Stars
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Making large AI models cheaper, faster and more accessible
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
PyTorch Tutorial for Deep Learning Researchers
Code for the paper "Language Models are Unsupervised Multitask Learners"
A PyTorch implementation of the Transformer model in "Attention is All You Need".
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
High accuracy RAG for answering questions from scientific documents with citations
A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS, 海量中文预训练ALBERT模型
A tool for extracting plain text from Wikipedia dumps
[EMNLP 2021] SimCSE: Simple Contrastive Learning of Sentence Embeddings https://arxiv.org/abs/2104.08821
PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)
Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.
Self-Supervised Speech Pre-training and Representation Learning Toolkit
Multi-Task Deep Neural Networks for Natural Language Understanding
Chinese NER using Lattice LSTM. Code for ACL 2018 paper.
Pytorch implementation of various Knowledge Distillation (KD) methods.
Data augmentation for NLP, presented at EMNLP 2019
A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. 🏆25 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemen…
PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet
A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
Human ChatGPT Comparison Corpus (HC3), Detectors, and more! 🔥
Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation
PyTorch code for EMNLP 2019 paper "LXMERT: Learning Cross-Modality Encoder Representations from Transformers".