,
-      ElementsAreArray({Token("問", 0, 1),
-                        Token("少", 1, 2),
-                        Token("目", 2, 3),
-                        Token("hello", 4, 9),
-                        Token("木", 10, 11),
-                        Token("輸", 11, 12),
-                        Token("ย", 12, 13),
-                        Token("า", 13, 14),
-                        Token("ม", 14, 15),
-                        Token("き", 15, 16),
-                        Token("ゃ", 16, 17)}));
-  // clang-format on
-}
-
-}  // namespace
-}  // namespace libtextclassifier
diff --git a/smartselect/types.h b/smartselect/types.h
deleted file mode 100644
index 443e3ac..0000000
--- a/smartselect/types.h
+++ /dev/null
@@ -1,80 +0,0 @@
-/*
- * Copyright (C) 2017 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_TYPES_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_TYPES_H_
-
-#include <ostream>
-#include <string>
-#include <utility>
-
-namespace libtextclassifier {
-
-constexpr int kInvalidIndex = -1;
-
-// Index for a 0-based array of tokens.
-using TokenIndex = int;
-
-// Index for a 0-based array of codepoints.
-using CodepointIndex = int;
-
-// Marks a span in a sequence of codepoints. The first element is the index of
-// the first codepoint of the span, and the second element is the index of the
-// codepoint one past the end of the span.
-using CodepointSpan = std::pair<CodepointIndex, CodepointIndex>;
-
-// Marks a span in a sequence of tokens. The first element is the index of the
-// first token in the span, and the second element is the index of the token one
-// past the end of the span.
-using TokenSpan = std::pair<TokenIndex, TokenIndex>;
-
-// Token holds a token, its position in the original string and whether it was
-// part of the input span.
-struct Token {
-  std::string value;
-  CodepointIndex start;
-  CodepointIndex end;
-
-  // Whether the token is a padding token.
-  bool is_padding;
-
-  // Default constructor constructs the padding-token.
-  Token()
-      : value(""), start(kInvalidIndex), end(kInvalidIndex), is_padding(true) {}
-
-  Token(const std::string& arg_value, CodepointIndex arg_start,
-        CodepointIndex arg_end)
-      : value(arg_value), start(arg_start), end(arg_end), is_padding(false) {}
-
-  bool operator==(const Token& other) const {
-    return value == other.value && start == other.start && end == other.end &&
-           is_padding == other.is_padding;
-  }
-
-  bool IsContainedInSpan(CodepointSpan span) const {
-    return start >= span.first && end <= span.second;
-  }
-};
-
-// Pretty-printing function for Token.
-inline std::ostream& operator<<(std::ostream& os, const Token& token) {
-  return os << "Token(\"" << token.value << "\", " << token.start << ", "
-            << token.end << ", is_padding=" << token.is_padding << ")";
-}
-
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_SMARTSELECT_TYPES_H_
diff --git a/strip-unpaired-brackets.cc b/strip-unpaired-brackets.cc
new file mode 100644
index 0000000..f813e6b
--- /dev/null
+++ b/strip-unpaired-brackets.cc
@@ -0,0 +1,105 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "strip-unpaired-brackets.h"
+
+#include <iterator>
+
+#include "util/base/logging.h"
+#include "util/utf8/unicodetext.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+// Returns true if given codepoint is contained in the given span in context.
+bool IsCodepointInSpan(const char32 codepoint, const std::string& context,
+                       const CodepointSpan span) {
+  const UnicodeText context_unicode =
+      UTF8ToUnicodeText(context, /*do_copy=*/false);
+
+  auto begin_it = context_unicode.begin();
+  std::advance(begin_it, span.first);
+  auto end_it = context_unicode.begin();
+  std::advance(end_it, span.second);
+
+  return std::find(begin_it, end_it, codepoint) != end_it;
+}
+
+// Returns the first codepoint of the span.
+char32 FirstSpanCodepoint(const std::string& context,
+                          const CodepointSpan span) {
+  const UnicodeText context_unicode =
+      UTF8ToUnicodeText(context, /*do_copy=*/false);
+
+  auto it = context_unicode.begin();
+  std::advance(it, span.first);
+  return *it;
+}
+
+// Returns the last codepoint of the span.
+char32 LastSpanCodepoint(const std::string& context, const CodepointSpan span) {
+  const UnicodeText context_unicode =
+      UTF8ToUnicodeText(context, /*do_copy=*/false);
+
+  auto it = context_unicode.begin();
+  std::advance(it, span.second - 1);
+  return *it;
+}
+
+}  // namespace
+
+// If the first or the last codepoint of the given span is a bracket, the
+// bracket is stripped if the span does not contain its corresponding paired
+// version.
+CodepointSpan StripUnpairedBrackets(const std::string& context,
+                                    CodepointSpan span, const UniLib& unilib) {
+  if (context.empty()) {
+    return span;
+  }
+
+  const char32 begin_char = FirstSpanCodepoint(context, span);
+  const char32 paired_begin_char = unilib.GetPairedBracket(begin_char);
+  if (paired_begin_char != begin_char) {
+    if (!unilib.IsOpeningBracket(begin_char) ||
+        !IsCodepointInSpan(paired_begin_char, context, span)) {
+      ++span.first;
+    }
+  }
+
+  if (span.first == span.second) {
+    return span;
+  }
+
+  const char32 end_char = LastSpanCodepoint(context, span);
+  const char32 paired_end_char = unilib.GetPairedBracket(end_char);
+  if (paired_end_char != end_char) {
+    if (!unilib.IsClosingBracket(end_char) ||
+        !IsCodepointInSpan(paired_end_char, context, span)) {
+      --span.second;
+    }
+  }
+
+  // Should not happen, but let's make sure.
+  if (span.first > span.second) {
+    TC_LOG(WARNING) << "Inverse indices result: " << span.first << ", "
+                    << span.second;
+    span.second = span.first;
+  }
+
+  return span;
+}
+
+}  // namespace libtextclassifier2
diff --git a/strip-unpaired-brackets.h b/strip-unpaired-brackets.h
new file mode 100644
index 0000000..2d7893e
--- /dev/null
+++ b/strip-unpaired-brackets.h
@@ -0,0 +1,33 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_STRIP_UNPAIRED_BRACKETS_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_STRIP_UNPAIRED_BRACKETS_H_
+
+#include <string>
+
+#include "types.h"
+#include "util/utf8/unilib.h"
+
+namespace libtextclassifier2 {
+// If the first or the last codepoint of the given span is a bracket, the
+// bracket is stripped if the span does not contain its corresponding paired
+// version.
+CodepointSpan StripUnpairedBrackets(const std::string& context,
+                                    CodepointSpan span, const UniLib& unilib);
+}  // namespace libtextclassifier2
+
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_STRIP_UNPAIRED_BRACKETS_H_
diff --git a/strip-unpaired-brackets_test.cc b/strip-unpaired-brackets_test.cc
new file mode 100644
index 0000000..fb99d82
--- /dev/null
+++ b/strip-unpaired-brackets_test.cc
@@ -0,0 +1,51 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "strip-unpaired-brackets.h"
+
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+TEST(StripUnpairedBracketsTest, StripUnpairedBrackets) {
+  UniLib unilib;
+  // If the brackets match, nothing gets stripped.
+  EXPECT_EQ(StripUnpairedBrackets("call me (123) 456 today", {8, 17}, unilib),
+            std::make_pair(8, 17));
+  EXPECT_EQ(StripUnpairedBrackets("call me (123 456) today", {8, 17}, unilib),
+            std::make_pair(8, 17));
+
+  // If the brackets don't match, they get stripped.
+  EXPECT_EQ(StripUnpairedBrackets("call me (123 456 today", {8, 16}, unilib),
+            std::make_pair(9, 16));
+  EXPECT_EQ(StripUnpairedBrackets("call me )123 456 today", {8, 16}, unilib),
+            std::make_pair(9, 16));
+  EXPECT_EQ(StripUnpairedBrackets("call me 123 456) today", {8, 16}, unilib),
+            std::make_pair(8, 15));
+  EXPECT_EQ(StripUnpairedBrackets("call me 123 456( today", {8, 16}, unilib),
+            std::make_pair(8, 15));
+
+  // Strips brackets correctly from length-1 selections that consist of
+  // a bracket only.
+  EXPECT_EQ(StripUnpairedBrackets("call me at ) today", {11, 12}, unilib),
+            std::make_pair(12, 12));
+  EXPECT_EQ(StripUnpairedBrackets("call me at ( today", {11, 12}, unilib),
+            std::make_pair(12, 12));
+}
+
+}  // namespace
+}  // namespace libtextclassifier2
diff --git a/common/config.h b/tensor-view.cc
similarity index 67%
rename from common/config.h
rename to tensor-view.cc
index b883e95..4acadc5 100644
--- a/common/config.h
+++ b/tensor-view.cc
@@ -14,16 +14,18 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
-#define LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
+#include "tensor-view.h"
 
-#ifndef PORTABLE_SAFT_MOBILE
-#if defined(__ANDROID__) || defined(__APPLE__)
-#define PORTABLE_SAFT_MOBILE 1
-#else
-#define PORTABLE_SAFT_MOBILE 0
-#endif
+namespace libtextclassifier2 {
 
-#endif
+namespace internal {
+int NumberOfElements(const std::vector<int>& shape) {
+  int size = 1;
+  for (const int dim : shape) {
+    size *= dim;
+  }
+  return size;
+}
+}  // namespace internal
 
