SetFit is significantly more sample efficient and robust to noise than standard fine-tuning. Few-shot learning with pretrained language models has emerged as a promising solution to every data scientist’s nightmare: dealing with data that has few to no labels 😱. Together with our research partners at Intel Labs and the UKP Lab, Hugging Face is excited to introduce SetFit: an efficient framework for few-shot fine-tuning of Sentence Transformers. SetFit achieves high accuracy with little labeled data – for example, with […]

Meta AI and BigScience recently open-sourced very large language models which won’t fit into memory (RAM or GPU) of most consumer hardware. At Hugging Face, part of our mission is to make even those large models accessible, so we developed tools to allow you to run those models even if you don’t own a supercomputer. All the examples picked in this blog    

Large language models can now be evaluated on zero-shot classification tasks with Evaluation on the Hub! Zero-shot evaluation is a popular way for researchers to measure the performance of large language models, as they have been shown to learn capabilities during training without explicitly being shown labeled examples. The Inverse Scaling Prize is an example of a recent community effort to conduct large-scale zero-shot evaluation across model sizes and families to discover tasks on which larger models may perform worse […]

Our mission at Hugging Face is to democratize good machine learning. That includes best practices that make ML models and datasets more reproducible, better documented, and easier to use and share. To solve this challenge, we’re excited to announce that you can now generate a DOI for your model or dataset directly from the Hub! DOIs can be generated directly from your repo settings, and anyone will then be able to cite your work by clicking “Cite this model/dataset” on […]

This article gives you the behind-the-scenes of how we made an efficient inference server that powers bloom. inference server that powers https://huggingface.co/bigscience/bloom. We achieved a 5x latency reduction over several weeks (and 50x more throughput). We wanted to share all the struggles and epic wins we went through to achieve such speed improvements. A lot of different people were involved    

🤗 Hugging Face Diffusers supports Flax since version 0.5.1! This allows for super fast inference on Google TPUs, such as those available in Colab, Kaggle or Google    

MTEB is a massive benchmark for measuring the performance of text embedding models on diverse embedding tasks. The 🥇 leaderboard provides a holistic view of the best text embedding models out there on a variety of tasks. The 📝 paper gives background on the tasks and datasets in MTEB and analyzes leaderboard results! The 💻 Github