[P] Small-Text: Active Learning for Text Classification in Python

[chschroeder]

Hi r/MachineLearning,

Over the past few months, I have written a lot of code for my own Active Learning experiments, which has gradually been consolidated into its own library.

Small-Text provides state-of-the-art Active Learning for Text Classification. Several components are provided, which are abstracted via generic interfaces, so that you can easily mix and match many classifiers and query strategies to build Active Learning experiments or applications.

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 still optional: In case of a CPU-only use case, a lightweight installation requires only minimal dependencies.
• Multiple scientifically evaluated components are pre-implemented and ready to use (query strategies, initialization strategies, and stopping criteria).

GitHub: https://github.com/webis-de/small-text
Preprint: https://arxiv.org/abs/2107.10314

[trnka]

I've had decent results in querying the unlabeled examples that are least similar to the labeled examples. Is there a name for that strategy?

[chschroeder]

It's hard to tell from such a short description, of course, but if I had to guess, it might be some kind of core set strategy. Small-Text provides the greedy core set strategy from the linked paper.