Active Learning for Text Classifcation in Python.
Installation | Quick Start | 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.
What is Active Learning?
Active Learning allows you to efficiently label training data in a small data scenario.
Features
- 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).
News
-
May Beta Release (v1.0.0b4) - May 04, 2022
- Two new query strategies: DiscriminativeActiveLearning, SEALS.
Datasetinterface now has aclone()method.- Added a concept for optional dependencies.
-
March Beta Release (v1.0.0b3) - March 06, 2022
- Consolidated interfaces: renamed and unified some arguments before v1.0.0.
- New query strategy: ContrastiveActiveLearning.
-
🎉 Beta Release (v1.0.0b1) - February 22, 2022- New features: multi-label classification and stopping criteria are now supported.
- Added/revised large parts of the documentation.
For a complete list of changes, see the change log.
Installation
Small-Text can be easily installed via pip:
pip install small-textFor a full installation include the transformers extra requirement:
pip install small-text[transformers]It 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.
Quick Start
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.
Notebooks
| # | Notebook | |
|---|---|---|
| 1 | Intro: Active Learning for Text Classification with Small-Text |  |
| 2 | Using Stopping Criteria for Active Learning | |
Documentation
Read the latest documentation here. Noteworthy pages include:
Alternatives
Contribution
Contributions are welcome. Details can be found in CONTRIBUTING.md.
Acknowledgments
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.
Citation
A preprint which introduces small-text is available here:
Small-Text: Active Learning for Text Classification in Python.
@misc{schroeder2021smalltext,
title={Small-Text: Active Learning for Text Classification in Python},
author={Christopher Schröder and Lydia Müller and Andreas Niekler and Martin Potthast},
year={2021},
eprint={2107.10314},
archivePrefix={arXiv},
primaryClass={cs.LG}
}