---
id: setfit
version: "1.1.3"
license: Apache 2.0
license_treatment: permissive
maintenance: active
---

# setfit — Efficient few-shot learning with Sentence Transformers

License: permissive · Maintenance: active · Downloads: 261.2K/mo

## What it is and 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—the 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.

The 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—the framework generates embeddings directly from raw text.

Use it for:

- 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

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

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.

Yes. SetFit is production-stable (Development Status 5), actively maintained, has no known vulnerabilities, and solves a real problem—achieving 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.

## Install

pip install setfit
uv add setfit
poetry add setfit

## Installing setfit

Before you install: Low friction: pure Python wheel with seven runtime dependencies (datasets, sentence-transformers, transformers, evaluate, huggingface_hub, scikit-learn, packaging). Repository is active with recent commits and no archived status.

License in practice: Apache 2.0 permissive license allows commercial and private use with minimal restrictions—suitable for most production and research contexts.

Quickstart:

pip install setfit

from setfit import SetFitModel, Trainer, TrainingArguments
from datasets import load_dataset

dataset = load_dataset("sst2")
model = SetFitModel.from_pretrained("sentence-transformers/paraphrase-mpnet-base-v2", labels=["negative", "positive"])
trainer = Trainer(model=model, args=TrainingArguments(batch_size=16, num_epochs=4), train_dataset=dataset["train"])
trainer.train()
preds = model.predict(["i loved the spiderman movie!"])

Verify before relying:

- Minimum Python version requirement (classifiers list 3.9–3.12 but requires_python field is unspecified)
- Whether multilingual support requires specific Sentence Transformer checkpoints or works automatically
- Typical memory and compute requirements for training on different dataset sizes

## Package facts

- License: Apache 2.0 (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: active
- Downloads: 261.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

few-shot text classification, efficient fine-tuning sentence transformers, low-data text labeling, prompt-free few-shot learning, multilingual text classification, small-sample model training, few-shot-learning, text-classification, sentence-embeddings

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