Mar 22, 2024 · What is Hugging Face Transformers? Hugging Face Transformers is an open-source Python library that provides access to thousands of pre-trained Transformers models for natural language processing (NLP), computer vision, audio tasks, and more.

Transformers is designed to be fast and easy to use so that everyone can start learning or building with transformer models. The number of user-facing abstractions is limited to only three classes for instantiating a model, and two APIs for inference or training. This quickstart introduces you to Transformers’ key features and shows you how to:

This course will teach you about large language models (LLMs) and natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub.

🤗 Transformers State-of-the-art Machine Learning for PyTorch, TensorFlow and JAX. 🤗 Transformers provides APIs to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you time from training a model from scratch.

🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between ...

Dec 19, 2024 · Ever wondered how transformers work under the hood? I recently took on the challenge of implementing the Transformer architecture from scratch, and I’ve just published a tutorial to share my journey!

Transformers provides the Trainer API, which offers a comprehensive set of training features, for fine-tuning any of the models on the Hub. Learn how to fine-tune models for other tasks in our Task Recipes section in Resources!

In Transformers, the generate() API handles text generation, and it is available for all models with generative capabilities. This guide will show you the basics of text generation with generate() and some common pitfalls to avoid.

If you aren’t familiar with finetuning a model with Keras, take a look at the basic tutorial here! To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

More specifically, we will look at the three main types of tokenizers used in 🤗 Transformers: Byte-Pair Encoding (BPE), WordPiece, and SentencePiece, and show examples of which tokenizer type is used by which model.