🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

TensorFlow code and pre-trained models for BERT

PyTorch Tutorial for Deep Learning Researchers

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source …

Graph Neural Network Library for PyTorch

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

Download market data from Yahoo! Finance's API

🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools

A very simple framework for state-of-the-art Natural Language Processing (NLP)

An open source implementation of CLIP.

An open-source NLP research library, built on PyTorch.

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data

A PyTorch implementation of the Transformer model in "Attention is All You Need".

Repo for external large-scale work

Google AI 2018 BERT pytorch implementation

A natural language modeling framework based on PyTorch

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

A collection of important graph embedding, classification and representation learning papers with implementations.

Sequence modeling benchmarks and temporal convolutional networks

Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)

Representation learning on large graphs using stochastic graph convolutions.

Models, data loaders and abstractions for language processing, powered by PyTorch