-#endif  // LIBTEXTCLASSIFIER_COMMON_CONFIG_H_
+}  // namespace libtextclassifier2
diff --git a/tensor-view.h b/tensor-view.h
new file mode 100644
index 0000000..69788c8
--- /dev/null
+++ b/tensor-view.h
@@ -0,0 +1,72 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TENSOR_VIEW_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TENSOR_VIEW_H_
+
+#include <algorithm>
+#include <vector>
+
+namespace libtextclassifier2 {
+namespace internal {
+// Computes the number of elements in a tensor of given shape.
+int NumberOfElements(const std::vector<int>& shape);
+}  // namespace internal
+
+// View of a tensor of given type.
+// NOTE: Does not own the underlying memory, so the contract about its validity
+// needs to be specified on the interface that returns it.
+template <typename T>
+class TensorView {
+ public:
+  TensorView(const T* data, const std::vector<int>& shape)
+      : data_(data), shape_(shape), size_(internal::NumberOfElements(shape)) {}
+
+  static TensorView Invalid() {
+    static std::vector<int>& invalid_shape =
+        *[]() { return new std::vector<int>(0); }();
+    return TensorView(nullptr, invalid_shape);
+  }
+
+  bool is_valid() const { return data_ != nullptr; }
+
+  const std::vector<int>& shape() const { return shape_; }
+
+  int dim(int i) const { return shape_[i]; }
+
+  int dims() const { return shape_.size(); }
+
+  const T* data() const { return data_; }
+
+  int size() const { return size_; }
+
+  bool copy_to(T* dest, int dest_size) const {
+    if (dest_size < size_) {
+      return false;
+    }
+    std::copy(data_, data_ + size_, dest);
+    return true;
+  }
+
+ private:
+  const T* data_ = nullptr;
+  const std::vector<int> shape_;
+  const int size_;
+};
+
+}  // namespace libtextclassifier2
+
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TENSOR_VIEW_H_
diff --git a/tensor-view_test.cc b/tensor-view_test.cc
new file mode 100644
index 0000000..d50fac7
--- /dev/null
+++ b/tensor-view_test.cc
@@ -0,0 +1,52 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "tensor-view.h"
+
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+TEST(TensorViewTest, TestSize) {
+  std::vector<float> data{0.1, 0.2, 0.3, 0.4, 0.5, 0.6};
+  const TensorView<float> tensor(data.data(), {3, 1, 2});
+  EXPECT_TRUE(tensor.is_valid());
+  EXPECT_EQ(tensor.shape(), (std::vector<int>{3, 1, 2}));
+  EXPECT_EQ(tensor.data(), data.data());
+  EXPECT_EQ(tensor.size(), 6);
+  EXPECT_EQ(tensor.dims(), 3);
+  EXPECT_EQ(tensor.dim(0), 3);
+  EXPECT_EQ(tensor.dim(1), 1);
+  EXPECT_EQ(tensor.dim(2), 2);
+  std::vector<float> output_data(6);
+  EXPECT_TRUE(tensor.copy_to(output_data.data(), output_data.size()));
+  EXPECT_EQ(data, output_data);
+
+  // Should not copy when the output is small.
+  std::vector<float> small_output_data{-1, -1, -1};
+  EXPECT_FALSE(
+      tensor.copy_to(small_output_data.data(), small_output_data.size()));
+  // The output buffer should not be changed.
+  EXPECT_EQ(small_output_data, (std::vector<float>{-1, -1, -1}));
+
+  const TensorView<float> invalid_tensor = TensorView<float>::Invalid();
+  EXPECT_FALSE(invalid_tensor.is_valid());
+}
+
+}  // namespace
+}  // namespace libtextclassifier2
diff --git a/test_data/dummy.fb b/test_data/dummy.fb
new file mode 100644
index 0000000..4fec970
--- /dev/null
+++ b/test_data/dummy.fb
Binary files differ
diff --git a/test_data/test_model.fb b/test_data/test_model.fb
new file mode 100644
index 0000000..f62d9fe
--- /dev/null
+++ b/test_data/test_model.fb
Binary files differ
diff --git a/test_data/wrong_embeddings.fb b/test_data/wrong_embeddings.fb
new file mode 100644
index 0000000..513fcf5
--- /dev/null
+++ b/test_data/wrong_embeddings.fb
Binary files differ
diff --git a/tests/testdata/langid.model b/tests/testdata/langid.model
deleted file mode 100644
index 6b68223..0000000
--- a/tests/testdata/langid.model
+++ /dev/null
Binary files differ
diff --git a/tests/testdata/smartselection.model b/tests/testdata/smartselection.model
deleted file mode 100644
index 645303d..0000000
--- a/tests/testdata/smartselection.model
+++ /dev/null
Binary files differ
diff --git a/text-classifier.cc b/text-classifier.cc
new file mode 100644
index 0000000..1ee7e56
--- /dev/null
+++ b/text-classifier.cc
@@ -0,0 +1,592 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "text-classifier.h"
+
+#include <algorithm>
+#include <cctype>
+#include <cmath>
+#include <iterator>
+#include <numeric>
+
+#include "util/base/logging.h"
+#include "util/math/softmax.h"
+#include "util/utf8/unicodetext.h"
+
+namespace libtextclassifier2 {
+namespace {
+const Model* LoadAndVerifyModel(const void* addr, int size) {
+  const Model* model = flatbuffers::GetRoot<Model>(addr);
+
+  flatbuffers::Verifier verifier(reinterpret_cast<const uint8_t*>(addr), size);
+  if (model->Verify(verifier)) {
+    return model;
+  } else {
+    return nullptr;
+  }
+}
+}  // namespace
+
+std::unique_ptr<TextClassifier> TextClassifier::FromUnownedBuffer(
+    const char* buffer, int size) {
+  const Model* model = LoadAndVerifyModel(buffer, size);
+  if (model == nullptr) {
+    return nullptr;
+  }
+
+  auto classifier = std::unique_ptr<TextClassifier>(new TextClassifier(model));
+  if (!classifier->IsInitialized()) {
+    return nullptr;
+  }
+
+  return classifier;
+}
+
+std::unique_ptr<TextClassifier> TextClassifier::FromScopedMmap(
+    std::unique_ptr<ScopedMmap>* mmap) {
+  if (!(*mmap)->handle().ok()) {
+    TC_VLOG(1) << "Mmap failed.";
+    return nullptr;
+  }
+
+  const Model* model = LoadAndVerifyModel((*mmap)->handle().start(),
+                                          (*mmap)->handle().num_bytes());
+  if (!model) {
+    TC_LOG(ERROR) << "Model verification failed.";
+    return nullptr;
+  }
+
+  auto classifier =
+      std::unique_ptr<TextClassifier>(new TextClassifier(mmap, model));
+  if (!classifier->IsInitialized()) {
+    return nullptr;
+  }
+
+  return classifier;
+}
+
+std::unique_ptr<TextClassifier> TextClassifier::FromFileDescriptor(int fd,
+                                                                   int offset,
+                                                                   int size) {
+  std::unique_ptr<ScopedMmap> mmap(new ScopedMmap(fd, offset, size));
+  return FromScopedMmap(&mmap);
+}
+
+std::unique_ptr<TextClassifier> TextClassifier::FromFileDescriptor(int fd) {
+  std::unique_ptr<ScopedMmap> mmap(new ScopedMmap(fd));
+  return FromScopedMmap(&mmap);
+}
+
+std::unique_ptr<TextClassifier> TextClassifier::FromPath(
+    const std::string& path) {
+  std::unique_ptr<ScopedMmap> mmap(new ScopedMmap(path));
+  return FromScopedMmap(&mmap);
+}
+
+void TextClassifier::ValidateAndInitialize() {
+  if (model_ == nullptr) {
+    TC_LOG(ERROR) << "No model specified.";
+    initialized_ = false;
+    return;
+  }
+
+  if (!model_->selection_options()) {
+    TC_LOG(ERROR) << "No selection options.";
+    initialized_ = false;
+    return;
+  }
+
+  if (!model_->classification_options()) {
+    TC_LOG(ERROR) << "No classification options.";
+    initialized_ = false;
+    return;
+  }
+
+  if (!model_->selection_feature_options()) {
+    TC_LOG(ERROR) << "No selection feature options.";
+    initialized_ = false;
+    return;
+  }
+
+  if (!model_->classification_feature_options()) {
+    TC_LOG(ERROR) << "No classification feature options.";
+    initialized_ = false;
+    return;
+  }
+
+  if (!model_->classification_feature_options()->bounds_sensitive_features()) {
+    TC_LOG(ERROR) << "No classification bounds sensitive feature options.";
+    initialized_ = false;
+    return;
+  }
+
+  if (!model_->selection_feature_options()->bounds_sensitive_features()) {
+    TC_LOG(ERROR) << "No selection bounds sensitive feature options.";
+    initialized_ = false;
+    return;
+  }
+
+  if (!model_->selection_model()) {
+    TC_LOG(ERROR) << "No selection model.";
+    initialized_ = false;
+    return;
+  }
+
+  if (!model_->embedding_model()) {
+    TC_LOG(ERROR) << "No embedding model.";
+    initialized_ = false;
+    return;
+  }
+
+  if (!model_->classification_model()) {
+    TC_LOG(ERROR) << "No clf model.";
+    initialized_ = false;
+    return;
+  }
+
+  if (model_->regex_options()) {
+    InitializeRegexModel();
+  }
+
+  embedding_executor_.reset(new TFLiteEmbeddingExecutor(
+      flatbuffers::GetRoot<tflite::Model>(model_->embedding_model()->data())));
+  if (!embedding_executor_ || !embedding_executor_->IsReady()) {
+    TC_LOG(ERROR) << "Could not initialize embedding executor.";
+    initialized_ = false;
+    return;
+  }
+  selection_executor_.reset(new ModelExecutor(
+      flatbuffers::GetRoot<tflite::Model>(model_->selection_model()->data())));
+  if (!selection_executor_) {
+    TC_LOG(ERROR) << "Could not initialize selection executor.";
+    initialized_ = false;
+    return;
+  }
+  classification_executor_.reset(
+      new ModelExecutor(flatbuffers::GetRoot<tflite::Model>(
+          model_->classification_model()->data())));
+  if (!classification_executor_) {
+    TC_LOG(ERROR) << "Could not initialize classification executor.";
+    initialized_ = false;
+    return;
+  }
+
+  selection_feature_processor_.reset(
+      new FeatureProcessor(model_->selection_feature_options(), unilib_.get()));
+  classification_feature_processor_.reset(new FeatureProcessor(
+      model_->classification_feature_options(), unilib_.get()));
+
+  initialized_ = true;
+}
+
+void TextClassifier::InitializeRegexModel() {
+  if (!model_->regex_options()->patterns()) {
+    initialized_ = false;
+    TC_LOG(ERROR) << "No patterns in the regex config.";
+    return;
+  }
+
+  // Initialize pattern recognizers.
+  for (const auto& regex_pattern : *model_->regex_options()->patterns()) {
+    std::unique_ptr<UniLib::RegexPattern> compiled_pattern(
+        unilib_->CreateRegexPattern(regex_pattern->pattern()->c_str()));
+
+    if (!compiled_pattern) {
+      TC_LOG(WARNING) << "Failed to load pattern"
+                      << regex_pattern->pattern()->str();
+      continue;
+    }
+
+    regex_patterns_.push_back(
+        {regex_pattern->collection_name()->str(), std::move(compiled_pattern)});
+  }
+}
+
+namespace {
+
+int CountDigits(const std::string& str, CodepointSpan selection_indices) {
+  int count = 0;
+  int i = 0;
+  const UnicodeText unicode_str = UTF8ToUnicodeText(str, /*do_copy=*/false);
+  for (auto it = unicode_str.begin(); it != unicode_str.end(); ++it, ++i) {
+    if (i >= selection_indices.first && i < selection_indices.second &&
+        isdigit(*it)) {
+      ++count;
+    }
+  }
+  return count;
+}
+
+std::string ExtractSelection(const std::string& context,
+                             CodepointSpan selection_indices) {
+  const UnicodeText context_unicode =
+      UTF8ToUnicodeText(context, /*do_copy=*/false);
+  auto selection_begin = context_unicode.begin();
+  std::advance(selection_begin, selection_indices.first);
+  auto selection_end = context_unicode.begin();
+  std::advance(selection_end, selection_indices.second);
+  return UnicodeText::UTF8Substring(selection_begin, selection_end);
+}
+
+}  // namespace
+
+CodepointSpan TextClassifier::SuggestSelection(
+    const std::string& context, CodepointSpan click_indices) const {
+  if (!initialized_) {
+    TC_LOG(ERROR) << "Not initialized";
+    return click_indices;
+  }
+
+  const int context_codepoint_size =
+      UTF8ToUnicodeText(context, /*do_copy=*/false).size();
+
+  if (click_indices.first < 0 || click_indices.second < 0 ||
+      click_indices.first >= context_codepoint_size ||
+      click_indices.second > context_codepoint_size ||
+      click_indices.first >= click_indices.second) {
+    TC_VLOG(1) << "Trying to run SuggestSelection with invalid indices: "
+               << click_indices.first << " " << click_indices.second;
+    return click_indices;
+  }
+
+  std::vector<Token> tokens;
+  int click_pos;
+  selection_feature_processor_->TokenizeAndFindClick(context, click_indices,
+                                                     &tokens, &click_pos);
+  if (click_pos == kInvalidIndex) {
+    TC_VLOG(1) << "Could not calculate the click position.";
+    return click_indices;
+  }
+
+  const int symmetry_context_size =
+      model_->selection_options()->symmetry_context_size();
+  const int max_selection_span =
+      selection_feature_processor_->GetOptions()->max_selection_span();
+  const FeatureProcessorOptions_::BoundsSensitiveFeatures*
+      bounds_sensitive_features =
+          model_->selection_feature_options()->bounds_sensitive_features();
+
+  // The symmetry context span is the clicked token with symmetry_context_size
+  // tokens on either side.
+  const TokenSpan symmetry_context_span = IntersectTokenSpans(
+      ExpandTokenSpan(SingleTokenSpan(click_pos),
+                      /*num_tokens_left=*/symmetry_context_size,
+                      /*num_tokens_right=*/symmetry_context_size),
+      {0, tokens.size()});
+
+  // The extraction span is the symmetry context span expanded to include
+  // max_selection_span tokens on either side, which is how far a selection can
+  // stretch from the click, plus a relevant number of tokens outside of the
+  // bounds of the selection.
+  const TokenSpan extraction_span = IntersectTokenSpans(
+      ExpandTokenSpan(symmetry_context_span,
+                      /*num_tokens_left=*/max_selection_span +
+                          bounds_sensitive_features->num_tokens_before(),
+                      /*num_tokens_right=*/max_selection_span +
+                          bounds_sensitive_features->num_tokens_after()),
+      {0, tokens.size()});
+
+  std::unique_ptr<CachedFeatures> cached_features;
+  if (!classification_feature_processor_->ExtractFeatures(
+          tokens, extraction_span, embedding_executor_.get(),
+          classification_feature_processor_->EmbeddingSize() +
+              classification_feature_processor_->DenseFeaturesCount(),
+          &cached_features)) {
+    TC_LOG(ERROR) << "Could not extract features.";
+    return click_indices;
+  }
+
+  std::vector<TokenSpan> chunks;
+  if (!Chunk(tokens.size(), /*span_of_interest=*/symmetry_context_span,
+             *cached_features, &chunks)) {
+    TC_LOG(ERROR) << "Could not chunk.";
+    return click_indices;
+  }
+
+  CodepointSpan result = click_indices;
+  for (const TokenSpan& chunk : chunks) {
+    if (chunk.first <= click_pos && click_pos < chunk.second) {
+      result = selection_feature_processor_->StripBoundaryCodepoints(
+          context, TokenSpanToCodepointSpan(tokens, chunk));
+      break;
+    }
+  }
+
+  if (model_->selection_options()->strip_unpaired_brackets()) {
+    const CodepointSpan stripped_result =
+        StripUnpairedBrackets(context, result, *unilib_);
+    if (stripped_result.first != stripped_result.second) {
+      result = stripped_result;
+    }
+  }
+
+  return result;
+}
+
+std::vector<std::pair<std::string, float>> TextClassifier::ClassifyText(
+    const std::string& context, CodepointSpan selection_indices) const {
+  if (!initialized_) {
+    TC_LOG(ERROR) << "Not initialized";
+    return {};
+  }
+
+  if (std::get<0>(selection_indices) >= std::get<1>(selection_indices)) {
+    TC_VLOG(1) << "Trying to run ClassifyText with invalid indices: "
+               << std::get<0>(selection_indices) << " "
+               << std::get<1>(selection_indices);
+    return {};
+  }
+
+  // Check whether any of the regular expressions match.
+  const std::string selection_text =
+      ExtractSelection(context, selection_indices);
+  for (const CompiledRegexPattern& regex_pattern : regex_patterns_) {
+    if (regex_pattern.pattern->Matches(selection_text)) {
+      return {{regex_pattern.collection_name, 1.0}};
+    }
+  }
+
+  const FeatureProcessorOptions_::BoundsSensitiveFeatures*
+      bounds_sensitive_features =
+          model_->classification_feature_options()->bounds_sensitive_features();
+
+  std::vector<Token> tokens;
+  classification_feature_processor_->TokenizeAndFindClick(
+      context, selection_indices, &tokens, /*click_pos=*/nullptr);
+  const TokenSpan selection_token_span =
+      CodepointSpanToTokenSpan(tokens, selection_indices);
+
+  if (selection_token_span.first == kInvalidIndex ||
+      selection_token_span.second == kInvalidIndex) {
+    return {};
+  }
+
+  // The extraction span is the selection span expanded to include a relevant
+  // number of tokens outside of the bounds of the selection.
+  const TokenSpan extraction_span = IntersectTokenSpans(
+      ExpandTokenSpan(selection_token_span,
+                      bounds_sensitive_features->num_tokens_before(),
+                      bounds_sensitive_features->num_tokens_after()),
+      {0, tokens.size()});
+
+  std::unique_ptr<CachedFeatures> cached_features;
+  if (!classification_feature_processor_->ExtractFeatures(
+          tokens, extraction_span, embedding_executor_.get(),
+          classification_feature_processor_->EmbeddingSize() +
+              classification_feature_processor_->DenseFeaturesCount(),
+          &cached_features)) {
+    TC_LOG(ERROR) << "Could not extract features.";
+    return {};
+  }
+
+  const std::vector<float> features =
+      cached_features->Get(selection_token_span);
+
+  TensorView<float> logits =
+      classification_executor_->ComputeLogits(TensorView<float>(
+          features.data(), {1, static_cast<int>(features.size())}));
+  if (!logits.is_valid()) {
+    TC_LOG(ERROR) << "Couldn't compute logits.";
+    return {};
+  }
+
+  if (logits.dims() != 2 || logits.dim(0) != 1 ||
+      logits.dim(1) != classification_feature_processor_->NumCollections()) {
+    TC_LOG(ERROR) << "Mismatching output";
+    return {};
+  }
+
+  const std::vector<float> scores =
+      ComputeSoftmax(logits.data(), logits.dim(1));
+
+  std::vector<std::pair<std::string, float>> result(scores.size());
+  for (int i = 0; i < scores.size(); i++) {
+    result[i] = {classification_feature_processor_->LabelToCollection(i),
+                 scores[i]};
+  }
+  std::sort(result.begin(), result.end(),
+            [](const std::pair<std::string, float>& a,
+               const std::pair<std::string, float>& b) {
+              return a.second > b.second;
+            });
+
+  // Phone class sanity check.
+  if (result.begin()->first == kPhoneCollection) {
+    const int digit_count = CountDigits(context, selection_indices);
+    if (digit_count <
+            model_->classification_options()->phone_min_num_digits() ||
+        digit_count >
+            model_->classification_options()->phone_max_num_digits()) {
+      return {{kOtherCollection, 1.0}};
+    }
+  }
+
+  return result;
+}
+
+std::vector<AnnotatedSpan> TextClassifier::Annotate(
+    const std::string& context) const {
+  const UnicodeText context_unicode = UTF8ToUnicodeText(context,
+                                                        /*do_copy=*/false);
+
+  std::vector<TokenSpan> chunks;
+  for (const UnicodeTextRange& line :
+       selection_feature_processor_->SplitContext(context_unicode)) {
+    const std::string line_str =
+        UnicodeText::UTF8Substring(line.first, line.second);
+
+    std::vector<Token> tokens;
+    selection_feature_processor_->TokenizeAndFindClick(
+        line_str, {0, std::distance(line.first, line.second)}, &tokens,
+        /*click_pos=*/nullptr);
+    const TokenSpan full_line_span = {0, tokens.size()};
+
+    std::unique_ptr<CachedFeatures> cached_features;
+    if (!classification_feature_processor_->ExtractFeatures(
+            tokens, full_line_span, embedding_executor_.get(),
+            classification_feature_processor_->EmbeddingSize() +
+                classification_feature_processor_->DenseFeaturesCount(),
+            &cached_features)) {
+      TC_LOG(ERROR) << "Could not extract features.";
+      continue;
+    }
+
+    std::vector<TokenSpan> local_chunks;
+    if (!Chunk(tokens.size(), /*span_of_interest=*/full_line_span,
+               *cached_features, &local_chunks)) {
+      TC_LOG(ERROR) << "Could not chunk.";
+      continue;
+    }
+
+    const int offset = std::distance(context_unicode.begin(), line.first);
+    for (const TokenSpan& chunk : local_chunks) {
+      const CodepointSpan codepoint_span =
+          selection_feature_processor_->StripBoundaryCodepoints(
+              line_str, TokenSpanToCodepointSpan(tokens, chunk));
+      chunks.push_back(
+          {codepoint_span.first + offset, codepoint_span.second + offset});
+    }
+  }
+
+  std::vector<AnnotatedSpan> result;
+  for (const CodepointSpan& chunk : chunks) {
+    result.emplace_back();
+    result.back().span = chunk;
+    result.back().classification = ClassifyText(context, chunk);
+  }
+  return result;
+}
+
+bool TextClassifier::Chunk(int num_tokens, const TokenSpan& span_of_interest,
+                           const CachedFeatures& cached_features,
+                           std::vector<TokenSpan>* chunks) const {
+  const int max_selection_span =
+      selection_feature_processor_->GetOptions()->max_selection_span();
+  const int max_chunk_length = selection_feature_processor_->GetOptions()
+                                       ->selection_reduced_output_space()
+                                   ? max_selection_span + 1
+                                   : 2 * max_selection_span + 1;
+
+  struct ScoredChunk {
+    bool operator<(const ScoredChunk& that) const { return score < that.score; }
+
+    TokenSpan token_span;
+    float score;
+  };
+
+  // The inference span is the span of interest expanded to include
+  // max_selection_span tokens on either side, which is how far a selection can
+  // stretch from the click.
+  const TokenSpan inference_span = IntersectTokenSpans(
+      ExpandTokenSpan(span_of_interest,
+                      /*num_tokens_left=*/max_selection_span,
+                      /*num_tokens_right=*/max_selection_span),
+      {0, num_tokens});
+
+  std::vector<ScoredChunk> scored_chunks;
+  // Iterate over chunk candidates that:
+  //   - Are contained in the inference span
+  //   - Have a non-empty intersection with the span of interest
+  //   - Are at least one token long
+  //   - Are not longer than the maximum chunk length
+  for (int start = inference_span.first; start < span_of_interest.second;
+       ++start) {
+    const int leftmost_end_index = std::max(start, span_of_interest.first) + 1;
+    for (int end = leftmost_end_index;
+         end <= inference_span.second && end - start <= max_chunk_length;
+         ++end) {
+      const std::vector<float> features = cached_features.Get({start, end});
+      TensorView<float> logits =
+          selection_executor_->ComputeLogits(TensorView<float>(
+              features.data(), {1, static_cast<int>(features.size())}));
+
+      if (!logits.is_valid()) {
+        TC_LOG(ERROR) << "Couldn't compute logits.";
+        return false;
+      }
+
+      if (logits.dims() != 2 || logits.dim(0) != 1 || logits.dim(1) != 1) {
+        TC_LOG(ERROR) << "Mismatching output";
+        return false;
+      }
+
+      scored_chunks.push_back(ScoredChunk{{start, end}, logits.data()[0]});
+    }
+  }
+
+  std::sort(scored_chunks.rbegin(), scored_chunks.rend());
+
+  // Traverse the candidate chunks from highest-scoring to lowest-scoring. Pick
+  // them greedily as long as they do not overlap with any previously picked
+  // chunks.
+  std::vector<bool> token_used(TokenSpanSize(inference_span));
+  chunks->clear();
+  for (const ScoredChunk& scored_chunk : scored_chunks) {
+    bool feasible = true;
+    for (int i = scored_chunk.token_span.first;
+         i < scored_chunk.token_span.second; ++i) {
+      if (token_used[i - inference_span.first]) {
+        feasible = false;
+        break;
+      }
+    }
+
+    if (!feasible) {
+      continue;
+    }
+
+    for (int i = scored_chunk.token_span.first;
+         i < scored_chunk.token_span.second; ++i) {
+      token_used[i - inference_span.first] = true;
+    }
+
+    chunks->push_back(scored_chunk.token_span);
+  }
+
+  std::sort(chunks->begin(), chunks->end());
+
+  return true;
+}
+
+const Model* ViewModel(const void* buffer, int size) {
+  if (!buffer) {
+    return nullptr;
+  }
+
+  return LoadAndVerifyModel(buffer, size);
+}
+
+}  // namespace libtextclassifier2
diff --git a/text-classifier.h b/text-classifier.h
new file mode 100644
index 0000000..cd84eb4
--- /dev/null
+++ b/text-classifier.h
@@ -0,0 +1,143 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Inference code for the text classification model.
+
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TEXT_CLASSIFIER_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TEXT_CLASSIFIER_H_
+
+#include <memory>
+#include <set>
+#include <string>
+#include <vector>
+
+#include "feature-processor.h"
+#include "model-executor.h"
+#include "model_generated.h"
+#include "strip-unpaired-brackets.h"
+#include "types.h"
+#include "util/memory/mmap.h"
+#include "util/utf8/unilib.h"
+
+namespace libtextclassifier2 {
+
+// A text processing model that provides text classification, annotation,
+// selection suggestion for various types.
+// NOTE: This class is not thread-safe.
+class TextClassifier {
+ public:
+  static std::unique_ptr<TextClassifier> FromUnownedBuffer(const char* buffer,
+                                                           int size);
+  // Takes ownership of the mmap.
+  static std::unique_ptr<TextClassifier> FromScopedMmap(
+      std::unique_ptr<ScopedMmap>* mmap);
+  static std::unique_ptr<TextClassifier> FromFileDescriptor(int fd, int offset,
+                                                            int size);
+  static std::unique_ptr<TextClassifier> FromFileDescriptor(int fd);
+  static std::unique_ptr<TextClassifier> FromPath(const std::string& path);
+
+  // Returns true if the model is ready for use.
+  bool IsInitialized() { return initialized_; }
+
+  // Runs inference for given a context and current selection (i.e. index
+  // of the first and one past last selected characters (utf8 codepoint
+  // offsets)). Returns the indices (utf8 codepoint offsets) of the selection
+  // beginning character and one past selection end character.
+  // Returns the original click_indices if an error occurs.
+  // NOTE: The selection indices are passed in and returned in terms of
+  // UTF8 codepoints (not bytes).
+  // Requires that the model is a smart selection model.
+  CodepointSpan SuggestSelection(const std::string& context,
+                                 CodepointSpan click_indices) const;
+
+  // Classifies the selected text given the context string.
+  // Returns an empty result if an error occurs.
+  std::vector<std::pair<std::string, float>> ClassifyText(
+      const std::string& context, CodepointSpan selection_indices) const;
+
+  // Annotates given input text. The annotations should cover the whole input
+  // context except for whitespaces, and are sorted by their position in the
+  // context string.
+  std::vector<AnnotatedSpan> Annotate(const std::string& context) const;
+
+ protected:
+  // Constructs and initializes text classifier from given model.
+  // Takes ownership of 'mmap', and thus owns the buffer that backs 'model'.
+  TextClassifier(std::unique_ptr<ScopedMmap>* mmap, const Model* model)
+      : model_(model), mmap_(std::move(*mmap)), unilib_(new UniLib()) {
+    ValidateAndInitialize();
+  }
+
+  // Constructs, validates and initializes text classifier from given model.
+  // Does not own the buffer that backs 'model'.
+  explicit TextClassifier(const Model* model)
+      : model_(model), unilib_(new UniLib()) {
+    ValidateAndInitialize();
+  }
+
+  // Checks that model contains all required fields, and initializes internal
+  // datastructures.
+  void ValidateAndInitialize();
+
+  // Initializes regular expressions for the regex model.
+  void InitializeRegexModel();
+
+  // Groups the tokens into chunks. A chunk is a token span that should be the
+  // suggested selection when any of its contained tokens is clicked. The chunks
+  // are non-overlapping and are sorted by their position in the context string.
+  // "num_tokens" is the total number of tokens available (as this method does
+  // not need the actual vector of tokens).
+  // "span_of_interest" is a span of all the tokens that could be clicked.
+  // The resulting chunks all have to overlap with it and they cover this span
+  // completely. The first and last chunk might extend beyond it.
+  // The chunks vector is cleared before filling.
+  bool Chunk(int num_tokens, const TokenSpan& span_of_interest,
+             const CachedFeatures& cached_features,
+             std::vector<TokenSpan>* chunks) const;
+
+  // Collection name for other.
+  const std::string kOtherCollection = "other";
+
+  // Collection name for phone.
+  const std::string kPhoneCollection = "phone";
+
+  const Model* model_;
+
+  std::unique_ptr<ModelExecutor> selection_executor_;
+  std::unique_ptr<ModelExecutor> classification_executor_;
+  std::unique_ptr<EmbeddingExecutor> embedding_executor_;
+
+  std::unique_ptr<FeatureProcessor> selection_feature_processor_;
+  std::unique_ptr<FeatureProcessor> classification_feature_processor_;
+
+ private:
+  struct CompiledRegexPattern {
+    std::string collection_name;
+    std::unique_ptr<UniLib::RegexPattern> pattern;
+  };
+
+  std::unique_ptr<ScopedMmap> mmap_;
+  bool initialized_ = false;
+  std::vector<CompiledRegexPattern> regex_patterns_;
+  std::unique_ptr<UniLib> unilib_;
+};
+
+// Interprets the buffer as a Model flatbuffer and returns it for reading.
+const Model* ViewModel(const void* buffer, int size);
+
+}  // namespace libtextclassifier2
+
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TEXT_CLASSIFIER_H_
diff --git a/text-classifier_test.cc b/text-classifier_test.cc
new file mode 100644
index 0000000..82904e5
--- /dev/null
+++ b/text-classifier_test.cc
@@ -0,0 +1,271 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "text-classifier.h"
+
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <string>
+
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+using testing::ElementsAreArray;
+using testing::Pair;
+
+std::string FirstResult(
+    const std::vector<std::pair<std::string, float>>& results) {
+  if (results.empty()) {
+    return "<INVALID RESULTS>";
+  }
+  return results[0].first;
+}
+
+MATCHER_P3(IsAnnotatedSpan, start, end, best_class, "") {
+  return testing::Value(arg.span, Pair(start, end)) &&
+         testing::Value(FirstResult(arg.classification), best_class);
+}
+
+std::string ReadFile(const std::string& file_name) {
+  std::ifstream file_stream(file_name);
+  return std::string(std::istreambuf_iterator<char>(file_stream), {});
+}
+
+std::string GetModelPath() {
+  return LIBTEXTCLASSIFIER_TEST_DATA_DIR;
+}
+
+TEST(TextClassifierTest, EmbeddingExecutorLoadingFails) {
+  std::unique_ptr<TextClassifier> classifier =
+      TextClassifier::FromPath(GetModelPath() + "wrong_embeddings.fb");
+  EXPECT_FALSE(classifier);
+}
+
+TEST(TextClassifierTest, ClassifyText) {
+  std::unique_ptr<TextClassifier> classifier =
+      TextClassifier::FromPath(GetModelPath() + "test_model.fb");
+  ASSERT_TRUE(classifier);
+
+  EXPECT_EQ("other",
+            FirstResult(classifier->ClassifyText(
+                "this afternoon Barack Obama gave a speech at", {15, 27})));
+  EXPECT_EQ("other",
+            FirstResult(classifier->ClassifyText("you@android.com", {0, 15})));
+  EXPECT_EQ("other", FirstResult(classifier->ClassifyText(
+                         "Contact me at you@android.com", {14, 29})));
+  EXPECT_EQ("phone", FirstResult(classifier->ClassifyText(
+                         "Call me at (800) 123-456 today", {11, 24})));
+  EXPECT_EQ("other", FirstResult(classifier->ClassifyText(
+                         "Visit www.google.com every today!", {6, 20})));
+
+  // More lines.
+  EXPECT_EQ("other",
+            FirstResult(classifier->ClassifyText(
+                "this afternoon Barack Obama gave a speech at|Visit "
+                "www.google.com every today!|Call me at (800) 123-456 today.",
+                {15, 27})));
+  EXPECT_EQ("other",
+            FirstResult(classifier->ClassifyText(
+                "this afternoon Barack Obama gave a speech at|Visit "
+                "www.google.com every today!|Call me at (800) 123-456 today.",
+                {51, 65})));
+  EXPECT_EQ("phone",
+            FirstResult(classifier->ClassifyText(
+                "this afternoon Barack Obama gave a speech at|Visit "
+                "www.google.com every today!|Call me at (800) 123-456 today.",
+                {90, 103})));
+
+  // Single word.
+  EXPECT_EQ("other", FirstResult(classifier->ClassifyText("obama", {0, 5})));
+  EXPECT_EQ("other", FirstResult(classifier->ClassifyText("asdf", {0, 4})));
+  EXPECT_EQ("<INVALID RESULTS>",
+            FirstResult(classifier->ClassifyText("asdf", {0, 0})));
+
+  // Junk.
+  EXPECT_EQ("<INVALID RESULTS>",
+            FirstResult(classifier->ClassifyText("", {0, 0})));
+  EXPECT_EQ("<INVALID RESULTS>", FirstResult(classifier->ClassifyText(
+                                     "a\n\n\n\nx x x\n\n\n\n\n\n", {1, 5})));
+}
+
+TEST(TextClassifierTest, PhoneFiltering) {
+  std::unique_ptr<TextClassifier> classifier =
+      TextClassifier::FromPath(GetModelPath() + "test_model.fb");
+  ASSERT_TRUE(classifier);
+
+  EXPECT_EQ("phone", FirstResult(classifier->ClassifyText(
+                         "phone: (123) 456 789", {7, 20})));
+  EXPECT_EQ("phone", FirstResult(classifier->ClassifyText(
+                         "phone: (123) 456 789,0001112", {7, 25})));
+  EXPECT_EQ("other", FirstResult(classifier->ClassifyText(
+                         "phone: (123) 456 789,0001112", {7, 28})));
+}
+
+TEST(TextClassifierTest, SuggestSelection) {
+  std::unique_ptr<TextClassifier> classifier =
+      TextClassifier::FromPath(GetModelPath() + "test_model.fb");
+  ASSERT_TRUE(classifier);
+
+  EXPECT_EQ(classifier->SuggestSelection(
+                "this afternoon Barack Obama gave a speech at", {15, 21}),
+            std::make_pair(15, 21));
+
+  // Try passing whole string.
+  // If more than 1 token is specified, we should return back what entered.
+  EXPECT_EQ(
+      classifier->SuggestSelection("350 Third Street, Cambridge", {0, 27}),
+      std::make_pair(0, 27));
+
+  // Single letter.
+  EXPECT_EQ(classifier->SuggestSelection("a", {0, 1}), std::make_pair(0, 1));
+
+  // Single word.
+  EXPECT_EQ(classifier->SuggestSelection("asdf", {0, 4}), std::make_pair(0, 4));
+
+  EXPECT_EQ(
+      classifier->SuggestSelection("call me at 857 225 3556 today", {11, 14}),
+      std::make_pair(11, 23));
+
+  // Unpaired bracket stripping.
+  EXPECT_EQ(
+      classifier->SuggestSelection("call me at (857) 225 3556 today", {11, 16}),
+      std::make_pair(11, 25));
+  EXPECT_EQ(
+      classifier->SuggestSelection("call me at (857 225 3556 today", {11, 15}),
+      std::make_pair(12, 24));
+  EXPECT_EQ(
+      classifier->SuggestSelection("call me at 857 225 3556) today", {11, 14}),
+      std::make_pair(11, 23));
+  EXPECT_EQ(
+      classifier->SuggestSelection("call me at )857 225 3556( today", {11, 15}),
+      std::make_pair(12, 24));
+
+  // If the resulting selection would be empty, the original span is returned.
+  EXPECT_EQ(classifier->SuggestSelection("call me at )( today", {11, 13}),
+            std::make_pair(11, 13));
+  EXPECT_EQ(classifier->SuggestSelection("call me at ( today", {11, 12}),
+            std::make_pair(11, 12));
+  EXPECT_EQ(classifier->SuggestSelection("call me at ) today", {11, 12}),
+            std::make_pair(11, 12));
+}
+
+TEST(TextClassifierTest, SuggestSelectionsAreSymmetric) {
+  std::unique_ptr<TextClassifier> classifier =
+      TextClassifier::FromPath(GetModelPath() + "test_model.fb");
+  ASSERT_TRUE(classifier);
+
+  EXPECT_EQ(classifier->SuggestSelection("350 Third Street, Cambridge", {0, 3}),
+            std::make_pair(0, 27));
+  EXPECT_EQ(classifier->SuggestSelection("350 Third Street, Cambridge", {4, 9}),
+            std::make_pair(0, 27));
+  EXPECT_EQ(
+      classifier->SuggestSelection("350 Third Street, Cambridge", {10, 16}),
+      std::make_pair(0, 27));
+  EXPECT_EQ(classifier->SuggestSelection("a\nb\nc\n350 Third Street, Cambridge",
+                                         {16, 22}),
+            std::make_pair(6, 33));
+}
+
+TEST(TextClassifierTest, SuggestSelectionWithNewLine) {
+  std::unique_ptr<TextClassifier> classifier =
+      TextClassifier::FromPath(GetModelPath() + "test_model.fb");
+  ASSERT_TRUE(classifier);
+
+  EXPECT_EQ(classifier->SuggestSelection("abc\n857 225 3556", {4, 7}),
+            std::make_pair(4, 16));
+  EXPECT_EQ(classifier->SuggestSelection("857 225 3556\nabc", {0, 3}),
+            std::make_pair(0, 12));
+}
+
+TEST(TextClassifierTest, SuggestSelectionWithPunctuation) {
+  std::unique_ptr<TextClassifier> classifier =
+      TextClassifier::FromPath(GetModelPath() + "test_model.fb");
+  ASSERT_TRUE(classifier);
+
+  // From the right.
+  EXPECT_EQ(classifier->SuggestSelection(
+                "this afternoon BarackObama, gave a speech at", {15, 26}),
+            std::make_pair(15, 26));
+
+  // From the right multiple.
+  EXPECT_EQ(classifier->SuggestSelection(
+                "this afternoon BarackObama,.,.,, gave a speech at", {15, 26}),
+            std::make_pair(15, 26));
+
+  // From the left multiple.
+  EXPECT_EQ(classifier->SuggestSelection(
+                "this afternoon ,.,.,,BarackObama gave a speech at", {21, 32}),
+            std::make_pair(21, 32));
+
+  // From both sides.
+  EXPECT_EQ(classifier->SuggestSelection(
+                "this afternoon !BarackObama,- gave a speech at", {16, 27}),
+            std::make_pair(16, 27));
+}
+
+TEST(TextClassifierTest, SuggestSelectionNoCrashWithJunk) {
+  std::unique_ptr<TextClassifier> classifier =
+      TextClassifier::FromPath(GetModelPath() + "test_model.fb");
+  ASSERT_TRUE(classifier);
+
+  // Try passing in bunch of invalid selections.
+  EXPECT_EQ(classifier->SuggestSelection("", {0, 27}), std::make_pair(0, 27));
+  EXPECT_EQ(classifier->SuggestSelection("", {-10, 27}),
+            std::make_pair(-10, 27));
+  EXPECT_EQ(classifier->SuggestSelection("Word 1 2 3 hello!", {0, 27}),
+            std::make_pair(0, 27));
+  EXPECT_EQ(classifier->SuggestSelection("Word 1 2 3 hello!", {-30, 300}),
+            std::make_pair(-30, 300));
+  EXPECT_EQ(classifier->SuggestSelection("Word 1 2 3 hello!", {-10, -1}),
+            std::make_pair(-10, -1));
+  EXPECT_EQ(classifier->SuggestSelection("Word 1 2 3 hello!", {100, 17}),
+            std::make_pair(100, 17));
+}
+
+TEST(TextClassifierTest, Annotate) {
+  std::unique_ptr<TextClassifier> classifier =
+      TextClassifier::FromPath(GetModelPath() + "test_model.fb");
+  ASSERT_TRUE(classifier);
+
+  const std::string test_string =
+      "& saw Barak Obama today .. 350 Third Street, Cambridge\nand my phone "
+      "number is 853 225 3556.";
+  EXPECT_THAT(classifier->Annotate(test_string),
+              ElementsAreArray({
+                  IsAnnotatedSpan(0, 0, "<INVALID RESULTS>"),
+                  IsAnnotatedSpan(2, 5, "other"),
+                  IsAnnotatedSpan(6, 11, "other"),
+                  IsAnnotatedSpan(12, 17, "other"),
+                  IsAnnotatedSpan(18, 23, "other"),
+                  IsAnnotatedSpan(24, 24, "<INVALID RESULTS>"),
+                  IsAnnotatedSpan(27, 54, "address"),
+                  IsAnnotatedSpan(55, 58, "other"),
+                  IsAnnotatedSpan(59, 61, "other"),
+                  IsAnnotatedSpan(62, 67, "other"),
+                  IsAnnotatedSpan(68, 74, "other"),
+                  IsAnnotatedSpan(75, 77, "other"),
+                  IsAnnotatedSpan(78, 90, "phone"),
+              }));
+}
+
+// TODO(jacekj): Test the regex functionality.
+
+}  // namespace
+}  // namespace libtextclassifier2
diff --git a/textclassifier_jni.cc b/textclassifier_jni.cc
index 8740f4c..ecc6500 100644
--- a/textclassifier_jni.cc
+++ b/textclassifier_jni.cc
@@ -14,20 +14,20 @@
  * limitations under the License.
  */
 
