Huggingface Transformers Examples Readme Md At Master Github

Leo Migdal

-Nov 18, 2025, 4:13 AM

huggingface transformers examples readme md at master github

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... There was an error while loading. Please reload this page. ⓘ You are viewing legacy docs. Go to latest documentation instead. Version 2.9 of 🤗 Transformers introduces a new Trainer class for PyTorch, and its equivalent TFTrainer for TF 2.

Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.2+. grouped by task (all official examples work for multiple models) with information on whether they are built on top of Trainer/TFTrainer (if not, they still work, they might just lack some features), whether they also include examples for pytorch-lightning, which is a great fully-featured, general-purpose training library for PyTorch, There was an error while loading. Please reload this page.

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.

Machine learning and the adoption of the Transformer architecture are rapidly growing and will revolutionize the way we live and work. From self-driving cars to personalized medicine, the applications of Transformers are limitless. In this blog post, we'll explore how the leverage and explore examples for Hugging Face Transformers from natural language processing to computer vision. Whether you're new to Hugging Face Transformers or an expert, this post is sure to provide valuable insights and inspiration. Hugging Face Transformers is a Python library of pre-trained state-of-the-art machine learning models for natural language processing, computer vision, speech, or multi-modalities. Transformers provides access to popular Transformer architecture, including BERT, GPT2, RoBERTa, VIT, Whisper, Wav2vec2, T5, LayoutLM, and CLIP.

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 We host a wide range of example scripts for multiple learning frameworks.

Simply choose your favorite: TensorFlow, PyTorch or JAX/Flax. We also have some research projects, as well as some legacy examples. Note that unlike the main examples these are not actively maintained, and may require specific older versions of dependencies in order to run. While we strive to present as many use cases as possible, the example scripts are just that - 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, allowing you to tweak and edit them as required.

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...

Huggingface Transformers Examples Readme Md At Master Github

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Running The Examples Requires PyTorch 1.3.1+ Or TensorFlow 2.2+. Grouped

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