In many Machine Learning applications, the amount of available labeled data is a barrier to producing a high-performing model. The latest developments in NLP show that you can overcome this limitation by providing a few examples at inference time with a large language model – a technique known as Few-Shot Learning. In this blog post, we’ll explain what Few-Shot Learning is, and    

Over the past few weeks, we’ve built collaborations with many Open Source frameworks in the machine learning ecosystem. One that gets us particularly excited is Sentence Transformers. Sentence Transformers is a framework for sentence, paragraph and image    

With the additional help of Quentin Lhoest and Sylvain Lesage. Modern language models often require a significant amount of compute for pretraining, making it impossible to obtain them without access to tens and hundreds of GPUs or    

Summer is now officially over and these last few months have been quite busy at Hugging Face. From new features in the Hub to research and Open Source development, our team has been working hard to empower the community through open and collaborative technology. In this blog post you’ll catch up on everything that happened at Hugging Face in June, July and August!

It’s so easy to demonstrate a Machine Learning project thanks to Gradio. In this blog post, we’ll walk you through: the recent Gradio integration that helps you demo models from the Hub seamlessly with few lines of code leveraging the Inference API. how to use Hugging Face Spaces to host demos of your own models.

Streamlit allows you to visualize datasets and build demos of Machine Learning models in a neat way. In this blog post we will walk you through hosting models and datasets and serving your Streamlit applications in Hugging Face Spaces. Building demos for