{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"SetFit fine-tunes Sentence Transformers for text classification using only a handful of labeled examples per class, without requiring handcrafted prompts or large language models.","skillfed_tags":["few-shot-learning","text-classification","sentence-embeddings"],"use_cases":["Train a sentiment classifier on a customer review dataset with only 8 labeled examples per sentiment class","Build a multilingual text classifier by fine-tuning a multilingual Sentence Transformer checkpoint on a small labeled corpus","Rapidly prototype a document categorization system when labeled training data is scarce or expensive to obtain","Evaluate few-shot learning performance on benchmark datasets and compare against other methods using the provided training scripts"],"what_it_does":"SetFit is a framework for few-shot text classification that combines a pretrained Sentence Transformer body with a lightweight classification head (either scikit-learn's LogisticRegression or a differentiable PyTorch-based head). It achieves competitive accuracy on classification tasks using only a small number of labeled examples per class\u2014the documentation cites achieving results comparable to fine-tuning RoBERTa Large on a full 3k-example training set using only 8 labeled examples per class on sentiment data.\n\nThe package integrates with Hugging Face Hub for model discovery, training, and sharing. It wraps the fine-tuning process in a Trainer class that handles dataset sampling, evaluation, and model persistence. Runtime dependencies include datasets for data loading, transformers and sentence-transformers for embeddings, evaluate for metrics, huggingface_hub for Hub integration, scikit-learn for the default classification head, and packaging for version handling. No prompts or verbalizers are required\u2014the framework generates embeddings directly from raw text.","worth_installing":"Yes. SetFit is production-stable (Development Status 5), actively maintained, has no known vulnerabilities, and solves a real problem\u2014achieving strong text classification with minimal labeled data. The low install friction, permissive license, and integration with Hugging Face Hub make it a practical choice for few-shot classification tasks. Install it if you need to classify text with limited labeled examples."},"id":"setfit","links":{"html":"https://skillfed.io/packages/setfit","md":"https://skillfed.io/packages/setfit.md","pypi":"https://pypi.org/project/setfit/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-08-05","license_spdx":null,"license_treatment":"permissive","name":"setfit","python_support":"unspecified","summary":"Efficient few-shot learning with Sentence Transformers"},"popularity":{"monthly_downloads":261172,"position":8386,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.3"}