The Elixir community is glad to announce the arrival of several Neural Networks models, from GPT2 to Stable Diffusion, to Elixir. This is possible thanks to the just announced Bumblebee library, which is an implementation of Hugging Face Transformers in pure Elixir. To help anyone get started with those models, the team behind Livebook – a    

This article has been translated to Chinese 简体中文 and Vietnamese đọc tiếng việt. Language models have shown impressive capabilities in the past few years by generating diverse and compelling text from human input prompts. However, what makes a “good” text is inherently hard to define as it is subjective and context dependent. There are many applications such as writing stories where you want creativity, pieces of informative text which should be truthful, or code snippets that we want to be […]

🤗 Datasets is an open-source library for downloading and preparing datasets from all domains. Its minimalistic API allows users to download and prepare datasets in just one line of Python code, with a suite of functions that enable efficient pre-processing. The number of datasets available is unparalleled, with all the most popular machine learning    

Model cards are an important documentation framework for understanding, sharing, and improving machine learning models. When done    

This guide shows how you can use CLIPSeg, a zero-shot image segmentation model, using 🤗 transformers. CLIPSeg creates rough segmentation masks that can be used for robot perception, image    

About a year ago, we showed you how to distribute the training of Hugging Face transformers on a cluster or third-generation Intel Xeon Scalable CPUs (aka Ice Lake). Recently, Intel has launched the fourth generation of Xeon CPUs, code-named Sapphire Rapids, with exciting new instructions that speed up operations commonly found in deep learning models. In this post, you    

In this blog post, we cover the basics of graph machine learning. We first study what graphs are, why they are used, and how best to represent them. We then cover briefly how people learn on graphs, from pre-neural methods (exploring graph features at the same time) to what are commonly called Graph Neural Networks. Lastly, we peek into the world of