Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.

all kinds of text classification models and more with deep learning

Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

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

An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites

A TensorFlow Implementation of the Transformer: Attention Is All You Need

A simple but complete full-attention transformer with a set of promising experimental features from various papers

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)

Keras Attention Layer (Luong and Bahdanau scores).

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning

My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!

Reformer, the efficient Transformer, in Pytorch

Implementation of LambdaNetworks, a new approach to image recognition that reaches SOTA with less compute

Multilingual Automatic Speech Recognition with word-level timestamps and confidence

To eventually become an unofficial Pytorch implementation / replication of Alphafold2, as details of the architecture get released

基于金融-司法领域(兼有闲聊性质)的聊天机器人，其中的主要模块有信息抽取、NLU、NLG、知识图谱等，并且利用Django整合了前端展示,目前已经封装了nlp和kg的restful接口

Sequence-to-sequence framework with a focus on Neural Machine Translation based on PyTorch