-// Simple JNI wrapper for the SmartSelection library.
+// JNI wrapper for the TextClassifier.
 
 #include "textclassifier_jni.h"
 
 #include <jni.h>
 #include <vector>
 
-#include "lang_id/lang-id.h"
-#include "smartselect/text-classification-model.h"
+#include "text-classifier.h"
 #include "util/java/scoped_local_ref.h"
+#include "util/memory/mmap.h"
 
-using libtextclassifier::ModelOptions;
-using libtextclassifier::TextClassificationModel;
-using libtextclassifier::nlp_core::lang_id::LangId;
+using libtextclassifier2::AnnotatedSpan;
+using libtextclassifier2::Model;
+using libtextclassifier2::TextClassifier;
 
 namespace {
 
@@ -101,17 +101,17 @@
 
 }  // namespace
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
-using libtextclassifier::CodepointSpan;
+using libtextclassifier2::CodepointSpan;
 
 namespace {
 
 CodepointSpan ConvertIndicesBMPUTF8(const std::string& utf8_str,
                                     CodepointSpan orig_indices,
                                     bool from_utf8) {
-  const libtextclassifier::UnicodeText unicode_str =
-      libtextclassifier::UTF8ToUnicodeText(utf8_str, /*do_copy=*/false);
+  const libtextclassifier2::UnicodeText unicode_str =
+      libtextclassifier2::UTF8ToUnicodeText(utf8_str, /*do_copy=*/false);
 
   int unicode_index = 0;
   int bmp_index = 0;
@@ -155,77 +155,78 @@
 }  // namespace
 
 CodepointSpan ConvertIndicesBMPToUTF8(const std::string& utf8_str,
-                                      CodepointSpan orig_indices) {
-  return ConvertIndicesBMPUTF8(utf8_str, orig_indices, /*from_utf8=*/false);
+                                      CodepointSpan bmp_indices) {
+  return ConvertIndicesBMPUTF8(utf8_str, bmp_indices, /*from_utf8=*/false);
 }
 
 CodepointSpan ConvertIndicesUTF8ToBMP(const std::string& utf8_str,
-                                      CodepointSpan orig_indices) {
-  return ConvertIndicesBMPUTF8(utf8_str, orig_indices, /*from_utf8=*/true);
+                                      CodepointSpan utf8_indices) {
+  return ConvertIndicesBMPUTF8(utf8_str, utf8_indices, /*from_utf8=*/true);
 }
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-using libtextclassifier::CodepointSpan;
-using libtextclassifier::ConvertIndicesBMPToUTF8;
-using libtextclassifier::ConvertIndicesUTF8ToBMP;
-using libtextclassifier::ScopedLocalRef;
+using libtextclassifier2::CodepointSpan;
+using libtextclassifier2::ConvertIndicesBMPToUTF8;
+using libtextclassifier2::ConvertIndicesUTF8ToBMP;
+using libtextclassifier2::ScopedLocalRef;
 
-JNI_METHOD(jlong, SmartSelection, nativeNew)
+JNI_METHOD(jlong, TC_CLASS_NAME, nativeNew)
 (JNIEnv* env, jobject thiz, jint fd) {
-  TextClassificationModel* model = new TextClassificationModel(fd);
-  return reinterpret_cast<jlong>(model);
+  return reinterpret_cast<jlong>(
+      TextClassifier::FromFileDescriptor(fd).release());
 }
 
-JNI_METHOD(jlong, SmartSelection, nativeNewFromPath)
+JNI_METHOD(jlong, TC_CLASS_NAME, nativeNewFromPath)
 (JNIEnv* env, jobject thiz, jstring path) {
   const std::string path_str = ToStlString(env, path);
-  TextClassificationModel* model = new TextClassificationModel(path_str);
-  return reinterpret_cast<jlong>(model);
+  return reinterpret_cast<jlong>(TextClassifier::FromPath(path_str).release());
 }
 
