Microsoft Huggingface Transformers Ghloc

Leo Migdal

-Nov 18, 2025, 2:49 AM

microsoft huggingface transformers ghloc

🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0. State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation and more in over 100 languages. Its aim is to make cutting-edge NLP easier to use for everyone. 🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.

🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other. You can test most of our models directly on their pages from the model hub. We also offer private model hosting, versioning, & an inference API for public and private models. and get access to the augmented documentation experience 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. The models can be used across different modalities such as: Our library supports seamless integration between three of the most popular deep learning libraries: PyTorch, TensorFlow and JAX. Train your model in three lines of code in one framework, and load it for inference with another. Each 🤗 Transformers architecture is defined in a standalone Python module so they can be easily customized for research and experiments.

Access to this page requires authorization. You can try signing in or changing directories. Access to this page requires authorization. You can try changing directories. This article provides an introduction to Hugging Face Transformers on Azure Databricks. It includes guidance on why to use Hugging Face Transformers and how to install it on your cluster.

Hugging Face Transformers is an open-source framework for deep learning created by Hugging Face. It provides APIs and tools to download state-of-the-art pre-trained models and further tune them to maximize performance. These models support common tasks in different modalities, such as natural language processing, computer vision, audio, and multi-modal applications. Databricks Runtime for Machine Learning includes Hugging Face transformers in Databricks Runtime 10.4 LTS ML and above, and includes Hugging Face datasets, accelerate, and evaluate in Databricks Runtime 13.0 ML and above. 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. There was an error while loading. Please reload this page.

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!

Microsoft Huggingface Transformers Ghloc

People Also Search

🤗Transformers: State-of-the-art Natural Language Processing For Pytorch And TensorFlow 2.0.

🤗 Transformers Is Backed By The Three Most Popular Deep

🤗 Transformers Provides APIs To Easily Download And Train State-of-the-art

Access To This Page Requires Authorization. You Can Try Signing

Hugging Face Transformers Is An Open-source Framework For Deep Learning