small-text
small-text: A focused, free library for building text-classification active-learning workflows. Ranked #14 of 16 in Active Learning Tools by our editors (6.0/10); pricing: Free plan; best for text-classification experiments with transformer models.
At a glance
- Editor score6.0 / 10
- PricingFree plan
- Best forText-classification experiments with transformer models
- Free planYes
- Paid fromNone
- Human annotation workflowExternal
- Facts checked24 Sep 2026
Where it wins
- Multiple sampling strategies, including uncertainty and embedding-based methods
- Supports PyTorch and Hugging Face Transformers classifiers
- Includes dataset utilities, splits, and stopping criteria
Where it doesn't
- Human annotation happens through an external process
- Supports text data rather than multiple data modalities
- Self-hosted deployment requires a developer-managed environment
Our verdict on small-text
small-text is a Python library for active learning in text classification. It is aimed at researchers and developers who want to build custom workflows for selecting informative examples, sending them for labeling, and iterating on classifiers. The project supports neural and transformer-based models and is distributed as open-source software for local or self-managed use, making it suitable for solo developers through enterprise teams with technical resources.
Its main strength is the breadth of its pool-based query loop for text. Users can configure random, uncertainty, diversity, and embedding-based sampling strategies to choose unlabeled examples for review. Support for transformer-based classifiers extends the library beyond basic model setups, while dataset abstractions and train/test split utilities help organize experiments. Stopping criteria provide a defined way to end active-learning iterations, which is useful when constructing repeatable research or development pipelines.
small-text is narrower than a hosted labeling product. Human annotation takes place through an external labeling process, so teams must connect the library to their own annotation workflow rather than work inside an integrated interface. Deployment is self-hosted, and the support channels are documentation and community resources. Choose it when you need a free, text-focused foundation with configurable sampling and transformer support. Consider an integrated labeling tool instead if annotation management, broader data types, or a hosted operating model are central requirements.
small-text pricing
small-text fact sheet
| Free plan | Yes |
|---|---|
| Paid from | None |
| Query strategies | random sampling; uncertainty sampling; diversity sampling; embedding-based sampling |
| Human annotation workflow | External |
| Supported data types | text |
| Model frameworks | PyTorch; Hugging Face Transformers |
| Deployment options | Self_hosted |
| Deployment | Self-hosted |
| Support | Docs, Community |
| Built for | Solo, Small business, Mid-market, Enterprise (editorial estimate) |
| Pricing | Free plan |
| Website | github.com |
| Facts checked | 24 Sep 2026 |
Alternatives to small-text
- ActiveTiggerA broad annotation and modeling workflow for teams working with text corpora.9.0
- PotatoA self-hosted annotation toolkit for teams combining active learning with varied media.8.2
- ProdigyA paid, developer-oriented annotator spanning NLP, vision, audio, and video.7.7
See all small-text alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026
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