Check out this tutorial with the Notebook Companion: Understanding embeddings An embedding is a numerical representation of a piece of information, for example, text, documents, images, audio, etc. The representation captures the semantic    

TL;DR: Today we introduce Evaluation on the Hub, a new tool powered by AutoTrain that lets you evaluate any model on any dataset on the Hub without writing a single line of code! Evaluate all the models 🔥🔥🔥! Progress in AI has been nothing short of amazing, to the point where some people are now seriously debating whether AI models may be better than humans at certain tasks. However, that progress has not at all been even: to a machine […]

In this post we will look at how we can leverage the Accelerate library for training large models which enables users to leverage the ZeRO features of DeeSpeed.

⚠️ A new updated version of this article is available here 👉 https://huggingface.co/deep-rl-course/unit1/introduction This article is part of the Deep Reinforcement Learning Class. A free course from beginner to expert. Check the syllabus here. ⚠️ A new updated version of this article is available here 👉 https://huggingface.co/deep-rl-course/unit1/introduction This article is part of the Deep Reinforcement Learning    

Sentiment analysis is the automatic process of classifying text data according to their polarity, such as positive, negative and neutral. Companies leverage sentiment analysis of tweets to get a sense of how customers are talking about their products and services, get insights to drive business decisions, and identify product issues and potential PR crises early on. In this guide, we will cover    

Large language models (LLMs) have made a significant impact on AI research. These powerful, general models can take on a wide variety of new language tasks from a user’s instructions. However, academia, nonprofits and smaller companies’ research labs find it difficult to create, study, or even use LLMs as only a few industrial labs with the necessary resources    

A short while ago I published a playlist generator that I’d built using Sentence Transformers and Gradio, and I followed that up with a reflection on how I try to use my projects as effective learning experiences. But how did I actually build the playlist generator? In this post we’ll break down that project and look at two technical details:    

In recent years, training ever larger language models has become the norm. While the issues of those models’ not being released for further study is frequently discussed, the hidden knowledge about how to train such models rarely gets any attention. This article aims to change this by shedding some light on the technology and engineering behind training such models both in terms    

What you will learn here 💡the basic idea of dynamic adversarial data collection and why it is important. ⚒ how to collect adversarial data dynamically and train your model on them – using an MNIST handwritten digit recognition task as an