-JNI_METHOD(jlong, SmartSelection, nativeNewFromAssetFileDescriptor)
+JNI_METHOD(jlong, TC_CLASS_NAME, nativeNewFromAssetFileDescriptor)
 (JNIEnv* env, jobject thiz, jobject afd, jlong offset, jlong size) {
   // Get system-level file descriptor from AssetFileDescriptor.
   ScopedLocalRef<jclass> afd_class(
       env->FindClass("android/content/res/AssetFileDescriptor"), env);
   if (afd_class == nullptr) {
-    TC_LOG(ERROR) << "Couln't find AssetFileDescriptor.";
+    TC_LOG(ERROR) << "Couldn't find AssetFileDescriptor.";
     return reinterpret_cast<jlong>(nullptr);
   }
   jmethodID afd_class_getFileDescriptor = env->GetMethodID(
       afd_class.get(), "getFileDescriptor", "()Ljava/io/FileDescriptor;");
   if (afd_class_getFileDescriptor == nullptr) {
-    TC_LOG(ERROR) << "Couln't find getFileDescriptor.";
+    TC_LOG(ERROR) << "Couldn't find getFileDescriptor.";
     return reinterpret_cast<jlong>(nullptr);
   }
 
   ScopedLocalRef<jclass> fd_class(env->FindClass("java/io/FileDescriptor"),
                                   env);
   if (fd_class == nullptr) {
-    TC_LOG(ERROR) << "Couln't find FileDescriptor.";
+    TC_LOG(ERROR) << "Couldn't find FileDescriptor.";
     return reinterpret_cast<jlong>(nullptr);
   }
   jfieldID fd_class_descriptor =
       env->GetFieldID(fd_class.get(), "descriptor", "I");
   if (fd_class_descriptor == nullptr) {
-    TC_LOG(ERROR) << "Couln't find descriptor.";
+    TC_LOG(ERROR) << "Couldn't find descriptor.";
     return reinterpret_cast<jlong>(nullptr);
   }
 
   jobject bundle_jfd = env->CallObjectMethod(afd, afd_class_getFileDescriptor);
   jint bundle_cfd = env->GetIntField(bundle_jfd, fd_class_descriptor);
 
-  TextClassificationModel* model =
-      new TextClassificationModel(bundle_cfd, offset, size);
-  return reinterpret_cast<jlong>(model);
+  return reinterpret_cast<jlong>(
+      TextClassifier::FromFileDescriptor(bundle_cfd, offset, size).release());
 }
 
-JNI_METHOD(jintArray, SmartSelection, nativeSuggest)
+JNI_METHOD(jintArray, TC_CLASS_NAME, nativeSuggest)
 (JNIEnv* env, jobject thiz, jlong ptr, jstring context, jint selection_begin,
  jint selection_end) {
-  TextClassificationModel* model =
-      reinterpret_cast<TextClassificationModel*>(ptr);
+  if (!ptr) {
+    return nullptr;
+  }
+
+  TextClassifier* model = reinterpret_cast<TextClassifier*>(ptr);
 
   const std::string context_utf8 = ToStlString(env, context);
   CodepointSpan input_indices =
@@ -240,39 +241,42 @@
   return result;
 }
 
-JNI_METHOD(jobjectArray, SmartSelection, nativeClassifyText)
+JNI_METHOD(jobjectArray, TC_CLASS_NAME, nativeClassifyText)
 (JNIEnv* env, jobject thiz, jlong ptr, jstring context, jint selection_begin,
- jint selection_end, jint input_flags) {
-  TextClassificationModel* ff_model =
-      reinterpret_cast<TextClassificationModel*>(ptr);
+ jint selection_end) {
+  if (!ptr) {
+    return nullptr;
+  }
+  TextClassifier* ff_model = reinterpret_cast<TextClassifier*>(ptr);
   const std::vector<std::pair<std::string, float>> classification_result =
       ff_model->ClassifyText(ToStlString(env, context),
-                             {selection_begin, selection_end}, input_flags);
+                             {selection_begin, selection_end});
 
   return ScoredStringsToJObjectArray(
-      env, TC_PACKAGE_PATH "SmartSelection$ClassificationResult",
+      env, TC_PACKAGE_PATH TC_CLASS_NAME_STR "$ClassificationResult",
       classification_result);
 }
 
-JNI_METHOD(jobjectArray, SmartSelection, nativeAnnotate)
+JNI_METHOD(jobjectArray, TC_CLASS_NAME, nativeAnnotate)
 (JNIEnv* env, jobject thiz, jlong ptr, jstring context) {
-  TextClassificationModel* model =
-      reinterpret_cast<TextClassificationModel*>(ptr);
+  if (!ptr) {
+    return nullptr;
+  }
+  TextClassifier* model = reinterpret_cast<TextClassifier*>(ptr);
   std::string context_utf8 = ToStlString(env, context);
-  std::vector<TextClassificationModel::AnnotatedSpan> annotations =
-      model->Annotate(context_utf8);
+  std::vector<AnnotatedSpan> annotations = model->Annotate(context_utf8);
 
   jclass result_class =
-      env->FindClass(TC_PACKAGE_PATH "SmartSelection$AnnotatedSpan");
+      env->FindClass(TC_PACKAGE_PATH TC_CLASS_NAME_STR "$AnnotatedSpan");
   if (!result_class) {
     TC_LOG(ERROR) << "Couldn't find result class: "
-                  << TC_PACKAGE_PATH "SmartSelection$AnnotatedSpan";
+                  << TC_PACKAGE_PATH TC_CLASS_NAME_STR "$AnnotatedSpan";
     return nullptr;
   }
 
   jmethodID result_class_constructor = env->GetMethodID(
       result_class, "<init>",
-      "(II[L" TC_PACKAGE_PATH "SmartSelection$ClassificationResult;)V");
+      "(II[L" TC_PACKAGE_PATH TC_CLASS_NAME_STR "$ClassificationResult;)V");
 
   jobjectArray results =
       env->NewObjectArray(annotations.size(), result_class, nullptr);
@@ -284,7 +288,7 @@
         result_class, result_class_constructor,
         static_cast<jint>(span_bmp.first), static_cast<jint>(span_bmp.second),
         ScoredStringsToJObjectArray(
-            env, TC_PACKAGE_PATH "SmartSelection$ClassificationResult",
+            env, TC_PACKAGE_PATH TC_CLASS_NAME_STR "$ClassificationResult",
             annotations[i].classification));
     env->SetObjectArrayElement(results, i, result);
     env->DeleteLocalRef(result);
@@ -293,58 +297,38 @@
   return results;
 }
 
-JNI_METHOD(void, SmartSelection, nativeClose)
+JNI_METHOD(void, TC_CLASS_NAME, nativeClose)
 (JNIEnv* env, jobject thiz, jlong ptr) {
-  TextClassificationModel* model =
-      reinterpret_cast<TextClassificationModel*>(ptr);
+  TextClassifier* model = reinterpret_cast<TextClassifier*>(ptr);
   delete model;
 }
 
-JNI_METHOD(jstring, SmartSelection, nativeGetLanguage)
+JNI_METHOD(jstring, TC_CLASS_NAME, nativeGetLanguage)
 (JNIEnv* env, jobject clazz, jint fd) {
-  ModelOptions model_options;
-  if (ReadSelectionModelOptions(fd, &model_options)) {
-    return env->NewStringUTF(model_options.language().c_str());
-  } else {
-    return env->NewStringUTF("UNK");
+  std::unique_ptr<libtextclassifier2::ScopedMmap> mmap(
+      new libtextclassifier2::ScopedMmap(fd));
+  if (!mmap->handle().ok()) {
+    return env->NewStringUTF("");
   }
-}
-
-JNI_METHOD(jint, SmartSelection, nativeGetVersion)
-(JNIEnv* env, jobject clazz, jint fd) {
-  ModelOptions model_options;
-  if (ReadSelectionModelOptions(fd, &model_options)) {
-    return model_options.version();
-  } else {
-    return -1;
+  const Model* model = libtextclassifier2::ViewModel(
+      mmap->handle().start(), mmap->handle().num_bytes());
+  if (!model || !model->language()) {
+    return env->NewStringUTF("");
   }
+  return env->NewStringUTF(model->language()->c_str());
 }
 
-#ifndef LIBTEXTCLASSIFIER_DISABLE_LANG_ID
-JNI_METHOD(jlong, LangId, nativeNew)
-(JNIEnv* env, jobject thiz, jint fd) {
-  return reinterpret_cast<jlong>(new LangId(fd));
-}
-
-JNI_METHOD(jobjectArray, LangId, nativeFindLanguages)
-(JNIEnv* env, jobject thiz, jlong ptr, jstring text) {
-  LangId* lang_id = reinterpret_cast<LangId*>(ptr);
-  const std::vector<std::pair<std::string, float>> scored_languages =
-      lang_id->FindLanguages(ToStlString(env, text));
-
-  return ScoredStringsToJObjectArray(
-      env, TC_PACKAGE_PATH "LangId$ClassificationResult", scored_languages);
-}
-
-JNI_METHOD(void, LangId, nativeClose)
-(JNIEnv* env, jobject thiz, jlong ptr) {
-  LangId* lang_id = reinterpret_cast<LangId*>(ptr);
-  delete lang_id;
-}
-
-JNI_METHOD(int, LangId, nativeGetVersion)
+JNI_METHOD(jint, TC_CLASS_NAME, nativeGetVersion)
 (JNIEnv* env, jobject clazz, jint fd) {
-  std::unique_ptr<LangId> lang_id(new LangId(fd));
-  return lang_id->version();
+  std::unique_ptr<libtextclassifier2::ScopedMmap> mmap(
+      new libtextclassifier2::ScopedMmap(fd));
+  if (!mmap->handle().ok()) {
+    return 0;
+  }
+  const Model* model = libtextclassifier2::ViewModel(
+      mmap->handle().start(), mmap->handle().num_bytes());
+  if (!model) {
+    return 0;
+  }
+  return model->version();
 }
-#endif
diff --git a/textclassifier_jni.h b/textclassifier_jni.h
index 1709ff4..1f64fff 100644
--- a/textclassifier_jni.h
+++ b/textclassifier_jni.h
@@ -14,17 +14,28 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_TEXTCLASSIFIER_JNI_H_
-#define LIBTEXTCLASSIFIER_TEXTCLASSIFIER_JNI_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TEXTCLASSIFIER_JNI_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TEXTCLASSIFIER_JNI_H_
 
 #include <jni.h>
 #include <string>
 
-#include "smartselect/types.h"
+#include "types.h"
+
+// When we use a macro as an argument for a macro, an additional level of
+// indirection is needed, if the macro argument is used with # or ##.
+#define ADD_QUOTES_HELPER(TOKEN) #TOKEN
+#define ADD_QUOTES(TOKEN) ADD_QUOTES_HELPER(TOKEN)
 
 #ifndef TC_PACKAGE_NAME
 #define TC_PACKAGE_NAME android_view_textclassifier
 #endif
+
+#ifndef TC_CLASS_NAME
+#define TC_CLASS_NAME SmartSelection
+#endif
+#define TC_CLASS_NAME_STR ADD_QUOTES(TC_CLASS_NAME)
+
 #ifndef TC_PACKAGE_PATH
 #define TC_PACKAGE_PATH "android/view/textclassifier/"
 #endif
@@ -35,6 +46,7 @@
       Java_##package_name##_##class_name##_##method_name
 
 // The indirection is needed to correctly expand the TC_PACKAGE_NAME macro.
+// See the explanation near ADD_QUOTES macro.
 #define JNI_METHOD2(return_type, package_name, class_name, method_name) \
   JNI_METHOD_PRIMITIVE(return_type, package_name, class_name, method_name)
 
@@ -46,63 +58,52 @@
 #endif
 
 // SmartSelection.
-JNI_METHOD(jlong, SmartSelection, nativeNew)
+JNI_METHOD(jlong, TC_CLASS_NAME, nativeNew)
 (JNIEnv* env, jobject thiz, jint fd);
 
-JNI_METHOD(jlong, SmartSelection, nativeNewFromPath)
+JNI_METHOD(jlong, TC_CLASS_NAME, nativeNewFromPath)
 (JNIEnv* env, jobject thiz, jstring path);
 
-JNI_METHOD(jlong, SmartSelection, nativeNewFromAssetFileDescriptor)
+JNI_METHOD(jlong, TC_CLASS_NAME, nativeNewFromAssetFileDescriptor)
 (JNIEnv* env, jobject thiz, jobject afd, jlong offset, jlong size);
 
-JNI_METHOD(jintArray, SmartSelection, nativeSuggest)
+JNI_METHOD(jintArray, TC_CLASS_NAME, nativeSuggest)
 (JNIEnv* env, jobject thiz, jlong ptr, jstring context, jint selection_begin,
  jint selection_end);
 
-JNI_METHOD(jobjectArray, SmartSelection, nativeClassifyText)
+JNI_METHOD(jobjectArray, TC_CLASS_NAME, nativeClassifyText)
 (JNIEnv* env, jobject thiz, jlong ptr, jstring context, jint selection_begin,
- jint selection_end, jint input_flags);
+ jint selection_end);
 
-JNI_METHOD(jobjectArray, SmartSelection, nativeAnnotate)
+JNI_METHOD(jobjectArray, TC_CLASS_NAME, nativeAnnotate)
 (JNIEnv* env, jobject thiz, jlong ptr, jstring context);
 
-JNI_METHOD(void, SmartSelection, nativeClose)
+JNI_METHOD(void, TC_CLASS_NAME, nativeClose)
 (JNIEnv* env, jobject thiz, jlong ptr);
 
-JNI_METHOD(jstring, SmartSelection, nativeGetLanguage)
+JNI_METHOD(jstring, TC_CLASS_NAME, nativeGetLanguage)
 (JNIEnv* env, jobject clazz, jint fd);
 
-JNI_METHOD(jint, SmartSelection, nativeGetVersion)
+JNI_METHOD(jint, TC_CLASS_NAME, nativeGetVersion)
 (JNIEnv* env, jobject clazz, jint fd);
 
-#ifndef LIBTEXTCLASSIFIER_DISABLE_LANG_ID
-// LangId.
-JNI_METHOD(jlong, LangId, nativeNew)(JNIEnv* env, jobject thiz, jint fd);
-
-JNI_METHOD(jobjectArray, LangId, nativeFindLanguages)
-(JNIEnv* env, jobject thiz, jlong ptr, jstring text);
-
-JNI_METHOD(void, LangId, nativeClose)(JNIEnv* env, jobject thiz, jlong ptr);
-
-JNI_METHOD(int, LangId, nativeGetVersion)(JNIEnv* env, jobject clazz, jint fd);
-#endif
-
 #ifdef __cplusplus
 }
 #endif
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 // Given a utf8 string and a span expressed in Java BMP (basic multilingual
 // plane) codepoints, converts it to a span expressed in utf8 codepoints.
-libtextclassifier::CodepointSpan ConvertIndicesBMPToUTF8(
-    const std::string& utf8_str, libtextclassifier::CodepointSpan bmp_indices);
+libtextclassifier2::CodepointSpan ConvertIndicesBMPToUTF8(
+    const std::string& utf8_str, libtextclassifier2::CodepointSpan bmp_indices);
 
 // Given a utf8 string and a span expressed in utf8 codepoints, converts it to a
 // span expressed in Java BMP (basic multilingual plane) codepoints.
-libtextclassifier::CodepointSpan ConvertIndicesUTF8ToBMP(
-    const std::string& utf8_str, libtextclassifier::CodepointSpan utf8_indices);
+libtextclassifier2::CodepointSpan ConvertIndicesUTF8ToBMP(
+    const std::string& utf8_str,
+    libtextclassifier2::CodepointSpan utf8_indices);
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_TEXTCLASSIFIER_JNI_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TEXTCLASSIFIER_JNI_H_
diff --git a/textclassifier_jni_test.cc b/textclassifier_jni_test.cc
index ffc193b..87b96fa 100644
--- a/textclassifier_jni_test.cc
+++ b/textclassifier_jni_test.cc
@@ -19,7 +19,7 @@
 #include "gmock/gmock.h"
 #include "gtest/gtest.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 namespace {
 
 TEST(TextClassifier, ConvertIndicesBMPUTF8) {
@@ -76,4 +76,4 @@
 }
 
 }  // namespace
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/smartselect/token-feature-extractor.cc b/token-feature-extractor.cc
similarity index 74%
rename from smartselect/token-feature-extractor.cc
rename to token-feature-extractor.cc
index 6afd951..33c4d75 100644
--- a/smartselect/token-feature-extractor.cc
+++ b/token-feature-extractor.cc
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-#include "smartselect/token-feature-extractor.h"
+#include "token-feature-extractor.h"
 
 #include <cctype>
 #include <string>
@@ -23,12 +23,8 @@
 #include "util/hash/farmhash.h"
 #include "util/strings/stringpiece.h"
 #include "util/utf8/unicodetext.h"
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-#include "unicode/regex.h"
-#include "unicode/uchar.h"
-#endif
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 namespace {
 
@@ -50,69 +46,41 @@
   return copy;
 }
 
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
 void RemapTokenUnicode(const std::string& token,
                        const TokenFeatureExtractorOptions& options,
-                       UnicodeText* remapped) {
+                       const UniLib& unilib, UnicodeText* remapped) {
   if (!options.remap_digits && !options.lowercase_tokens) {
     // Leave remapped untouched.
     return;
   }
 
   UnicodeText word = UTF8ToUnicodeText(token, /*do_copy=*/false);
-  icu::UnicodeString icu_string;
+  remapped->clear();
   for (auto it = word.begin(); it != word.end(); ++it) {
-    if (options.remap_digits && u_isdigit(*it)) {
-      icu_string.append('0');
+    if (options.remap_digits && unilib.IsDigit(*it)) {
+      remapped->AppendCodepoint('0');
     } else if (options.lowercase_tokens) {
-      icu_string.append(u_tolower(*it));
+      remapped->AppendCodepoint(unilib.ToLower(*it));
     } else {
-      icu_string.append(*it);
+      remapped->AppendCodepoint(*it);
     }
   }
-  std::string utf8_str;
-  icu_string.toUTF8String(utf8_str);
-  remapped->CopyUTF8(utf8_str.data(), utf8_str.length());
 }
-#endif
 
