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Active Learning for Text Classification in Python.

Installation | Quick Start | Contribution | Changelog | Docs

Small-Text provides state-of-the-art Active Learning for Text Classification. Several pre-implemented Query Strategies, Initialization Strategies, and Stopping Critera are provided, which can be easily mixed and matched to build active learning experiments or applications.

• Provides unified interfaces for Active Learning so that you can easily mix and match query strategies with classifiers provided by sklearn, Pytorch, or transformers.
• Supports GPU-based Pytorch models and integrates transformers so that you can use state-of-the-art Text Classification models for Active Learning.
• GPU is supported but not required. In case of a CPU-only use case, a lightweight installation only requires a minimal set of dependencies.
• Multiple scientifically evaluated components are pre-implemented and ready to use (Query Strategies, Initialization Strategies, and Stopping Criteria).

Active Learning allows you to efficiently label training data for supervised learning in a scenario where you have little to no labeled data.

• Version 1.4.0 (v1.4.0) - June 9th, 2024

  • New query strategy: AnchorSubsampling (aka AnchorAL).
    Special thanks to Pietro Lesci for the correspondence and code review.

• June 2024 Update

  • Version 2.0.0 is in progress and should be ready soon.

• Version 1.3.3 (v1.3.3) - December 29th, 2023

  • Bugfix release.

• Version 1.3.2 (v1.3.2) - August 19th, 2023

  • Bugfix release.

• Paper accepted at EACL 2023 🎉

  • The paper introducing small-text has been accepted at EACL 2023. Meet us at the conference in May!
  • Update: the paper was awarded EACL Best System Demonstration. Thank you, for your support!

For a complete list of changes, see the change log.

Small-Text can be easily installed via pip (or conda):

pip install small-text

The command results in a slim installation with only the necessary dependencies. For a full installation via pip, you just need to include the transformers extra requirement:

pip install small-text[transformers]

For conda, which lacks the extra requirements feature, a full installation can be achieved as follows:

conda install "torch>=1.6.0" "torchtext>=0.7.0" transformers small-text

The library requires Python 3.7 or newer. For using the GPU, CUDA 10.1 or newer is required. More information regarding the installation can be found in the documentation.

For a quick start, see the provided examples for binary classification, pytorch multi-class classification, and transformer-based multi-class classification, or check out the notebooks.

#	Notebook
1	Intro: Active Learning for Text Classification with Small-Text
2	Using Stopping Criteria for Active Learning
3	Active Learning using SetFit
4	Using SetFit's Zero Shot Capabilities for Cold Start Initialization

• Tutorial: 👂 Active learning for text classification with small-text (Use small-text conveniently from the argilla UI.)

A full list of showcases can be found in the docs.

🎀 Would you like to share your use case? Regardless if it is a paper, an experiment, a practical application, a thesis, a dataset, or other, let us know and we will add you to the showcase section or even here.

Read the latest documentation here. Noteworthy pages include:

• Overview of Query Strategies
• Reproducibility Notes

modAL, ALiPy, libact, ALToolbox

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

This software was created by Christopher Schröder (@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.

Small-Text has been introduced in detail in the EACL23 System Demonstration Paper "Small-Text: Active Learning for Text Classification in Python" which can be cited as follows:

@inproceedings{schroeder2023small-text,
    title = "Small-Text: Active Learning for Text Classification in Python",
    author = {Schr{\"o}der, Christopher  and  M{\"u}ller, Lydia  and  Niekler, Andreas  and  Potthast, Martin},
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.eacl-demo.11",
    pages = "84--95"
}

MIT License