nlpipes 0.1.5

Text Classification with Transformers

NLPIPES

Text Classification with Transformers

Examples • Documentation • 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:

• 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-labels 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, ...).
• 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.

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

Getting Started

Installation

1. Create a virtual environment

python3 -m venv nlpipesenv
source nlpipesenv/bin/activate

2. Install the package

pip install nlpipes

Examples

NLPipes expose a simple Model API that offers a common 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 complexity of transformers based algorithms to build a text classifier. Here are some examples of NLPipes applications on real open datasets:

Name	Notebook	Description	Task	Size	Memory
GooglePlay Sentiment Detection	Link	Train a model to label reviews from the GooglePlay store reviews	Single-label classification
StackOverflow tags Detection	Coming soon	Train a model to detect tags from the StackOverFlow questions	Multiple-labels classification
GooglePlay Aspect and Sentiment Detection	Coming soon	Train a model for aspect based sentiment detection to detect the aspects mentionned in GooglePlay reviews along his assocated sentiment polarity	Aspect-based classification

Notice

NLPipes is still in its early stage and is not yet suitable for production usage. Proceed with caution and use it at your own risk. The library comes with no warranty and future releases could bring substantial API and behavior changes.