CLI Demos for C++ Text Task APIs

This folder contains simple command-line tools for easily trying out the C++ Text Task APIs.

Bert Question Answerer

Prerequisites

You will need:

a TFLite bert based question answerer model from model maker. (e.g. mobilebert or albert available on TensorFlow Hub).

Usage

In the console, run:

# Download the model:
curl \
 -L 'https://tfhub.dev/tensorflow/lite-model/mobilebert/1/default/1?lite-format=tflite' \
 -o /tmp/mobilebert.tflite

# Run the classification tool:
bazel run -c opt \
 tensorflow_lite_support/examples/task/text/desktop:bert_question_answerer_demo -- \
 --model_path=/tmp/mobilebert.tflite \
 --question="Where is Amazon rainforest?" \
 --context="The Amazon rainforest, alternatively, the Amazon Jungle, also known in \
English as Amazonia, is a moist broadleaf tropical rainforest in the Amazon \
biome that covers most of the Amazon basin of South America. This basin \
encompasses 7,000,000 km2 (2,700,000 sq mi), of which \
5,500,000 km2 (2,100,000 sq mi) are covered by the rainforest. This region \
includes territory belonging to nine nations."

Results

In the console, you should get:

answer[0]:  'South America.'
logit: 1.84847, start_index: 39, end_index: 40
answer[1]:  'most of the Amazon basin of South America.'
logit: 1.2921, start_index: 34, end_index: 40
answer[2]:  'the Amazon basin of South America.'
logit: -0.0959535, start_index: 36, end_index: 40
answer[3]:  'the Amazon biome that covers most of the Amazon basin of South America.'
logit: -0.498558, start_index: 28, end_index: 40
answer[4]:  'Amazon basin of South America.'
logit: -0.774266, start_index: 37, end_index: 40

NLClassifier

Prerequisites

You will need:

a TFLite text classification model with certain format. (e.g. movie_review_model, a model to classify movie reviews), you'll need to configure the input tensor and out tensor for the API, see the doc for details.

Usage

In the console, run:

# Download the model:
curl \
 -L 'https://storage.googleapis.com/download.tensorflow.org/models/tflite/text_classification/text_classification_v2.tflite' \
 -o /tmp/movie_review.tflite

# Run the detection tool:
bazel run -c opt \
 tensorflow_lite_support/examples/task/text/desktop:nl_classifier_demo -- \
 --model_path=/tmp/movie_review.tflite \
 --text="What a waste of my time." \
 --input_tensor_name="input_text" \
 --output_score_tensor_name="probability"

Results

In the console, you should get:

category[0]: 'Negative' : '0.81313'
category[1]: 'Positive' : '0.18687'

BertNLClassifier

Prerequisites

TODO(b/163086702): Update the links to models with metadata attached.

You will need:

a Bert based TFLite text classification model from model maker. (e.g. movie_review_model available on TensorFlow Hub).

Usage

In the console, run:

# Download the model:
curl \
 -L 'https://url/to/bert/nl/classifier' \
 -o /tmp/bert_movie_review.tflite

# Run the segmentation tool:
bazel run -c opt \
 tensorflow_lite_support/examples/task/text/desktop:bert_nl_classifier_demo -- \
 --model_path=/tmp/bert_movie_review.tflite \
 --text="it's a charming and often affecting journey"

Results

In the console, you should get:

category[0]: 'negative' : '0.00006'
category[1]: 'positive' : '0.99994'