small-text
Active Learning for Text Classifcation in Python.
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 which allows to 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 use any classifier provided by 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
Small-text can be easily installed via pip:
pip install small-text
For a full installation include the transformers extra requirement:
pip install small-text[transformers]
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, or
transformer-based multi-class classification
Documentation
Read the latest documentation (currently work in progress) here.