 }  // namespace
 
 TokenFeatureExtractor::TokenFeatureExtractor(
-    const TokenFeatureExtractorOptions& options)
-    : options_(options) {
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-  UErrorCode status;
+    const TokenFeatureExtractorOptions& options, const UniLib& unilib)
+    : options_(options), unilib_(unilib) {
   for (const std::string& pattern : options.regexp_features) {
-    status = U_ZERO_ERROR;
-    regex_patterns_.push_back(
-        std::unique_ptr<icu::RegexPattern>(icu::RegexPattern::compile(
-            icu::UnicodeString(pattern.c_str(), pattern.size(), "utf-8"), 0,
-            status)));
-    if (U_FAILURE(status)) {
-      TC_LOG(WARNING) << "Failed to load pattern" << pattern;
-    }
+    regex_patterns_.push_back(std::unique_ptr<UniLib::RegexPattern>(
+        unilib_.CreateRegexPattern(pattern)));
   }
-#else
-  bool found_unsupported_regexp_features = false;
-  for (const std::string& pattern : options.regexp_features) {
-    // A temporary solution to support this specific regexp pattern without
-    // adding too much binary size.
-    if (pattern == "^[^a-z]*$") {
-      enable_all_caps_feature_ = true;
-    } else {
-      found_unsupported_regexp_features = true;
-    }
-  }
-  if (found_unsupported_regexp_features) {
-    TC_LOG(WARNING) << "ICU not supported regexp features ignored.";
-  }
-#endif
 }
 
 int TokenFeatureExtractor::HashToken(StringPiece token) const {
   if (options_.allowed_chargrams.empty()) {
-    return tcfarmhash::Fingerprint64(token) % options_.num_buckets;
+    return tc2farmhash::Fingerprint64(token) % options_.num_buckets;
   } else {
     // Padding and out-of-vocabulary tokens have extra buckets reserved because
     // they are special and important tokens, and we don't want them to share
@@ -126,7 +94,7 @@
                options_.allowed_chargrams.end()) {
       return 0;  // Out-of-vocabulary.
     } else {
-      return (tcfarmhash::Fingerprint64(token) %
+      return (tc2farmhash::Fingerprint64(token) %
               (options_.num_buckets - kNumExtraBuckets)) +
              kNumExtraBuckets;
     }
@@ -192,13 +160,12 @@
 
 std::vector<int> TokenFeatureExtractor::ExtractCharactergramFeaturesUnicode(
     const Token& token) const {
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
   std::vector<int> result;
   if (token.is_padding || token.value.empty()) {
     result.push_back(HashToken("<PAD>"));
   } else {
     UnicodeText word = UTF8ToUnicodeText(token.value, /*do_copy=*/false);
-    RemapTokenUnicode(token.value, options_, &word);
+    RemapTokenUnicode(token.value, options_, unilib_, &word);
 
     // Trim the word if needed by finding a left-cut point and right-cut point.
     auto left_cut = word.begin();
@@ -268,10 +235,6 @@
     }
   }
   return result;
-#else
-  TC_LOG(WARNING) << "ICU not supported. No feature extracted.";
-  return {};
-#endif
 }
 
 bool TokenFeatureExtractor::Extract(const Token& token, bool is_in_span,
@@ -287,13 +250,7 @@
     if (options_.unicode_aware_features) {
       UnicodeText token_unicode =
           UTF8ToUnicodeText(token.value, /*do_copy=*/false);
-      bool is_upper;
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-      is_upper = u_isupper(*token_unicode.begin());
-#else
-      TC_LOG(WARNING) << "Using non-unicode isupper because ICU is disabled.";
-      is_upper = isupper(*token_unicode.begin());
-#endif
+      const bool is_upper = unilib_.IsUpper(*token_unicode.begin());
       if (!token.value.empty() && is_upper) {
         dense_features->push_back(1.0);
       } else {
@@ -320,46 +277,23 @@
     }
   }
 
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
   // Add regexp features.
   if (!regex_patterns_.empty()) {
-    icu::UnicodeString unicode_str(token.value.c_str(), token.value.size(),
-                                   "utf-8");
     for (int i = 0; i < regex_patterns_.size(); ++i) {
       if (!regex_patterns_[i].get()) {
         dense_features->push_back(-1.0);
         continue;
       }
 
-      // Check for match.
-      UErrorCode status = U_ZERO_ERROR;
-      std::unique_ptr<icu::RegexMatcher> matcher(
-          regex_patterns_[i]->matcher(unicode_str, status));
-      if (matcher->find()) {
+      if (regex_patterns_[i]->Matches(token.value)) {
         dense_features->push_back(1.0);
       } else {
         dense_features->push_back(-1.0);
       }
     }
   }
-#else
-  if (enable_all_caps_feature_) {
-    bool is_all_caps = true;
-    for (const char character_byte : token.value) {
-      if (islower(character_byte)) {
-        is_all_caps = false;
-        break;
-      }
-    }
-    if (is_all_caps) {
-      dense_features->push_back(1.0);
-    } else {
-      dense_features->push_back(-1.0);
-    }
-  }
-#endif
 
   return true;
 }
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/smartselect/token-feature-extractor.h b/token-feature-extractor.h
similarity index 81%
rename from smartselect/token-feature-extractor.h
rename to token-feature-extractor.h
index 5afeca4..9d476ba 100644
--- a/smartselect/token-feature-extractor.h
+++ b/token-feature-extractor.h
@@ -14,20 +14,18 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_SMARTSELECT_TOKEN_FEATURE_EXTRACTOR_H_
-#define LIBTEXTCLASSIFIER_SMARTSELECT_TOKEN_FEATURE_EXTRACTOR_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TOKEN_FEATURE_EXTRACTOR_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TOKEN_FEATURE_EXTRACTOR_H_
 
 #include <memory>
 #include <unordered_set>
 #include <vector>
 
-#include "smartselect/types.h"
+#include "types.h"
 #include "util/strings/stringpiece.h"
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-#include "unicode/regex.h"
-#endif
+#include "util/utf8/unilib.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 struct TokenFeatureExtractorOptions {
   // Number of buckets used for hashing charactergrams.
@@ -67,7 +65,8 @@
 
 class TokenFeatureExtractor {
  public:
-  explicit TokenFeatureExtractor(const TokenFeatureExtractorOptions& options);
+  TokenFeatureExtractor(const TokenFeatureExtractorOptions& options,
+                        const UniLib& unilib);
 
   // Extracts features from a token.
   //  - is_in_span is a bool indicator whether the token is a part of the
@@ -83,13 +82,7 @@
   int DenseFeaturesCount() const {
     int feature_count =
         options_.extract_case_feature + options_.extract_selection_mask_feature;
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
     feature_count += regex_patterns_.size();
-#else
-    if (enable_all_caps_feature_) {
-      feature_count += 1;
-    }
-#endif
     return feature_count;
   }
 
@@ -110,13 +103,10 @@
 
  private:
   TokenFeatureExtractorOptions options_;
-#ifndef LIBTEXTCLASSIFIER_DISABLE_ICU_SUPPORT
-  std::vector<std::unique_ptr<icu::RegexPattern>> regex_patterns_;
-#else
-  bool enable_all_caps_feature_ = false;
-#endif
+  std::vector<std::unique_ptr<UniLib::RegexPattern>> regex_patterns_;
+  const UniLib& unilib_;
 };
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_SMARTSELECT_TOKEN_FEATURE_EXTRACTOR_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TOKEN_FEATURE_EXTRACTOR_H_
diff --git a/smartselect/token-feature-extractor_test.cc b/token-feature-extractor_test.cc
similarity index 92%
rename from smartselect/token-feature-extractor_test.cc
rename to token-feature-extractor_test.cc
index 4b635fd..d6e48bb 100644
--- a/smartselect/token-feature-extractor_test.cc
+++ b/token-feature-extractor_test.cc
@@ -14,18 +14,18 @@
  * limitations under the License.
  */
 
-#include "smartselect/token-feature-extractor.h"
+#include "token-feature-extractor.h"
 
 #include "gmock/gmock.h"
 #include "gtest/gtest.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 namespace {
 
 class TestingTokenFeatureExtractor : public TokenFeatureExtractor {
  public:
-  using TokenFeatureExtractor::TokenFeatureExtractor;
   using TokenFeatureExtractor::HashToken;
+  using TokenFeatureExtractor::TokenFeatureExtractor;
 };
 
 TEST(TokenFeatureExtractorTest, ExtractAscii) {
@@ -35,7 +35,8 @@
   options.extract_case_feature = true;
   options.unicode_aware_features = false;
   options.extract_selection_mask_feature = true;
-  TestingTokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   std::vector<int> sparse_features;
   std::vector<float> dense_features;
@@ -105,7 +106,8 @@
   options.extract_case_feature = true;
   options.unicode_aware_features = false;
   options.extract_selection_mask_feature = true;
-  TestingTokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   std::vector<int> sparse_features;
   std::vector<float> dense_features;
@@ -134,7 +136,8 @@
   options.extract_case_feature = true;
   options.unicode_aware_features = true;
   options.extract_selection_mask_feature = true;
-  TestingTokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   std::vector<int> sparse_features;
   std::vector<float> dense_features;
@@ -204,7 +207,8 @@
   options.extract_case_feature = true;
   options.unicode_aware_features = true;
   options.extract_selection_mask_feature = true;
-  TestingTokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   std::vector<int> sparse_features;
   std::vector<float> dense_features;
@@ -227,6 +231,7 @@
   EXPECT_THAT(dense_features, testing::ElementsAreArray({-1.0, -1.0}));
 }
 
+#ifdef LIBTEXTCLASSIFIER_TEST_ICU
 TEST(TokenFeatureExtractorTest, ICUCaseFeature) {
   TokenFeatureExtractorOptions options;
   options.num_buckets = 1000;
@@ -234,7 +239,8 @@
   options.extract_case_feature = true;
   options.unicode_aware_features = true;
   options.extract_selection_mask_feature = false;
-  TokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   std::vector<int> sparse_features;
   std::vector<float> dense_features;
@@ -260,6 +266,7 @@
                     &dense_features);
   EXPECT_THAT(dense_features, testing::ElementsAreArray({-1.0}));
 }
+#endif
 
 TEST(TokenFeatureExtractorTest, DigitRemapping) {
   TokenFeatureExtractorOptions options;
@@ -267,7 +274,8 @@
   options.chargram_orders = std::vector<int>{1, 2};
   options.remap_digits = true;
   options.unicode_aware_features = false;
-  TokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   std::vector<int> sparse_features;
   std::vector<float> dense_features;
@@ -291,7 +299,8 @@
   options.chargram_orders = std::vector<int>{1, 2};
   options.remap_digits = true;
   options.unicode_aware_features = true;
-  TokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   std::vector<int> sparse_features;
   std::vector<float> dense_features;
@@ -315,7 +324,8 @@
   options.chargram_orders = std::vector<int>{1, 2};
   options.lowercase_tokens = true;
   options.unicode_aware_features = false;
-  TokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   std::vector<int> sparse_features;
   std::vector<float> dense_features;
@@ -332,13 +342,15 @@
   EXPECT_THAT(sparse_features, testing::ElementsAreArray(sparse_features2));
 }
 
+#ifdef LIBTEXTCLASSIFIER_TEST_ICU
 TEST(TokenFeatureExtractorTest, LowercaseUnicode) {
   TokenFeatureExtractorOptions options;
   options.num_buckets = 1000;
   options.chargram_orders = std::vector<int>{1, 2};
   options.lowercase_tokens = true;
   options.unicode_aware_features = true;
-  TokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   std::vector<int> sparse_features;
   std::vector<float> dense_features;
@@ -349,7 +361,9 @@
                     &dense_features);
   EXPECT_THAT(sparse_features, testing::ElementsAreArray(sparse_features2));
 }
+#endif
 
+#ifdef LIBTEXTCLASSIFIER_TEST_ICU
 TEST(TokenFeatureExtractorTest, RegexFeatures) {
   TokenFeatureExtractorOptions options;
   options.num_buckets = 1000;
@@ -358,7 +372,8 @@
   options.unicode_aware_features = false;
   options.regexp_features.push_back("^[a-z]+$");  // all lower case.
   options.regexp_features.push_back("^[0-9]+$");  // all digits.
-  TokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   std::vector<int> sparse_features;
   std::vector<float> dense_features;
@@ -381,6 +396,7 @@
                     &dense_features);
   EXPECT_THAT(dense_features, testing::ElementsAreArray({-1.0, 1.0}));
 }
+#endif
 
 TEST(TokenFeatureExtractorTest, ExtractTooLongWord) {
   TokenFeatureExtractorOptions options;
@@ -389,7 +405,8 @@
   options.extract_case_feature = true;
   options.unicode_aware_features = true;
   options.extract_selection_mask_feature = true;
-  TestingTokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   // Test that this runs. ASAN should catch problems.
   std::vector<int> sparse_features;
@@ -413,10 +430,12 @@
   options.extract_case_feature = true;
   options.unicode_aware_features = true;
   options.extract_selection_mask_feature = true;
-  TestingTokenFeatureExtractor extractor_unicode(options);
+
+  UniLib unilib;
+  TestingTokenFeatureExtractor extractor_unicode(options, unilib);
 
   options.unicode_aware_features = false;
-  TestingTokenFeatureExtractor extractor_ascii(options);
+  TestingTokenFeatureExtractor extractor_ascii(options, unilib);
 
   for (const std::string& input :
        {"https://www.abcdefgh.com/in/xxxkkkvayio",
@@ -447,7 +466,8 @@
   options.unicode_aware_features = false;
   options.extract_selection_mask_feature = true;
 
-  TestingTokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   std::vector<int> sparse_features;
   std::vector<float> dense_features;
@@ -473,7 +493,8 @@
   options.allowed_chargrams.insert("!");
   options.allowed_chargrams.insert("\xc4");  // UTF8 control character.
 
-  TestingTokenFeatureExtractor extractor(options);
+  const UniLib unilib;
+  TestingTokenFeatureExtractor extractor(options, unilib);
 
   std::vector<int> sparse_features;
   std::vector<float> dense_features;
@@ -540,4 +561,4 @@
 }
 
