small-text 1.0.0a3

A simple, modular active learning library for text classification.

Active Learning for Text Classifcation in Python.

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Installation | Quick Start | Docs

Active Learning allows you to efficiently label training data in a small-data scenario.

This library provides state-of-the-art active learning for text classification, built with modularity and extensibility in mind.

Features

• Provides unified interfaces for Active Learning so that you can easily use any classifier provided by Integrates sklearn.
• (Optionally) As an optional feature, you can also use pytorch classifiers, including transformers models.
• Multiple scientifically-proven strategies re-implemented: Query Strategies, Initialization Strategies

Installation

pip install small-text

Requires Python 3.7 or newer. For using the GPU, CUDA 10.1 or newer is required.

Quick Start

For a quick start, see the provided examples for binary classification, pytorch multi-class classification, or transformer-based multi-class classification

Docs

The API docs (currently work in progress) can be generated using sphinx:

pip install sphinx sphinx-rtd-theme
cd docs/
make

Alternatives

• modAL
• ALiPy
• libact

Contribution

Contributions are welcome. Details can be found in CONTRIBUTING.md.

Acknowledgments

This software was created by @chschroeder at Leipzig University's NLP group which is a part of the Webis research network. The encompassing project was funded by the Development Bank of Saxony (SAB) under project number 100335729.

License

MIT License