⚠️ 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    

⚠️ 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 Class.    

In the past few months, the Hugging Face team and external contributors added a variety of vision models in TensorFlow to Transformers. This list is growing comprehensively and already includes state-of-the-art pre-trained models like Vision Transformer, Masked Autoencoders, RegNet, ConvNeXt, and many others! When it comes to deploying TensorFlow models,    

TL;DR: Text Generation on 🤗 transformers using TensorFlow can now be compiled with XLA. It is up to 100x faster than before, and even faster than PyTorch — check the colab below! Text Generation