webis-de/small-text
Active Learning for Text Classification in Python
This project helps you efficiently categorize large amounts of text data when you only have a small number of already-labeled examples. You provide an initial set of labeled text and a larger pool of unlabeled text, and the system intelligently suggests which additional pieces of text you should label next to get the most accurate categorization model possible. This is ideal for researchers, analysts, or anyone who needs to classify text but has limited resources for manual labeling.
638 stars. Actively maintained with 1 commit in the last 30 days. Available on PyPI.
Use this if you need to classify text into categories but find traditional manual labeling too time-consuming or expensive for large datasets.
Not ideal if you already have a massive, perfectly labeled dataset or if your task doesn't involve text classification.
Stars
638
Forks
77
Language
Python
License
MIT
Category
Last pushed
Mar 08, 2026
Commits (30d)
1
Dependencies
6
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/webis-de/small-text"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related models
MaartenGr/BERTopic
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
mead-ml/mead-baseline
Deep-Learning Model Exploration and Development for NLP
x-tabdeveloping/turftopic
Robust and fast topic models with sentence-transformers.
HumanSignal/label-studio-transformers
Label data using HuggingFace's transformers and automatically get a prediction service
hiyouga/Dual-Contrastive-Learning
Code for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation"