# Small-Text: Active Learning for Text Classification in Python

arXiv: https://arxiv.org/abs/2107.10314 · HTML: https://mlsanity.com/paper/2107.10314/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 73 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Debiased Contrastive Learning](https://mlsanity.com/paper/2007.00224/) (2020)
- [On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines](https://mlsanity.com/paper/2006.04884/) (2020)
- [German's Next Language Model](https://mlsanity.com/paper/2010.10906/) (2020)
- [Cold-start Active Learning through Self-supervised Language Modeling](https://mlsanity.com/paper/2010.09535/) (2020)
- [Discriminative Active Learning](https://mlsanity.com/paper/1907.06347/) (2019)
- [Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers](https://mlsanity.com/paper/2107.05687/) (2021)
- [modAL: A modular active learning framework for Python](https://mlsanity.com/paper/1805.00979/) (2018)
- [Similarity Search for Efficient Active Learning and Search of Rare Concepts](https://mlsanity.com/paper/2007.00077/) (2020)
- [A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping](https://mlsanity.com/paper/1409.5165/) (2014)
- [Bayesian active learning for production, a systematic study and a reusable library](https://mlsanity.com/paper/2006.09916/) (2020)
- [Introducing Geometry in Active Learning for Image Segmentation](https://mlsanity.com/paper/1508.04955/) (2015)
- [libact: Pool-based Active Learning in Python](https://mlsanity.com/paper/1710.00379/) (2017)
- [ALiPy: Active Learning in Python](https://mlsanity.com/paper/1901.03802/) (2019)
- [Stopping Active Learning based on Predicted Change of F Measure for Text Classification](https://mlsanity.com/paper/1901.09118/) (2019)

## Built on by (most cited first, 19 of 19)

- [A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice](https://mlsanity.com/paper/2410.15326/) (2024)
- [Active Learning Principles for In-Context Learning with Large Language Models](https://mlsanity.com/paper/2305.14264/) (2023)
- [ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios](https://mlsanity.com/paper/2405.10808/) (2024)
- [On the Limitations of Simulating Active Learning](https://mlsanity.com/paper/2305.13342/) (2023)
- [STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2403.01165/) (2024)
- [Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning](https://mlsanity.com/paper/2209.10193/) (2022)
- [To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models](https://mlsanity.com/paper/2210.03005/) (2022)
- [A Cost-Effective LLM-based Approach to Identify Wildlife Trafficking in Online Marketplaces](https://mlsanity.com/paper/2504.21211/) (2025)
- [Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning](https://mlsanity.com/paper/2408.09914/) (2024)
- [Retrieving Floods without Floodlights: Topic Models as Binary Classifiers for Extreme Climate Events in German News](https://mlsanity.com/paper/2605.03450/) (2026)
- [ALANNO: An Active Learning Annotation System for Mortals](https://mlsanity.com/paper/2211.06224/) (2022)
- [ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval](https://mlsanity.com/paper/2509.07512/) (2025)
- [Active Slice Discovery in Large Language Models](https://mlsanity.com/paper/2511.20713/) (2025)
- [Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Models](https://mlsanity.com/paper/2406.09206/) (2024)
- [ALPET: Active Few-shot Learning for Citation Worthiness Detection in Low-Resource Wikipedia Languages](https://mlsanity.com/paper/2502.03292/) (2025)
- [Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid](https://mlsanity.com/paper/2506.03817/) (2025)
- [ATGen: A Framework for Active Text Generation](https://mlsanity.com/paper/2506.23342/) (2025)
- [LASTIST: LArge-Scale Target-Independent STance dataset](https://mlsanity.com/paper/2510.25783/) (2025)
- [Active Learning for Finding a Needle in a Haystack](https://mlsanity.com/paper/2610.15165/) (2026)

## Abstract (arXiv)

We introduce small-text, an easy-to-use active learning library, which offers pool-based active learning for single- and multi-label text classification in Python. It features numerous pre-implemented state-of-the-art query strategies, including some that leverage the GPU. Standardized interfaces allow the combination of a variety of classifiers, query strategies, and stopping criteria, facilitating a quick mix and match, and enabling a rapid and convenient development of both active learning experiments and applications. With the objective of making various classifiers and query strategies accessible for active learning, small-text integrates several well-known machine learning libraries, namely scikit-learn, PyTorch, and Hugging Face transformers. The latter integrations are optionally installable extensions, so GPUs can be used but are not required. Using this new library, we investigate the performance of the recently published SetFit training paradigm, which we compare to vanilla transformer fine-tuning, finding that it matches the latter in classification accuracy while outperforming it in area under the curve. The library is available under the MIT License at https://github.com/webis-de/small-text, in version 1.3.0 at the time of writing.