coreweave/transformers

coreweave/transformers

Description: 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Language: Python

License: Apache-2.0

Stars: 0

Forks: 1

Open issues: 0

Created: 2024-02-26T19:25:23Z

Pushed: 2024-09-10T23:31:12Z

Default branch: main

Fork: yes

Parent repository: huggingface/transformers

Archived: no

README:

English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Рortuguês | తెలుగు | Français | Deutsch | Tiếng Việt | العربية |

State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow

🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

These models can be applied on:

• 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages.
• 🖼️ Images, for tasks like image classification, object detection, and segmentation.
• 🗣️ Audio, for tasks like speech recognition and audio classification.

Transformer models can also perform tasks on several modalities combined, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.

🤗 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.

Online demos

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.

Here are a few examples:

In Natural Language Processing:

• Masked word completion with BERT
• Named Entity Recognition with Electra
• Text generation with Mistral
• Natural Language Inference with RoBERTa
• Summarization with BART
• Question answering with DistilBERT
• Translation with T5

In Computer Vision:

• Image classification with ViT
• Object Detection with DETR
• Semantic Segmentation with SegFormer
• Panoptic Segmentation with Mask2Former
• Depth Estimation with Depth Anything
• Video Classification with VideoMAE
• Universal Segmentation with OneFormer

In Audio:

• Automatic Speech Recognition with Whisper
• Keyword Spotting with Wav2Vec2
• Audio Classification with Audio Spectrogram Transformer

In Multimodal tasks:

• Table Question Answering with TAPAS
• Visual Question Answering with ViLT
• [Image captioning with…