 }  // namespace
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/tokenizer.cc b/tokenizer.cc
new file mode 100644
index 0000000..456826d
--- /dev/null
+++ b/tokenizer.cc
@@ -0,0 +1,120 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "tokenizer.h"
+
+#include <algorithm>
+
+#include "util/base/logging.h"
+#include "util/strings/utf8.h"
+#include "util/utf8/unicodetext.h"
+
+namespace libtextclassifier2 {
+
+Tokenizer::Tokenizer(
+    const std::vector<const TokenizationCodepointRange*>& codepoint_ranges,
+    bool split_on_script_change)
+    : codepoint_ranges_(codepoint_ranges),
+      split_on_script_change_(split_on_script_change) {
+  std::sort(codepoint_ranges_.begin(), codepoint_ranges_.end(),
+            [](const TokenizationCodepointRange* a,
+               const TokenizationCodepointRange* b) {
+              return a->start() < b->start();
+            });
+}
+
+const TokenizationCodepointRange* Tokenizer::FindTokenizationRange(
+    int codepoint) const {
+  auto it = std::lower_bound(
+      codepoint_ranges_.begin(), codepoint_ranges_.end(), codepoint,
+      [](const TokenizationCodepointRange* range, int codepoint) {
+        // This function compares range with the codepoint for the purpose of
+        // finding the first greater or equal range. Because of the use of
+        // std::lower_bound it needs to return true when range < codepoint;
+        // the first time it will return false the lower bound is found and
+        // returned.
+        //
+        // It might seem weird that the condition is range.end <= codepoint
+        // here but when codepoint == range.end it means it's actually just
+        // outside of the range, thus the range is less than the codepoint.
+        return range->end() <= codepoint;
+      });
+  if (it != codepoint_ranges_.end() && (*it)->start() <= codepoint &&
+      (*it)->end() > codepoint) {
+    return *it;
+  } else {
+    return nullptr;
+  }
+}
+
+void Tokenizer::GetScriptAndRole(char32 codepoint,
+                                 TokenizationCodepointRange_::Role* role,
+                                 int* script) const {
+  const TokenizationCodepointRange* range = FindTokenizationRange(codepoint);
+  if (range) {
+    *role = range->role();
+    *script = range->script_id();
+  } else {
+    *role = TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+    *script = kUnknownScript;
+  }
+}
+
+std::vector<Token> Tokenizer::Tokenize(const std::string& utf8_text) const {
+  UnicodeText context_unicode = UTF8ToUnicodeText(utf8_text, /*do_copy=*/false);
+
+  std::vector<Token> result;
+  Token new_token("", 0, 0);
+  int codepoint_index = 0;
+
+  int last_script = kInvalidScript;
+  for (auto it = context_unicode.begin(); it != context_unicode.end();
+       ++it, ++codepoint_index) {
+    TokenizationCodepointRange_::Role role;
+    int script;
+    GetScriptAndRole(*it, &role, &script);
+
+    if (role & TokenizationCodepointRange_::Role_SPLIT_BEFORE ||
+        (split_on_script_change_ && last_script != kInvalidScript &&
+         last_script != script)) {
+      if (!new_token.value.empty()) {
+        result.push_back(new_token);
+      }
+      new_token = Token("", codepoint_index, codepoint_index);
+    }
+    if (!(role & TokenizationCodepointRange_::Role_DISCARD_CODEPOINT)) {
+      new_token.value += std::string(
+          it.utf8_data(),
+          it.utf8_data() + GetNumBytesForNonZeroUTF8Char(it.utf8_data()));
+      ++new_token.end;
+    }
+    if (role & TokenizationCodepointRange_::Role_SPLIT_AFTER) {
+      if (!new_token.value.empty()) {
+        result.push_back(new_token);
+      }
+      new_token = Token("", codepoint_index + 1, codepoint_index + 1);
+    }
+
+    last_script = script;
+  }
+  if (!new_token.value.empty()) {
+    result.push_back(new_token);
+  }
+
+  return result;
+}
+
+}  // namespace libtextclassifier2
diff --git a/tokenizer.h b/tokenizer.h
new file mode 100644
index 0000000..72a9fbd
--- /dev/null
+++ b/tokenizer.h
@@ -0,0 +1,66 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TOKENIZER_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TOKENIZER_H_
+
+#include <string>
+#include <vector>
+
+#include "model_generated.h"
+#include "types.h"
+#include "util/base/integral_types.h"
+
+namespace libtextclassifier2 {
+
+const int kInvalidScript = -1;
+const int kUnknownScript = -2;
+
+// Tokenizer splits the input string into a sequence of tokens, according to the
+// configuration.
+class Tokenizer {
+ public:
+  explicit Tokenizer(
+      const std::vector<const TokenizationCodepointRange*>& codepoint_ranges,
+      bool split_on_script_change);
+
+  // Tokenizes the input string using the selected tokenization method.
+  std::vector<Token> Tokenize(const std::string& utf8_text) const;
+
+ protected:
+  // Finds the tokenization codepoint range config for given codepoint.
+  // Internally uses binary search so should be O(log(# of codepoint_ranges)).
+  const TokenizationCodepointRange* FindTokenizationRange(int codepoint) const;
+
+  // Finds the role and script for given codepoint. If not found, DEFAULT_ROLE
+  // and kUnknownScript are assigned.
+  void GetScriptAndRole(char32 codepoint,
+                        TokenizationCodepointRange_::Role* role,
+                        int* script) const;
+
+ private:
+  // Codepoint ranges that determine how different codepoints are tokenized.
+  // The ranges must not overlap.
+  std::vector<const TokenizationCodepointRange*> codepoint_ranges_;
+
+  // If true, tokens will be additionally split when the codepoint's script_id
+  // changes.
+  bool split_on_script_change_;
+};
+
+}  // namespace libtextclassifier2
+
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TOKENIZER_H_
diff --git a/tokenizer_test.cc b/tokenizer_test.cc
new file mode 100644
index 0000000..d9a0dea
--- /dev/null
+++ b/tokenizer_test.cc
@@ -0,0 +1,334 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "tokenizer.h"
+
+#include <vector>
+
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+using testing::ElementsAreArray;
+
+class TestingTokenizer : public Tokenizer {
+ public:
+  explicit TestingTokenizer(
+      const std::vector<const TokenizationCodepointRange*>&
+          codepoint_range_configs,
+      bool split_on_script_change)
+      : Tokenizer(codepoint_range_configs, split_on_script_change) {}
+
+  using Tokenizer::FindTokenizationRange;
+};
+
+class TestingTokenizerProxy {
+ public:
+  explicit TestingTokenizerProxy(
+      const std::vector<TokenizationCodepointRangeT>& codepoint_range_configs,
+      bool split_on_script_change) {
+    int num_configs = codepoint_range_configs.size();
+    std::vector<const TokenizationCodepointRange*> configs_fb;
+    buffers_.reserve(num_configs);
+    for (int i = 0; i < num_configs; i++) {
+      flatbuffers::FlatBufferBuilder builder;
+      builder.Finish(CreateTokenizationCodepointRange(
+          builder, &codepoint_range_configs[i]));
+      buffers_.push_back(builder.Release());
+      configs_fb.push_back(
+          flatbuffers::GetRoot<TokenizationCodepointRange>(buffers_[i].data()));
+    }
+    tokenizer_ = std::unique_ptr<TestingTokenizer>(
+        new TestingTokenizer(configs_fb, split_on_script_change));
+  }
+
+  TokenizationCodepointRange_::Role TestFindTokenizationRole(int c) const {
+    const TokenizationCodepointRange* range =
+        tokenizer_->FindTokenizationRange(c);
+    if (range != nullptr) {
+      return range->role();
+    } else {
+      return TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+    }
+  }
+
+  std::vector<Token> Tokenize(const std::string& utf8_text) const {
+    return tokenizer_->Tokenize(utf8_text);
+  }
+
+ private:
+  std::vector<flatbuffers::DetachedBuffer> buffers_;
+  std::unique_ptr<TestingTokenizer> tokenizer_;
+};
+
+TEST(TokenizerTest, FindTokenizationRange) {
+  std::vector<TokenizationCodepointRangeT> configs;
+  TokenizationCodepointRangeT* config;
+
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0;
+  config->end = 10;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 32;
+  config->end = 33;
+  config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
+
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 1234;
+  config->end = 12345;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+
+  TestingTokenizerProxy tokenizer(configs, /*split_on_script_change=*/false);
+
+  // Test hits to the first group.
+  EXPECT_EQ(tokenizer.TestFindTokenizationRole(0),
+            TokenizationCodepointRange_::Role_TOKEN_SEPARATOR);
+  EXPECT_EQ(tokenizer.TestFindTokenizationRole(5),
+            TokenizationCodepointRange_::Role_TOKEN_SEPARATOR);
+  EXPECT_EQ(tokenizer.TestFindTokenizationRole(10),
+            TokenizationCodepointRange_::Role_DEFAULT_ROLE);
+
+  // Test a hit to the second group.
+  EXPECT_EQ(tokenizer.TestFindTokenizationRole(31),
+            TokenizationCodepointRange_::Role_DEFAULT_ROLE);
+  EXPECT_EQ(tokenizer.TestFindTokenizationRole(32),
+            TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR);
+  EXPECT_EQ(tokenizer.TestFindTokenizationRole(33),
+            TokenizationCodepointRange_::Role_DEFAULT_ROLE);
+
+  // Test hits to the third group.
+  EXPECT_EQ(tokenizer.TestFindTokenizationRole(1233),
+            TokenizationCodepointRange_::Role_DEFAULT_ROLE);
+  EXPECT_EQ(tokenizer.TestFindTokenizationRole(1234),
+            TokenizationCodepointRange_::Role_TOKEN_SEPARATOR);
+  EXPECT_EQ(tokenizer.TestFindTokenizationRole(12344),
+            TokenizationCodepointRange_::Role_TOKEN_SEPARATOR);
+  EXPECT_EQ(tokenizer.TestFindTokenizationRole(12345),
+            TokenizationCodepointRange_::Role_DEFAULT_ROLE);
+
+  // Test a hit outside.
+  EXPECT_EQ(tokenizer.TestFindTokenizationRole(99),
+            TokenizationCodepointRange_::Role_DEFAULT_ROLE);
+}
+
+TEST(TokenizerTest, TokenizeOnSpace) {
+  std::vector<TokenizationCodepointRangeT> configs;
+  TokenizationCodepointRangeT* config;
+
+  configs.emplace_back();
+  config = &configs.back();
+  // Space character.
+  config->start = 32;
+  config->end = 33;
+  config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
+
+  TestingTokenizerProxy tokenizer(configs, /*split_on_script_change=*/false);
+  std::vector<Token> tokens = tokenizer.Tokenize("Hello world!");
+
+  EXPECT_THAT(tokens,
+              ElementsAreArray({Token("Hello", 0, 5), Token("world!", 6, 12)}));
+}
+
+TEST(TokenizerTest, TokenizeOnSpaceAndScriptChange) {
+  std::vector<TokenizationCodepointRangeT> configs;
+  TokenizationCodepointRangeT* config;
+
+  // Latin.
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0;
+  config->end = 32;
+  config->role = TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+  config->script_id = 1;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 32;
+  config->end = 33;
+  config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
+  config->script_id = 1;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 33;
+  config->end = 0x77F + 1;
+  config->role = TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+  config->script_id = 1;
+
+  TestingTokenizerProxy tokenizer(configs, /*split_on_script_change=*/true);
+  EXPECT_THAT(tokenizer.Tokenize("앨라배마 주 전화(123) 456-789웹사이트"),
+              std::vector<Token>({Token("앨라배마", 0, 4), Token("주", 5, 6),
+                                  Token("전화", 7, 10), Token("(123)", 10, 15),
+                                  Token("456-789", 16, 23),
+                                  Token("웹사이트", 23, 28)}));
+}  // namespace
+
+TEST(TokenizerTest, TokenizeComplex) {
+  std::vector<TokenizationCodepointRangeT> configs;
+  TokenizationCodepointRangeT* config;
+
+  // Source: http://www.unicode.org/Public/10.0.0/ucd/Blocks-10.0.0d1.txt
+  // Latin - cyrilic.
+  //   0000..007F; Basic Latin
+  //   0080..00FF; Latin-1 Supplement
+  //   0100..017F; Latin Extended-A
+  //   0180..024F; Latin Extended-B
+  //   0250..02AF; IPA Extensions
+  //   02B0..02FF; Spacing Modifier Letters
+  //   0300..036F; Combining Diacritical Marks
+  //   0370..03FF; Greek and Coptic
+  //   0400..04FF; Cyrillic
+  //   0500..052F; Cyrillic Supplement
+  //   0530..058F; Armenian
+  //   0590..05FF; Hebrew
+  //   0600..06FF; Arabic
+  //   0700..074F; Syriac
+  //   0750..077F; Arabic Supplement
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0;
+  config->end = 32;
+  config->role = TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 32;
+  config->end = 33;
+  config->role = TokenizationCodepointRange_::Role_WHITESPACE_SEPARATOR;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 33;
+  config->end = 0x77F + 1;
+  config->role = TokenizationCodepointRange_::Role_DEFAULT_ROLE;
+
+  // CJK
+  // 2E80..2EFF; CJK Radicals Supplement
+  // 3000..303F; CJK Symbols and Punctuation
+  // 3040..309F; Hiragana
+  // 30A0..30FF; Katakana
+  // 3100..312F; Bopomofo
+  // 3130..318F; Hangul Compatibility Jamo
+  // 3190..319F; Kanbun
+  // 31A0..31BF; Bopomofo Extended
+  // 31C0..31EF; CJK Strokes
+  // 31F0..31FF; Katakana Phonetic Extensions
+  // 3200..32FF; Enclosed CJK Letters and Months
+  // 3300..33FF; CJK Compatibility
+  // 3400..4DBF; CJK Unified Ideographs Extension A
+  // 4DC0..4DFF; Yijing Hexagram Symbols
+  // 4E00..9FFF; CJK Unified Ideographs
+  // A000..A48F; Yi Syllables
+  // A490..A4CF; Yi Radicals
+  // A4D0..A4FF; Lisu
+  // A500..A63F; Vai
+  // F900..FAFF; CJK Compatibility Ideographs
+  // FE30..FE4F; CJK Compatibility Forms
+  // 20000..2A6DF; CJK Unified Ideographs Extension B
+  // 2A700..2B73F; CJK Unified Ideographs Extension C
+  // 2B740..2B81F; CJK Unified Ideographs Extension D
+  // 2B820..2CEAF; CJK Unified Ideographs Extension E
+  // 2CEB0..2EBEF; CJK Unified Ideographs Extension F
+  // 2F800..2FA1F; CJK Compatibility Ideographs Supplement
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0x2E80;
+  config->end = 0x2EFF + 1;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0x3000;
+  config->end = 0xA63F + 1;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0xF900;
+  config->end = 0xFAFF + 1;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0xFE30;
+  config->end = 0xFE4F + 1;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0x20000;
+  config->end = 0x2A6DF + 1;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0x2A700;
+  config->end = 0x2B73F + 1;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0x2B740;
+  config->end = 0x2B81F + 1;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0x2B820;
+  config->end = 0x2CEAF + 1;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0x2CEB0;
+  config->end = 0x2EBEF + 1;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0x2F800;
+  config->end = 0x2FA1F + 1;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+
+  // Thai.
+  // 0E00..0E7F; Thai
+  configs.emplace_back();
+  config = &configs.back();
+  config->start = 0x0E00;
+  config->end = 0x0E7F + 1;
+  config->role = TokenizationCodepointRange_::Role_TOKEN_SEPARATOR;
+
+  TestingTokenizerProxy tokenizer(configs, /*split_on_script_change=*/false);
+  std::vector<Token> tokens;
+
+  tokens = tokenizer.Tokenize(
+      "問少目木輸走猶術権自京門録球変。細開括省用掲情結傍走愛明氷。");
+  EXPECT_EQ(tokens.size(), 30);
+
+  tokens = tokenizer.Tokenize("問少目 hello 木輸ยามきゃ");
+  // clang-format off
+  EXPECT_THAT(
+      tokens,
+      ElementsAreArray({Token("問", 0, 1),
+                        Token("少", 1, 2),
+                        Token("目", 2, 3),
+                        Token("hello", 4, 9),
+                        Token("木", 10, 11),
+                        Token("輸", 11, 12),
+                        Token("ย", 12, 13),
+                        Token("า", 13, 14),
+                        Token("ม", 14, 15),
+                        Token("き", 15, 16),
+                        Token("ゃ", 16, 17)}));
+  // clang-format on
+}
+
+}  // namespace
+}  // namespace libtextclassifier2
diff --git a/types.h b/types.h
new file mode 100644
index 0000000..d50d438
--- /dev/null
+++ b/types.h
@@ -0,0 +1,164 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TYPES_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TYPES_H_
+
+#include <algorithm>
+#include <functional>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+
+constexpr int kInvalidIndex = -1;
+
+// Index for a 0-based array of tokens.
+using TokenIndex = int;
+
+// Index for a 0-based array of codepoints.
+using CodepointIndex = int;
+
+// Marks a span in a sequence of codepoints. The first element is the index of
+// the first codepoint of the span, and the second element is the index of the
+// codepoint one past the end of the span.
+// TODO(b/71982294): Make it a struct.
+using CodepointSpan = std::pair<CodepointIndex, CodepointIndex>;
+
+// Marks a span in a sequence of tokens. The first element is the index of the
+// first token in the span, and the second element is the index of the token one
+// past the end of the span.
+// TODO(b/71982294): Make it a struct.
+using TokenSpan = std::pair<TokenIndex, TokenIndex>;
+
+// Returns the size of the token span. Assumes that the span is valid.
+inline int TokenSpanSize(const TokenSpan& token_span) {
+  return token_span.second - token_span.first;
+}
+
+// Returns a token span consisting of one token.
+inline TokenSpan SingleTokenSpan(int token_index) {
+  return {token_index, token_index + 1};
+}
+
+// Returns an intersection of two token spans. Assumes that both spans are valid
+// and overlapping.
+inline TokenSpan IntersectTokenSpans(const TokenSpan& token_span1,
+                                     const TokenSpan& token_span2) {
+  return {std::max(token_span1.first, token_span2.first),
+          std::min(token_span1.second, token_span2.second)};
+}
+
+// Returns and expanded token span by adding a certain number of tokens on its
+// left and on its right.
+inline TokenSpan ExpandTokenSpan(const TokenSpan& token_span,
+                                 int num_tokens_left, int num_tokens_right) {
+  return {token_span.first - num_tokens_left,
+          token_span.second + num_tokens_right};
+}
+
+// Token holds a token, its position in the original string and whether it was
+// part of the input span.
+struct Token {
+  std::string value;
+  CodepointIndex start;
+  CodepointIndex end;
+
+  // Whether the token is a padding token.
+  bool is_padding;
+
+  // Default constructor constructs the padding-token.
+  Token()
+      : value(""), start(kInvalidIndex), end(kInvalidIndex), is_padding(true) {}
+
+  Token(const std::string& arg_value, CodepointIndex arg_start,
+        CodepointIndex arg_end)
+      : value(arg_value), start(arg_start), end(arg_end), is_padding(false) {}
+
+  bool operator==(const Token& other) const {
+    return value == other.value && start == other.start && end == other.end &&
+           is_padding == other.is_padding;
+  }
+
+  bool IsContainedInSpan(CodepointSpan span) const {
+    return start >= span.first && end <= span.second;
+  }
+};
+
+// Pretty-printing function for Token.
+inline logging::LoggingStringStream& operator<<(
+    logging::LoggingStringStream& stream, const Token& token) {
+  if (!token.is_padding) {
+    return stream << "Token(\"" << token.value << "\", " << token.start << ", "
+                  << token.end << ")";
+  } else {
+    return stream << "Token()";
+  }
+}
+
+// Represents a result of Annotate call.
+struct AnnotatedSpan {
+  // Unicode codepoint indices in the input string.
+  CodepointSpan span = {kInvalidIndex, kInvalidIndex};
+
+  // Classification result for the span.
+  std::vector<std::pair<std::string, float>> classification;
+};
+
+// Pretty-printing function for AnnotatedSpan.
+inline logging::LoggingStringStream& operator<<(
+    logging::LoggingStringStream& stream, const AnnotatedSpan& span) {
+  std::string best_class;
+  float best_score = -1;
+  if (!span.classification.empty()) {
+    best_class = span.classification[0].first;
+    best_score = span.classification[0].second;
+  }
+  return stream << "Span(" << span.span.first << ", " << span.span.second
+                << ", " << best_class << ", " << best_score << ")";
+}
+
+// StringPiece analogue for std::vector<T>.
+template <class T>
+class VectorSpan {
+ public:
+  VectorSpan() : begin_(), end_() {}
+  VectorSpan(const std::vector<T>& v)  // NOLINT(runtime/explicit)
+      : begin_(v.begin()), end_(v.end()) {}
+  VectorSpan(typename std::vector<T>::const_iterator begin,
+             typename std::vector<T>::const_iterator end)
+      : begin_(begin), end_(end) {}
+
+  const T& operator[](typename std::vector<T>::size_type i) const {
+    return *(begin_ + i);
+  }
+
+  int size() const { return end_ - begin_; }
+  typename std::vector<T>::const_iterator begin() const { return begin_; }
+  typename std::vector<T>::const_iterator end() const { return end_; }
+  const float* data() const { return &(*begin_); }
+
+ private:
+  typename std::vector<T>::const_iterator begin_;
+  typename std::vector<T>::const_iterator end_;
+};
+
+}  // namespace libtextclassifier2
+
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_TYPES_H_
diff --git a/util/base/casts.h b/util/base/casts.h
index 805ee89..c33173a 100644
--- a/util/base/casts.h
+++ b/util/base/casts.h
@@ -14,12 +14,12 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_BASE_CASTS_H_
-#define LIBTEXTCLASSIFIER_UTIL_BASE_CASTS_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_CASTS_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_CASTS_H_
 
 #include <string.h>  // for memcpy
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 // bit_cast<Dest, Source> is a template function that implements the equivalent
 // of "*reinterpret_cast<Dest*>(&source)".  We need this in very low-level
@@ -87,6 +87,6 @@
   return dest;
 }
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_BASE_CASTS_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_CASTS_H_
diff --git a/util/base/config.h b/util/base/config.h
index e6c19a4..41b99a9 100644
--- a/util/base/config.h
+++ b/util/base/config.h
@@ -16,10 +16,10 @@
 
 // Define macros to indicate C++ standard / platform / etc we use.
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_BASE_CONFIG_H_
-#define LIBTEXTCLASSIFIER_UTIL_BASE_CONFIG_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_CONFIG_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_CONFIG_H_
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 // Define LANG_CXX11 to 1 if current compiler supports C++11.
 //
@@ -38,6 +38,6 @@
 #define LANG_CXX11 1
 #endif
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_BASE_CONFIG_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_CONFIG_H_
diff --git a/util/base/endian.h b/util/base/endian.h
index 75f8bf7..2a6e654 100644
--- a/util/base/endian.h
+++ b/util/base/endian.h
@@ -14,12 +14,12 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_BASE_ENDIAN_H_
-#define LIBTEXTCLASSIFIER_UTIL_BASE_ENDIAN_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_ENDIAN_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_ENDIAN_H_
 
 #include "util/base/integral_types.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 #if defined OS_LINUX || defined OS_CYGWIN || defined OS_ANDROID || \
     defined(__ANDROID__)
@@ -40,7 +40,7 @@
 
 // The following guarantees declaration of the byte swap functions, and
 // defines __BYTE_ORDER for MSVC
-#if defined(__GLIBC__) || defined(__BIONIC__) || defined(__CYGWIN__)
+#if defined(__GLIBC__) || defined(__CYGWIN__)
 #include <byteswap.h>  // IWYU pragma: export
 // The following section defines the byte swap functions for OS X / iOS,
 // which does not ship with byteswap.h.
@@ -133,6 +133,6 @@
 #endif /* ENDIAN */
 };
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_BASE_ENDIAN_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_ENDIAN_H_
diff --git a/util/base/integral_types.h b/util/base/integral_types.h
index 0322d33..a599f3c 100644
--- a/util/base/integral_types.h
+++ b/util/base/integral_types.h
@@ -16,12 +16,12 @@
 
