Package: setfit
🤗Models| 📊Datasets| 📕Documentation| 📖Blog| 📃PaperSetFit - Efficient Few-shot Learning with Sentence TransformersSetFit is an efficient and prompt-free framework for few-shot fine-tuning ofSentence Transformers. It achieves high accuracy with little labeled data - for instance, with only 8 labeled examples per class on the Customer Reviews sentiment dataset, SetFit is competitive with fine-tuning RoBERTa Large on the full training set of 3k examples 🤯!Compared to other few-shot learning methods, SetFit has several unique features:🗣No prompts or verbalizers:Current techniques for few-shot fine-tuning require handcrafted prompts or verbalizers to convert examples into a format suitable for the underlying language model. SetFit dispenses with prompts altogether by generating rich embeddings directly from text examples.🏎Fast to train:SetFit doesn't require large-scale models like T0 or GPT-3 to achieve high accuracy. As a result, it is typically an order of magnitude (or more) faster to train and run inference with.🌎Multilingual support: SetFit can be used with anySentence Transformeron the Hub, which means you can classify text in multiple languages by simply fine-tuning a multilingual checkpoint.Check out theSetFit Documentationfor more information!InstallationDownload and installsetfitby running:pipinstallsetfitIf you want the bleeding-edge version instead, install from source by running:pipinstallgit+https://github.com/huggingface/setfit.gitUsageThequickstartis a good place to learn about training, saving, loading, and performing inference with SetFit models.For more examples, check out thenotebooksdirectory, thetutorials, or thehow-to guides.Training a SetFit modelsetfitis integrated with theHugging Face Huband provides two main classes:SetFitModel: a wrapper that combines a pretrained body fromsentence_transformersand a classification head from eitherscikit-learnorSetFitHead(a differentiable head built uponPyTorchwith similar APIs tosentence_transformers).Trainer: a helper class that wraps the fine-tuning process of SetFit.Here is a simple end-to-end training example using the default classification head fromscikit-learn:fromdatasetsimportload_datasetfromsetfitimportSetFitModel,Trainer,TrainingArguments,sample_dataset# Load a dataset from the Hugging Face Hubdataset=load_dataset("sst2")# Simulate the few-shot regime by sampling 8 examples per classtrain_dataset=sample_dataset(dataset["train"],label_column="label",num_samples=8)eval_dataset=dataset["validation"].select(range(100))test_dataset=dataset["validation"].select(range(100,len(dataset["validation"])))# Load a SetFit model from Hubmodel=SetFitModel.from_pretrained("sentence-transformers/paraphrase-mpnet-base-v2",labels=["negative","positive"],)args=TrainingArguments(batch_size=16,num_epochs=4,evaluation_strategy="epoch",save_strategy="epoch",load_best_model_at_end=True,)trainer=Trainer(model=model,args=args,train_dataset=train_dataset,eval_dataset=eval_dataset,metric="accuracy",column_mapping={"sentence":"text","label":"label"}# Map dataset columns to text/label expected by trainer)# Train and evaluatetrainer.train()metrics=trainer.evaluate(test_dataset)print(metrics)# {'accuracy': 0.8691709844559585}# Push model to the Hubtrainer.push_to_hub("tomaarsen/setfit-paraphrase-mpnet-base-v2-sst2")# Download from Hubmodel=SetFitModel.from_pretrained("tomaarsen/setfit-paraphrase-mpnet-base-v2-sst2")# Run inferencepreds=model.predict(["i loved the spiderman movie!","pineapple on pizza is the worst 🤮"])print(preds)# ["positive", "negative"]Reproducing the results from the paperWe provide scripts to reproduce the results for SetFit and various baselines presented in Table 2 of our paper. Check out the setup and training instructions in thescripts/directory.Developer installationTo run the code in this project, first create a Python virtual environment using e.g. Conda:condacreate-nsetfitpython=3.9&&condaactivatesetfitThen install the base requirements with:pipinstall-e'.[dev]'This will install mandatory packages for SetFit likedatasetsas well as development packages likeblackandisortthat we use to ensure consistent code formatting.Formatting your codeWe useblackandisortto ensure consistent code formatting. After following the installation steps, you can check your code locally by running:make style && make qualityProject structure├── LICENSE
├── Makefile        <- Makefile with commands like `make style` or `make tests`
├── README.md       <- The top-level README for developers using this project.
├── docs            <- Documentation source
├── notebooks       <- Jupyter notebooks.
├── final_results   <- Model predictions from the paper
├── scripts         <- Scripts for training and inference
├── setup.cfg       <- Configuration file to define package metadata
├── setup.py        <- Make this project pip installable with `pip install -e`
├── src             <- Source code for SetFit
└── tests           <- Unit testsRelated workhttps://github.com/pmbaumgartner/setfit- A scikit-learn API version of SetFit.jxpress/setfit-pytorch-lightning- A PyTorch Lightning implementation of SetFit.davidberenstein1957/spacy-setfit- An easy and intuitive approach to use SetFit in combination with spaCy.Citation@misc{https://doi.org/10.48550/arxiv.2209.11055,doi={10.48550/ARXIV.2209.11055},url={https://arxiv.org/abs/2209.11055},author={Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},keywords={Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},title={Efficient Few-Shot Learning Without Prompts},publisher={arXiv},year={2022},copyright={Creative Commons Attribution 4.0 International}}