nlpipes 0.1.1

Text Classification with TensorFlow

NLPIPES

Text Classification with Transformers

Table of Contents

1. Overview
   • Built With
2. How to Use
• Train a model
• Evaluate a model
• Predict with a model
3. References

Overview

NLPipes provides an easy way to use Transformers-based models for training, evaluation and inference on a diversity of text classification tasks, including:

• Zero-shot classification [Not yet implemented]: Assign one or more label to each text from a list of possible labels without the requirement of an annotated training dataset.
• Single-label classification: Assign one label to each text. A typical use case is sentiment analysis where one want to detect the overall sentiment polarity (e.g., positive, neutral, negative) in a review.
• Multi-label classification [Not yet implemented]: Assign one or more label to each text from a list of possible labels. A typical use case is tag detection where one want to detect the multiple aspects mentionned in a review (e.g., #product_quality, #delivery_time, #price, ...).
• Aspect-based classification [Not yet implemented]: Assign one label from a list of possible labels for each of a list of aspects. A typical use case is aspect based sentiment analysis where one want to detect each aspect mentionned in a review along his assocated sentiment polarity (e.g., #product_quality: neutral, #delivery_time: negative, #price: positive, ...).

Built with

NLPipes is built with TensorFlow and HuggingFace Transformers:

• TensorFlow: An end-to-end open source deep learning framework
• Transformers: An general-purpose open-sources library for transformers-based architectures

Pre-required

We recommend using a machine with at least 1 GPU accelerator and XX Go of memory to use NLPipes.

How To Use

NLPipes expose a simple Model API that offers an high-level abstraction to run several text classification tasks. The Model encapsulate most of the complex code from the library and save having to deal with the diversity and complexity of transformers based algorithms to build an end-to-end solution for text classification.

Train a model

Give NLPipes a single label for each text as a target to train a single-label classifier.

from nlpipes import Model

reviews = ["text", "text", "text", ...]
sentiments = ["positive", "negative", "neutral", ...]
all_labels = ["negative", "neutral", "positive"]

model = Model(task='single-label-classification',
              name_or_path='bert-base-uncased',
              all_labels=all_labels)

model.train(reviews, sentiments)
 
model.save('./sentiment_detection_model')

Evaluate a model

Give NLPipes an evaluation metric and the label(s) for each text to evaluate the model.

from nlpipes import Model, Confusion_matrix

eval_reviews   = ["text", "text", "text", ...]
eval_sentiments = ["positive", "negative", "neutral", ...]
eval_metric = Confusion_matrix(num_labels=3)

model = Model('./sentiment_detection_model')

evaluation = model.evaluate(eval_reviews, eval_sentiments,
                            metric=eval_metric)
 
print('Model performance: ', evaluation)

Predict with a model

Give NLPipes new texts to predict the most relevant label(s).

from nlpipes import Model

new_reviews = ["text", "text", "text", ...]

model = Model('./sentiment_detection_model')

predictions = model.predict(new_reviews)

print("Reviews: ", new_reviews)
print('Predicted labels: ', predictions.label)
print('Confidence Score: ', predictions.scores)

References