setfit

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?

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Install

setfit on PyPI

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!"])

Package facts

Evidence: setfit-1.1.3-py3-none-any.whl

Tags

More Artificial Intelligence packages

See also trl · transformer-smaller-training-vocab · model2vec · sentence-transformers · peft · flair · detoxify · gliner · InstructorEmbedding

Further reading