 // Basic integer type definitions.
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_BASE_INTEGRAL_TYPES_H_
-#define LIBTEXTCLASSIFIER_UTIL_BASE_INTEGRAL_TYPES_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_INTEGRAL_TYPES_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_INTEGRAL_TYPES_H_
 
 #include "util/base/config.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 typedef unsigned int uint32;
 typedef unsigned long long uint64;
@@ -56,6 +56,6 @@
 static_assert(sizeof(int64) == 8, "wrong size");
 #endif  // LANG_CXX11
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_BASE_INTEGRAL_TYPES_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_INTEGRAL_TYPES_H_
diff --git a/util/base/logging.cc b/util/base/logging.cc
index 9de35ca..919bb36 100644
--- a/util/base/logging.cc
+++ b/util/base/logging.cc
@@ -22,7 +22,7 @@
 
 #include "util/base/logging_raw.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 namespace logging {
 
 namespace {
@@ -57,12 +57,11 @@
 }
 
 LogMessage::~LogMessage() {
-  const std::string message = stream_.str();
-  LowLevelLogging(severity_, /* tag = */ "txtClsf", message);
+  LowLevelLogging(severity_, /* tag = */ "txtClsf", stream_.message);
   if (severity_ == FATAL) {
     exit(1);
   }
 }
 
 }  // namespace logging
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/util/base/logging.h b/util/base/logging.h
index dba0ed4..cebbbf2 100644
--- a/util/base/logging.h
+++ b/util/base/logging.h
@@ -14,18 +14,17 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_H_
-#define LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_LOGGING_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_LOGGING_H_
 
 #include <cassert>
-#include <sstream>
 #include <string>
 
 #include "util/base/logging_levels.h"
 #include "util/base/port.h"
 
 // TC_STRIP
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 // string class that can't be instantiated.  Makes sure that the code does not
 // compile when non std::string is used.
 //
@@ -38,12 +37,49 @@
   // Makes the class non-instantiable.
   virtual ~string() = 0;
 };
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 // TC_END_STRIP
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 namespace logging {
 
+// A tiny code footprint string stream for assembling log messages.
+struct LoggingStringStream {
+  LoggingStringStream() {}
+  LoggingStringStream &stream() { return *this; }
+  // Needed for invocation in TC_CHECK macro.
+  explicit operator bool() const { return true; }
+
+  std::string message;
+};
+
+template <typename T>
+inline LoggingStringStream &operator<<(LoggingStringStream &stream,
+                                       const T &entry) {
+  stream.message.append(std::to_string(entry));
+  return stream;
+}
+
+inline LoggingStringStream &operator<<(LoggingStringStream &stream,
+                                       const char *message) {
+  stream.message.append(message);
+  return stream;
+}
+
+#if defined(HAS_GLOBAL_STRING)
+inline LoggingStringStream &operator<<(LoggingStringStream &stream,
+                                       const ::string &message) {
+  stream.message.append(message);
+  return stream;
+}
+#endif
+
+inline LoggingStringStream &operator<<(LoggingStringStream &stream,
+                                       const std::string &message) {
+  stream.message.append(message);
+  return stream;
+}
+
 // The class that does all the work behind our TC_LOG(severity) macros.  Each
 // TC_LOG(severity) << obj1 << obj2 << ...; logging statement creates a
 // LogMessage temporary object containing a stringstream.  Each operator<< adds
@@ -61,19 +97,34 @@
   ~LogMessage() TC_ATTRIBUTE_NOINLINE;
 
   // Returns the stream associated with the logger object.
-  std::stringstream &stream() { return stream_; }
+  LoggingStringStream &stream() { return stream_; }
 
  private:
   const LogSeverity severity_;
 
   // Stream that "prints" all info into a string (not to a file).  We construct
   // here the entire logging message and next print it in one operation.
-  std::stringstream stream_;
+  LoggingStringStream stream_;
 };
 
-#define TC_LOG(severity)                                          \
-  ::libtextclassifier::logging::LogMessage(                       \
-      ::libtextclassifier::logging::severity, __FILE__, __LINE__) \
+// Pseudo-stream that "eats" the tokens <<-pumped into it, without printing
+// anything.
+class NullStream {
+ public:
+  NullStream() {}
+  NullStream &stream() { return *this; }
+};
+template <typename T>
+inline NullStream &operator<<(NullStream &str, const T &) {
+  return str;
+}
+
+}  // namespace logging
+}  // namespace libtextclassifier2
+
+#define TC_LOG(severity)                                           \
+  ::libtextclassifier2::logging::LogMessage(                       \
+      ::libtextclassifier2::logging::severity, __FILE__, __LINE__) \
       .stream()
 
 // If condition x is true, does nothing.  Otherwise, crashes the program (liek
@@ -92,19 +143,7 @@
 #define TC_CHECK_GE(x, y) TC_CHECK((x) >= (y))
 #define TC_CHECK_NE(x, y) TC_CHECK((x) != (y))
 
-// Pseudo-stream that "eats" the tokens <<-pumped into it, without printing
-// anything.
-class NullStream {
- public:
-  NullStream() {}
-  NullStream &stream() { return *this; }
-};
-template <typename T>
-inline NullStream &operator<<(NullStream &str, const T &) {
-  return str;
-}
-
-#define TC_NULLSTREAM ::libtextclassifier::logging::NullStream().stream()
+#define TC_NULLSTREAM ::libtextclassifier2::logging::NullStream().stream()
 
 // Debug checks: a TC_DCHECK<suffix> macro should behave like TC_CHECK<suffix>
 // in debug mode an don't check / don't print anything in non-debug mode.
@@ -133,15 +172,12 @@
 #endif  // NDEBUG
 
 #ifdef LIBTEXTCLASSIFIER_VLOG
-#define TC_VLOG(severity)                                                      \
-  ::libtextclassifier::logging::LogMessage(::libtextclassifier::logging::INFO, \
-                                           __FILE__, __LINE__)                 \
+#define TC_VLOG(severity)                                      \
+  ::libtextclassifier2::logging::LogMessage(                   \
+      ::libtextclassifier2::logging::INFO, __FILE__, __LINE__) \
       .stream()
 #else
 #define TC_VLOG(severity) TC_NULLSTREAM
 #endif
 
-}  // namespace logging
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_LOGGING_H_
diff --git a/util/base/logging_levels.h b/util/base/logging_levels.h
index d16f96a..7d7dff2 100644
--- a/util/base/logging_levels.h
+++ b/util/base/logging_levels.h
@@ -14,10 +14,10 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_LEVELS_H_
-#define LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_LEVELS_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_LOGGING_LEVELS_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_LOGGING_LEVELS_H_
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 namespace logging {
 
 enum LogSeverity {
@@ -28,6 +28,6 @@
 };
 
 }  // namespace logging
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_LEVELS_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_LOGGING_LEVELS_H_
diff --git a/util/base/logging_raw.cc b/util/base/logging_raw.cc
index 8e0eb1b..6d97852 100644
--- a/util/base/logging_raw.cc
+++ b/util/base/logging_raw.cc
@@ -26,7 +26,7 @@
 // Compiled as part of Android.
 #include <android/log.h>
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 namespace logging {
 
 namespace {
@@ -60,12 +60,12 @@
 }
 
 }  // namespace logging
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
 #else  // if defined(__ANDROID__)
 
 // Not on Android: implement LowLevelLogging to print to stderr (see below).
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 namespace logging {
 
 namespace {
@@ -94,6 +94,6 @@
 }
 
 }  // namespace logging
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
 #endif  // if defined(__ANDROID__)
diff --git a/util/base/logging_raw.h b/util/base/logging_raw.h
index 40c2497..6cae105 100644
--- a/util/base/logging_raw.h
+++ b/util/base/logging_raw.h
@@ -14,14 +14,14 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_RAW_H_
-#define LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_RAW_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_LOGGING_RAW_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_LOGGING_RAW_H_
 
 #include <string>
 
 #include "util/base/logging_levels.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 namespace logging {
 
 // Low-level logging primitive.  Logs a message, with the indicated log
@@ -31,6 +31,6 @@
                      const std::string &message);
 
 }  // namespace logging
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_BASE_LOGGING_RAW_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_LOGGING_RAW_H_
diff --git a/util/base/macros.h b/util/base/macros.h
index aec3a8a..7aca681 100644
--- a/util/base/macros.h
+++ b/util/base/macros.h
@@ -14,12 +14,12 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_BASE_MACROS_H_
-#define LIBTEXTCLASSIFIER_UTIL_BASE_MACROS_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_MACROS_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_MACROS_H_
 
 #include "util/base/config.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 #if LANG_CXX11
 #define TC_DISALLOW_COPY_AND_ASSIGN(TypeName) \
@@ -78,6 +78,6 @@
 #define TC_FALLTHROUGH_INTENDED do { } while (0)
 #endif
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_BASE_MACROS_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_MACROS_H_
diff --git a/util/base/port.h b/util/base/port.h
index 394aaab..5a68daa 100644
--- a/util/base/port.h
+++ b/util/base/port.h
@@ -16,10 +16,10 @@
 
 // Various portability macros, type definitions, and inline functions.
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_BASE_PORT_H_
-#define LIBTEXTCLASSIFIER_UTIL_BASE_PORT_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_PORT_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_PORT_H_
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 #if defined(__GNUC__) && \
     (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 1))
@@ -40,6 +40,6 @@
 #define TC_ATTRIBUTE_NOINLINE
 #endif
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_BASE_PORT_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_BASE_PORT_H_
diff --git a/util/gtl/map_util.h b/util/gtl/map_util.h
index b5eaafa..d14071e 100644
--- a/util/gtl/map_util.h
+++ b/util/gtl/map_util.h
@@ -14,10 +14,10 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_GTL_MAP_UTIL_H_
-#define LIBTEXTCLASSIFIER_UTIL_GTL_MAP_UTIL_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_GTL_MAP_UTIL_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_GTL_MAP_UTIL_H_
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 // Returns a const reference to the value associated with the given key if it
 // exists, otherwise returns a const reference to the provided default value.
@@ -60,6 +60,6 @@
                             typename Collection::value_type(key, value));
 }
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_GTL_MAP_UTIL_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_GTL_MAP_UTIL_H_
diff --git a/util/gtl/stl_util.h b/util/gtl/stl_util.h
index 8e1c452..9d93c03 100644
--- a/util/gtl/stl_util.h
+++ b/util/gtl/stl_util.h
@@ -14,10 +14,10 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_GTL_STL_UTIL_H_
-#define LIBTEXTCLASSIFIER_UTIL_GTL_STL_UTIL_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_GTL_STL_UTIL_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_GTL_STL_UTIL_H_
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 // Deletes all the elements in an STL container and clears the container. This
 // function is suitable for use with a vector, set, hash_set, or any other STL
@@ -50,6 +50,6 @@
   container->clear();
 }
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_GTL_STL_UTIL_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_GTL_STL_UTIL_H_
diff --git a/util/hash/farmhash.h b/util/hash/farmhash.h
index 7adf3aa..3bbe294 100644
--- a/util/hash/farmhash.h
+++ b/util/hash/farmhash.h
@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_HASH_FARMHASH_H_
-#define LIBTEXTCLASSIFIER_UTIL_HASH_FARMHASH_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_HASH_FARMHASH_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_HASH_FARMHASH_H_
 
 #include <assert.h>
 #include <stdint.h>
@@ -24,7 +24,7 @@
 #include <utility>
 
 #ifndef NAMESPACE_FOR_HASH_FUNCTIONS
-#define NAMESPACE_FOR_HASH_FUNCTIONS tcfarmhash
+#define NAMESPACE_FOR_HASH_FUNCTIONS tc2farmhash
 #endif
 
 namespace NAMESPACE_FOR_HASH_FUNCTIONS {
@@ -261,4 +261,4 @@
 
 }  // namespace NAMESPACE_FOR_HASH_FUNCTIONS
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_HASH_FARMHASH_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_HASH_FARMHASH_H_
diff --git a/util/hash/hash.cc b/util/hash/hash.cc
index 1261417..9722ddc 100644
--- a/util/hash/hash.cc
+++ b/util/hash/hash.cc
@@ -18,7 +18,7 @@
 
 #include "util/base/macros.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 namespace {
 // Lower-level versions of Get... that read directly from a character buffer
@@ -76,4 +76,4 @@
   return h;
 }
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/util/hash/hash.h b/util/hash/hash.h
index 0abb72b..beabd6e 100644
--- a/util/hash/hash.h
+++ b/util/hash/hash.h
@@ -14,14 +14,14 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_HASH_HASH_H_
-#define LIBTEXTCLASSIFIER_UTIL_HASH_HASH_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_HASH_HASH_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_HASH_HASH_H_
 
 #include <string>
 
 #include "util/base/integral_types.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 uint32 Hash32(const char *data, size_t n, uint32 seed);
 
@@ -33,6 +33,6 @@
   return Hash32WithDefaultSeed(input.data(), input.size());
 }
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_HASH_HASH_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_HASH_HASH_H_
diff --git a/util/java/scoped_local_ref.h b/util/java/scoped_local_ref.h
index d995468..e716df5 100644
--- a/util/java/scoped_local_ref.h
+++ b/util/java/scoped_local_ref.h
@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_JAVA_SCOPED_LOCAL_REF_H_
-#define LIBTEXTCLASSIFIER_UTIL_JAVA_SCOPED_LOCAL_REF_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_JAVA_SCOPED_LOCAL_REF_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_JAVA_SCOPED_LOCAL_REF_H_
 
 #include <jni.h>
 #include <memory>
@@ -23,7 +23,7 @@
 
 #include "util/base/logging.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 // A deleter to be used with std::unique_ptr to delete JNI local references.
 class LocalRefDeleter {
@@ -60,6 +60,6 @@
 using ScopedLocalRef =
     std::unique_ptr<typename std::remove_pointer<T>::type, LocalRefDeleter>;
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_JAVA_SCOPED_LOCAL_REF_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_JAVA_SCOPED_LOCAL_REF_H_
diff --git a/common/fastexp.cc b/util/math/fastexp.cc
similarity index 93%
rename from common/fastexp.cc
rename to util/math/fastexp.cc
index 0376ad2..4bf8592 100644
--- a/common/fastexp.cc
+++ b/util/math/fastexp.cc
@@ -14,10 +14,9 @@
  * limitations under the License.
  */
 
-#include "common/fastexp.h"
+#include "util/math/fastexp.h"
 
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
 
 const int FastMathClass::kBits;
 const int FastMathClass::kMask1;
@@ -46,5 +45,4 @@
      7940441, 8029106, 8118253, 8207884, 8298001}
 };
 
-}  // namespace nlp_core
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/common/fastexp.h b/util/math/fastexp.h
similarity index 85%
rename from common/fastexp.h
rename to util/math/fastexp.h
index 1781b36..acc1453 100644
--- a/common/fastexp.h
+++ b/util/math/fastexp.h
@@ -16,8 +16,8 @@
 
 // Fast approximation for exp.
 
-#ifndef LIBTEXTCLASSIFIER_COMMON_FASTEXP_H_
-#define LIBTEXTCLASSIFIER_COMMON_FASTEXP_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_MATH_FASTEXP_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_MATH_FASTEXP_H_
 
 #include <cassert>
 #include <cmath>
@@ -27,8 +27,7 @@
 #include "util/base/integral_types.h"
 #include "util/base/logging.h"
 
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
 
 class FastMathClass {
  private:
@@ -64,7 +63,6 @@
 inline float VeryFastExp2(float f) { return FastMathInstance.VeryFastExp2(f); }
 inline float VeryFastExp(float f) { return FastMathInstance.VeryFastExp(f); }
 
-}  // namespace nlp_core
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_COMMON_FASTEXP_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_MATH_FASTEXP_H_
diff --git a/common/softmax.cc b/util/math/softmax.cc
similarity index 85%
rename from common/softmax.cc
rename to util/math/softmax.cc
index 3610de8..986787f 100644
--- a/common/softmax.cc
+++ b/util/math/softmax.cc
@@ -14,15 +14,14 @@
  * limitations under the License.
  */
 
-#include "common/softmax.h"
+#include "util/math/softmax.h"
 
 #include <limits>
 
-#include "common/fastexp.h"
 #include "util/base/logging.h"
+#include "util/math/fastexp.h"
 
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
 
 float ComputeSoftmaxProbability(const std::vector<float> &scores, int label) {
   if ((label < 0) || (label >= scores.size())) {
@@ -71,18 +70,24 @@
 }
 
 std::vector<float> ComputeSoftmax(const std::vector<float> &scores) {
+  return ComputeSoftmax(scores.data(), scores.size());
+}
+
+std::vector<float> ComputeSoftmax(const float *scores, int scores_size) {
   std::vector<float> softmax;
   std::vector<float> exp_scores;
-  exp_scores.reserve(scores.size());
-  softmax.reserve(scores.size());
+  exp_scores.reserve(scores_size);
+  softmax.reserve(scores_size);
 
   // Find max value in "scores" vector and rescale to avoid overflows.
   float max = std::numeric_limits<float>::min();
-  for (const auto &score : scores) {
+  for (int i = 0; i < scores_size; ++i) {
+    const float score = scores[i];
     if (score > max) max = score;
   }
   float denominator = 0;
-  for (auto &score : scores) {
+  for (int i = 0; i < scores_size; ++i) {
+    const float score = scores[i];
     // See comments above in ComputeSoftmaxProbability for the reasoning behind
     // this approximation.
     const float exp_score = score - max < -16.0f ? 0 : VeryFastExp(score - max);
@@ -90,11 +95,10 @@
     denominator += exp_score;
   }
 
-  for (int i = 0; i < scores.size(); ++i) {
+  for (int i = 0; i < scores_size; ++i) {
     softmax.push_back(exp_scores[i] / denominator);
   }
   return softmax;
 }
 
-}  // namespace nlp_core
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/common/softmax.h b/util/math/softmax.h
similarity index 71%
rename from common/softmax.h
rename to util/math/softmax.h
index e1cc2d9..57bf832 100644
--- a/common/softmax.h
+++ b/util/math/softmax.h
@@ -14,13 +14,12 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_COMMON_SOFTMAX_H_
-#define LIBTEXTCLASSIFIER_COMMON_SOFTMAX_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_MATH_SOFTMAX_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_MATH_SOFTMAX_H_
 
 #include <vector>
 
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
 
 // Computes probability of a softmax label.  Parameter "scores" is the vector of
 // softmax logits.  Returns 0.0f if "label" is outside the range [0,
@@ -31,7 +30,9 @@
 // "scores" is the vector of softmax logits.
 std::vector<float> ComputeSoftmax(const std::vector<float> &scores);
 
