mlsanityData to 7 Oct 2026

Small-Text: Active Learning for Text Classification in Python

arXiv 2107.10314 ↗First posted 21 Jul 2021cs.LG cs.CLcited by 18

Not on the map. Why?

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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 26 of its 73 references that are in the map. First posted on arXiv 21 Jul 2021.

Builds on

26 of 73 references.

References · 26

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  6. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  7. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  8. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  9. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,548
  10. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  11. 2019Data-Efficient Image Recognition with Contrastive Predictive Codingcited by 672
  12. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 602
14 more
  1. 2020Debiased Contrastive Learningcited by 354
  2. 2020On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselinescited by 254
  3. 2020German's Next Language Modelcited by 144
  4. 2020Cold-start Active Learning through Self-supervised Language Modelingcited by 128
  5. 2019Discriminative Active Learningcited by 97
  6. 2021Revisiting Uncertainty-based Query Strategies for Active Learning with Transformerscited by 59
  7. 2018modAL: A modular active learning framework for Pythoncited by 35
  8. 2020Similarity Search for Efficient Active Learning and Search of Rare Conceptscited by 24
  9. 2014A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stoppingcited by 21
  10. 2020Bayesian active learning for production, a systematic study and a reusable librarycited by 15
  11. 2015Introducing Geometry in Active Learning for Image Segmentationcited by 12
  12. 2017libact: Pool-based Active Learning in Pythoncited by 12
  13. 2019ALiPy: Active Learning in Pythoncited by 11
  14. 2019Stopping Active Learning based on Predicted Change of F Measure for Text Classificationcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 2; 2024: 5; 2025: 7; 2026: 1.

18 papers.

Sort:

By area:

  1. 2024A Survey of Uncertainty Estimation in LLMs: Theory Meets Practicecited by 54
  2. 2023Active Learning Principles for In-Context Learning with Large Language Modelscited by 53
  3. 2024ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarioscited by 17
  4. 2023On the Limitations of Simulating Active Learningcited by 9
  5. 2024STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Modelscited by 5
  6. 2022Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learningcited by 4
  7. 2022To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Modelscited by 3
  8. 2025A Cost-Effective LLM-based Approach to Identify Wildlife Trafficking in Online Marketplacescited by 3
  9. 2024Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuningcited by 2
  10. 2026Retrieving Floods without Floodlights: Topic Models as Binary Classifiers for Extreme Climate Events in German Newscited by 2
  11. 2022ALANNO: An Active Learning Annotation System for Mortalscited by 1
  12. 2025ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrievalcited by 1
6 more
  1. 2025Active Slice Discovery in Large Language Modelscited by 1
  2. 2024Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language Modelscited by 0
  3. 2025ALPET: Active Few-shot Learning for Citation Worthiness Detection in Low-Resource Wikipedia Languagescited by 0
  4. 2025Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Gridcited by 0
  5. 2025ATGen: A Framework for Active Text Generationcited by 0
  6. 2025LASTIST: LArge-Scale Target-Independent STance datasetcited by 0