Transformers At Main Huggingface Transformers Github

There was an error while loading. Please reload this page. English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Português | తెలుగు | Français | Deutsch | Italiano | Tiếng Việt | العربية | اردو | বাংলা... State-of-the-art pretrained models for inference and training Transformers acts as the model-definition framework for state-of-the-art machine learning with text, computer vision, audio, video, and multimodal models, for both inference and training. It centralizes the model definition so that this definition is agreed upon across the ecosystem.

transformers is the pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI, ...),... We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be simple, customizable, and efficient. and get access to the augmented documentation experience Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer vision, audio, video, and multimodal model, for both inference and training. It centralizes the model definition so that this definition is agreed upon across the ecosystem. transformers is the pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, …), inference engines (vLLM, SGLang, TGI, …),...

We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be simple, customizable, and efficient. There are over 1M+ Transformers model checkpoints on the Hugging Face Hub you can use. This repo contains various research projects using 🤗 Transformers. They are not maintained and require a specific version of 🤗 Transformers that is indicated in the requirements file of each folder. Updating them to the most recent version of the library will require some work. To use any of them, just run the command

If you need help with any of those, contact the author(s), indicated at the top of the README of each folder. and get access to the augmented documentation experience Transformers works with PyTorch. It has been tested on Python 3.9+ and PyTorch 2.2+. uv is an extremely fast Rust-based Python package and project manager and requires a virtual environment by default to manage different projects and avoids compatibility issues between dependencies. It can be used as a drop-in replacement for pip, but if you prefer to use pip, remove uv from the commands below.

Refer to the uv installation docs to install uv. and get access to the augmented documentation experience This folder contains actively maintained examples of use of 🤗 Transformers organized along NLP tasks. If you are looking for an example that used to be in this folder, it may have moved to the corresponding framework subfolder (pytorch, tensorflow or flax), our research projects subfolder (which contains frozen... While we strive to present as many use cases as possible, the scripts in this folder are just examples. It is expected that they won’t work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs.

To help you with that, most of the examples fully expose the preprocessing of the data. This way, you can easily tweak them. This is similar if you want the scripts to report another metric than the one they currently use: look at the compute_metrics function inside the script. It takes the full arrays of predictions and labels and has to return a dictionary of string keys and float values. Just change it to add (or replace) your own metric to the ones already reported. Please discuss on the forum or in an issue a feature you would like to implement in an example before submitting a PR: we welcome bug fixes but since we want to keep the...

and get access to the augmented documentation experience 🤗 transformers is a library maintained by Hugging Face and the community, for state-of-the-art Machine Learning for Pytorch, TensorFlow and JAX. It provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. We are a bit biased, but we really like 🤗 transformers! There are over 630,000 transformers models in the Hub which you can find by filtering at the left of the models page. You can find models for many different tasks:

You can try out the models directly in the browser if you want to test them out without downloading them thanks to the in-browser widgets! There was an error while loading. Please reload this page. Implementing a Decoder-Only Transformer from Scratch.

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