-}  // namespace nlp_core
-}  // namespace libtextclassifier
+// Same as above but operates on an array of floats.
+std::vector<float> ComputeSoftmax(const float *scores, int scores_size);
 
-#endif  // LIBTEXTCLASSIFIER_COMMON_SOFTMAX_H_
+}  // namespace libtextclassifier2
+
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_MATH_SOFTMAX_H_
diff --git a/common/mmap.cc b/util/memory/mmap.cc
similarity index 96%
rename from common/mmap.cc
rename to util/memory/mmap.cc
index 6e15a84..6b0bdf2 100644
--- a/common/mmap.cc
+++ b/util/memory/mmap.cc
@@ -14,7 +14,7 @@
  * limitations under the License.
  */
 
-#include "common/mmap.h"
+#include "util/memory/mmap.h"
 
 #include <errno.h>
 #include <fcntl.h>
@@ -27,8 +27,7 @@
 #include "util/base/logging.h"
 #include "util/base/macros.h"
 
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
 
 namespace {
 inline std::string GetLastSystemError() { return std::string(strerror(errno)); }
@@ -133,5 +132,4 @@
   return true;
 }
 
-}  // namespace nlp_core
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/common/mmap.h b/util/memory/mmap.h
similarity index 93%
rename from common/mmap.h
rename to util/memory/mmap.h
index 69f7b4c..781f222 100644
--- a/common/mmap.h
+++ b/util/memory/mmap.h
@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_COMMON_MMAP_H_
-#define LIBTEXTCLASSIFIER_COMMON_MMAP_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_MEMORY_MMAP_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_MEMORY_MMAP_H_
 
 #include <stddef.h>
 
@@ -24,8 +24,7 @@
 #include "util/base/integral_types.h"
 #include "util/strings/stringpiece.h"
 
-namespace libtextclassifier {
-namespace nlp_core {
+namespace libtextclassifier2 {
 
 // Handle for a memory area where a file has been mmapped.
 //
@@ -137,7 +136,6 @@
   MmapHandle handle_;
 };
 
-}  // namespace nlp_core
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_COMMON_MMAP_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_MEMORY_MMAP_H_
diff --git a/util/strings/numbers.cc b/util/strings/numbers.cc
index 4bd8b82..a89c0ef 100644
--- a/util/strings/numbers.cc
+++ b/util/strings/numbers.cc
@@ -22,7 +22,7 @@
 
 #include <stdlib.h>
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 bool ParseInt32(const char *c_str, int32 *value) {
   char *temp;
@@ -72,4 +72,4 @@
 }
 #endif  // COMPILER_MSVC
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/util/strings/numbers.h b/util/strings/numbers.h
index eda53bf..096954e 100644
--- a/util/strings/numbers.h
+++ b/util/strings/numbers.h
@@ -14,14 +14,14 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_STRINGS_NUMBERS_H_
-#define LIBTEXTCLASSIFIER_UTIL_STRINGS_NUMBERS_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_STRINGS_NUMBERS_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_STRINGS_NUMBERS_H_
 
 #include <string>
 
 #include "util/base/integral_types.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 // Parses an int32 from a C-style string.
 //
@@ -47,7 +47,6 @@
 // int types.
 std::string IntToString(int64 input);
 
+}  // namespace libtextclassifier2
 
-}  // namespace libtextclassifier
-
-#endif  // LIBTEXTCLASSIFIER_UTIL_STRINGS_NUMBERS_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_STRINGS_NUMBERS_H_
diff --git a/util/strings/numbers_test.cc b/util/strings/numbers_test.cc
index f3a3f27..1fdd78a 100644
--- a/util/strings/numbers_test.cc
+++ b/util/strings/numbers_test.cc
@@ -19,7 +19,7 @@
 #include "util/base/integral_types.h"
 #include "gtest/gtest.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 namespace {
 
 void TestParseInt32(const char *c_str, bool expected_parsing_success,
@@ -100,4 +100,4 @@
   TestParseDouble("23.5a", false);
 }
 }  // namespace
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/util/strings/split.cc b/util/strings/split.cc
index 8d250bb..e61e3ba 100644
--- a/util/strings/split.cc
+++ b/util/strings/split.cc
@@ -16,7 +16,7 @@
 
 #include "util/strings/split.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 namespace strings {
 
 std::vector<std::string> Split(const std::string &text, char delim) {
@@ -35,4 +35,4 @@
 }
 
 }  // namespace strings
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/util/strings/split.h b/util/strings/split.h
index b661ede0..9860265 100644
--- a/util/strings/split.h
+++ b/util/strings/split.h
@@ -14,18 +14,18 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_STRINGS_SPLIT_H_
-#define LIBTEXTCLASSIFIER_UTIL_STRINGS_SPLIT_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_STRINGS_SPLIT_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_STRINGS_SPLIT_H_
 
 #include <string>
 #include <vector>
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 namespace strings {
 
 std::vector<std::string> Split(const std::string &text, char delim);
 
 }  // namespace strings
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_STRINGS_SPLIT_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_STRINGS_SPLIT_H_
diff --git a/util/strings/stringpiece.h b/util/strings/stringpiece.h
index 8c42d83..f6187e9 100644
--- a/util/strings/stringpiece.h
+++ b/util/strings/stringpiece.h
@@ -14,14 +14,14 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_STRINGS_STRINGPIECE_H_
-#define LIBTEXTCLASSIFIER_UTIL_STRINGS_STRINGPIECE_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_STRINGS_STRINGPIECE_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_STRINGS_STRINGPIECE_H_
 
 #include <stddef.h>
 
 #include <string>
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 // Read-only "view" of a piece of data.  Does not own the underlying data.
 class StringPiece {
@@ -61,6 +61,6 @@
   size_t size_;
 };
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_STRINGS_STRINGPIECE_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_STRINGS_STRINGPIECE_H_
diff --git a/util/strings/utf8.h b/util/strings/utf8.h
index 93c7fea..89823e2 100644
--- a/util/strings/utf8.h
+++ b/util/strings/utf8.h
@@ -14,10 +14,10 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_STRINGS_UTF8_H_
-#define LIBTEXTCLASSIFIER_UTIL_STRINGS_UTF8_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_STRINGS_UTF8_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_STRINGS_UTF8_H_
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 // Returns the length (number of bytes) of the Unicode code point starting at
 // src, based on inspecting just that one byte.  Preconditions: src != NULL,
@@ -44,6 +44,6 @@
   return static_cast<signed char>(x) < -0x40;
 }
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_STRINGS_UTF8_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_STRINGS_UTF8_H_
diff --git a/util/utf8/unicodetext.cc b/util/utf8/unicodetext.cc
index dbab1c8..c814a2e 100644
--- a/util/utf8/unicodetext.cc
+++ b/util/utf8/unicodetext.cc
@@ -22,7 +22,7 @@
 
 #include "util/strings/utf8.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 // *************** Data representation **********
 // Note: the copy constructor is undefined.
@@ -109,6 +109,61 @@
   return *this;
 }
 
+namespace {
+
+enum {
+  RuneError = 0xFFFD,  // Decoding error in UTF.
+  RuneMax = 0x10FFFF,  // Maximum rune value.
+};
+
+int runetochar(const char32 rune, char* dest) {
+  // Convert to unsigned for range check.
+  uint32 c;
+
+  // 1 char 00-7F
+  c = rune;
+  if (c <= 0x7F) {
+    dest[0] = static_cast<char>(c);
+    return 1;
+  }
+
+  // 2 char 0080-07FF
+  if (c <= 0x07FF) {
+    dest[0] = 0xC0 | static_cast<char>(c >> 1 * 6);
+    dest[1] = 0x80 | (c & 0x3F);
+    return 2;
+  }
+
+  // Range check
+  if (c > RuneMax) {
+    c = RuneError;
+  }
+
+  // 3 char 0800-FFFF
+  if (c <= 0xFFFF) {
+    dest[0] = 0xE0 | static_cast<char>(c >> 2 * 6);
+    dest[1] = 0x80 | ((c >> 1 * 6) & 0x3F);
+    dest[2] = 0x80 | (c & 0x3F);
+    return 3;
+  }
+
+  // 4 char 10000-1FFFFF
+  dest[0] = 0xF0 | static_cast<char>(c >> 3 * 6);
+  dest[1] = 0x80 | ((c >> 2 * 6) & 0x3F);
+  dest[2] = 0x80 | ((c >> 1 * 6) & 0x3F);
+  dest[3] = 0x80 | (c & 0x3F);
+  return 4;
+}
+
+}  // namespace
+
+UnicodeText& UnicodeText::AppendCodepoint(char32 ch) {
+  char str[4];
+  int char_len = runetochar(ch, str);
+  repr_.append(str, char_len);
+  return *this;
+}
+
 void UnicodeText::clear() { repr_.clear(); }
 
 int UnicodeText::size() const { return std::distance(begin(), end()); }
@@ -195,4 +250,4 @@
   return UTF8ToUnicodeText(str.data(), str.size(), do_copy);
 }
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
diff --git a/util/utf8/unicodetext.h b/util/utf8/unicodetext.h
index 6a21058..d331f9b 100644
--- a/util/utf8/unicodetext.h
+++ b/util/utf8/unicodetext.h
@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-#ifndef LIBTEXTCLASSIFIER_UTIL_UTF8_UNICODETEXT_H_
-#define LIBTEXTCLASSIFIER_UTIL_UTF8_UNICODETEXT_H_
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_UTF8_UNICODETEXT_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_UTF8_UNICODETEXT_H_
 
 #include <iterator>
 #include <string>
@@ -23,7 +23,7 @@
 
 #include "util/base/integral_types.h"
 
-namespace libtextclassifier {
+namespace libtextclassifier2 {
 
 // ***************************** UnicodeText **************************
 //
@@ -150,6 +150,7 @@
 
   // Calling this may invalidate pointers to underlying data.
   UnicodeText& AppendUTF8(const char* utf8, int len);
+  UnicodeText& AppendCodepoint(char32 ch);
   void clear();
 
   static std::string UTF8Substring(const const_iterator& first,
@@ -193,6 +194,6 @@
 UnicodeText UTF8ToUnicodeText(const char* utf8_buf, int len, bool do_copy);
 UnicodeText UTF8ToUnicodeText(const std::string& str, bool do_copy);
 
-}  // namespace libtextclassifier
+}  // namespace libtextclassifier2
 
-#endif  // LIBTEXTCLASSIFIER_UTIL_UTF8_UNICODETEXT_H_
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_UTF8_UNICODETEXT_H_
diff --git a/util/utf8/unilib-icu.cc b/util/utf8/unilib-icu.cc
new file mode 100644
index 0000000..147a364
--- /dev/null
+++ b/util/utf8/unilib-icu.cc
@@ -0,0 +1,109 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "util/utf8/unilib-icu.h"
+
+#include "util/base/logging.h"
+
+namespace libtextclassifier2 {
+
+bool UniLib::IsOpeningBracket(char32 codepoint) const {
+  return u_getIntPropertyValue(codepoint, UCHAR_BIDI_PAIRED_BRACKET_TYPE) ==
+         U_BPT_OPEN;
+}
+
+bool UniLib::IsClosingBracket(char32 codepoint) const {
+  return u_getIntPropertyValue(codepoint, UCHAR_BIDI_PAIRED_BRACKET_TYPE) ==
+         U_BPT_CLOSE;
+}
+
+bool UniLib::IsWhitespace(char32 codepoint) const {
+  return u_isWhitespace(codepoint);
+}
+
+bool UniLib::IsDigit(char32 codepoint) const { return u_isdigit(codepoint); }
+
+bool UniLib::IsUpper(char32 codepoint) const { return u_isupper(codepoint); }
+
+char32 UniLib::ToLower(char32 codepoint) const { return u_tolower(codepoint); }
+
+char32 UniLib::GetPairedBracket(char32 codepoint) const {
+  return u_getBidiPairedBracket(codepoint);
+}
+
+bool UniLib::RegexPattern::Matches(const std::string& text) {
+  const icu::UnicodeString unicode_text(text.c_str(), text.size(), "utf-8");
+  UErrorCode status;
+  status = U_ZERO_ERROR;
+  std::unique_ptr<icu::RegexMatcher> matcher(
+      pattern_->matcher(unicode_text, status));
+  if (U_FAILURE(status) || !matcher) {
+    return false;
+  }
+
+  status = U_ZERO_ERROR;
+  const bool result = matcher->matches(/*startIndex=*/0, status);
+  if (U_FAILURE(status)) {
+    return false;
+  }
+
+  return result;
+}
+
+constexpr int UniLib::BreakIterator::kDone;
+
+UniLib::BreakIterator::BreakIterator(const std::string& text) {
+  icu::ErrorCode status;
+  break_iterator_.reset(
+      icu::BreakIterator::createWordInstance(icu::Locale("en"), status));
+  if (!status.isSuccess()) {
+    break_iterator_.reset();
+    return;
+  }
+
+  const icu::UnicodeString unicode_text = icu::UnicodeString::fromUTF8(text);
+  break_iterator_->setText(unicode_text);
+}
+
+int UniLib::BreakIterator::Next() {
+  const int result = break_iterator_->next();
+  if (result == icu::BreakIterator::DONE) {
+    return BreakIterator::kDone;
+  } else {
+    return result;
+  }
+}
+
+std::unique_ptr<UniLib::RegexPattern> UniLib::CreateRegexPattern(
+    const std::string& regex) const {
+  UErrorCode status = U_ZERO_ERROR;
+  std::unique_ptr<icu::RegexPattern> pattern(icu::RegexPattern::compile(
+      icu::UnicodeString(regex.c_str(), regex.size(), "utf-8"), /*flags=*/0,
+      status));
+  if (U_FAILURE(status) || !pattern) {
+    return nullptr;
+  }
+  return std::unique_ptr<UniLib::RegexPattern>(
+      new UniLib::RegexPattern(std::move(pattern)));
+}
+
+std::unique_ptr<UniLib::BreakIterator> UniLib::CreateBreakIterator(
+    const std::string& text) const {
+  return std::unique_ptr<UniLib::BreakIterator>(
+      new UniLib::BreakIterator(text));
+}
+
+}  // namespace libtextclassifier2
diff --git a/util/utf8/unilib-icu.h b/util/utf8/unilib-icu.h
new file mode 100644
index 0000000..0d34b74
--- /dev/null
+++ b/util/utf8/unilib-icu.h
@@ -0,0 +1,81 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// UniLib implementation with the help of ICU. UniLib is basically a wrapper
+// around the ICU functionality.
+
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_UTF8_UNILIB_ICU_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_UTF8_UNILIB_ICU_H_
+
+#include <memory>
+#include <string>
+
+#include "util/base/integral_types.h"
+#include "unicode/brkiter.h"
+#include "unicode/errorcode.h"
+#include "unicode/regex.h"
+#include "unicode/uchar.h"
+
+namespace libtextclassifier2 {
+
+class UniLib {
+ public:
+  bool IsOpeningBracket(char32 codepoint) const;
+  bool IsClosingBracket(char32 codepoint) const;
+  bool IsWhitespace(char32 codepoint) const;
+  bool IsDigit(char32 codepoint) const;
+  bool IsUpper(char32 codepoint) const;
+
+  char32 ToLower(char32 codepoint) const;
+  char32 GetPairedBracket(char32 codepoint) const;
+
+  class RegexPattern {
+   public:
+    // Returns true if the whole input matches with the regex.
+    bool Matches(const std::string& text);
+
+   protected:
+    friend class UniLib;
+    explicit RegexPattern(std::unique_ptr<icu::RegexPattern> pattern)
+        : pattern_(std::move(pattern)) {}
+
+   private:
+    std::unique_ptr<icu::RegexPattern> pattern_;
+  };
+
+  class BreakIterator {
+   public:
+    int Next();
+
+    static constexpr int kDone = -1;
+
+   protected:
+    friend class UniLib;
+    explicit BreakIterator(const std::string& text);
+
+   private:
+    std::unique_ptr<icu::BreakIterator> break_iterator_;
+  };
+
+  std::unique_ptr<RegexPattern> CreateRegexPattern(
+      const std::string& regex) const;
+  std::unique_ptr<BreakIterator> CreateBreakIterator(
+      const std::string& text) const;
+};
+
+}  // namespace libtextclassifier2
+
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_UTF8_UNILIB_ICU_H_
diff --git a/util/utf8/unilib.h b/util/utf8/unilib.h
new file mode 100644
index 0000000..b583d72
--- /dev/null
+++ b/util/utf8/unilib.h
@@ -0,0 +1,28 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_UTF8_UNILIB_H_
+#define KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_UTF8_UNILIB_H_
+
+#if defined LIBTEXTCLASSIFIER_UNILIB_ICU
+#include "util/utf8/unilib-icu.h"
+#elif defined LIBTEXTCLASSIFIER_UNILIB_DUMMY
+#include "util/utf8/unilib-dummy.h"
+#else
+#error No LIBTEXTCLASSIFIER_UNILIB implementation specified.
+#endif
+
+#endif  // KNOWLEDGE_CEREBRA_SENSE_TEXT_CLASSIFIER_LIB2_UTIL_UTF8_UNILIB_H_
diff --git a/util/utf8/unilib_test.cc b/util/utf8/unilib_test.cc
new file mode 100644
index 0000000..a1bbdf4
--- /dev/null
+++ b/util/utf8/unilib_test.cc
@@ -0,0 +1,45 @@
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "util/utf8/unilib.h"
+
+#include "util/base/logging.h"
+
+#include "gtest/gtest.h"
+
+namespace libtextclassifier2 {
+namespace {
+
+TEST(UniLibTest, Interface) {
+  UniLib unilib;
+  TC_LOG(INFO) << unilib.IsOpeningBracket('(');
+  TC_LOG(INFO) << unilib.IsClosingBracket(')');
+  TC_LOG(INFO) << unilib.IsWhitespace(')');
+  TC_LOG(INFO) << unilib.IsDigit(')');
+  TC_LOG(INFO) << unilib.IsUpper(')');
+  TC_LOG(INFO) << unilib.ToLower(')');
+  TC_LOG(INFO) << unilib.GetPairedBracket(')');
+  std::unique_ptr<UniLib::RegexPattern> pattern =
+      unilib.CreateRegexPattern("[0-9]");
+  TC_LOG(INFO) << pattern->Matches("Hello");
+  std::unique_ptr<UniLib::BreakIterator> iterator =
+      unilib.CreateBreakIterator("some text");
+  TC_LOG(INFO) << iterator->Next();
+  TC_LOG(INFO) << UniLib::BreakIterator::kDone;
+}
+
+}  // namespace
+}  // namespace libtextclassifier2