Huggingface Transformers Research Projects Github

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. 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. There was an error while loading.

Please reload this page. There was an error while loading. Please reload this page. This is a list of some wonderful open-source projects & applications integrated with Hugging Face libraries. First-party cool stuff made with ❤️ by 🤗 Hugging Face. Learn how to use Hugging Face toolkits, step-by-step.

NLP toolkits built upon Transformers. Swiss Army! Highly optimized inference engines implementing Transformers-compatible APIs. There was an error while loading. Please reload this page. 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... ⓘ You are viewing legacy docs. Go to latest documentation instead.

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 provides many example training scripts for PyTorch and tasks in transformers/examples.

There are additional scripts in transformers/research projects and transformers/legacy, but these aren’t actively maintained and requires a specific version of Transformers. Example scripts are only examples and you may need to adapt the script to your use-case. To help you with this, most scripts are very transparent in how data is preprocessed, allowing you to edit it as necessary. For any feature you’d like to implement in an example script, please discuss it on the forum or in an issue before submitting a pull request. While we welcome contributions, it is unlikely a pull request that adds more functionality is added at the cost of readability. This guide will show you how to run an example summarization training script in PyTorch.

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