all kinds of text classification models and more with deep learning

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

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

Attention mechanism Implementation for Keras.

Speech synthesis, voice conversion, self-supervised learning, music generation,Automatic Speech Recognition, Speaker Verification, Speech Synthesis, Language Modeling

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

Text classifier for Hierarchical Attention Networks for Document Classification

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

TensorFlow Implementation of "Show, Attend and Tell"

Visualizing RNNs using the attention mechanism

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

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

Neural Machine Translation with Keras

A Structured Self-attentive Sentence Embedding

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

Action recognition using soft attention based deep recurrent neural networks

Transformer of "Attention Is All You Need" (Vaswani et al. 2017) by Chainer.

Implementation of "Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning"

Code for PaperRobot: Incremental Draft Generation of Scientific Ideas

End-to-End speech recognition implementation base on TensorFlow (CTC, Attention, and MTL training)

基于seq2seq模型的简单对话系统的tf实现，具有embedding、attention、beam_search等功能，数据集是Cornell Movie Dialogs

A curated list of NLP resources focused on BERT, attention mechanism, Transformer networks, and transfer learning.

Attention mechanism for processing sequential data that considers the context for each timestamp.

Code/Model release for NIPS 2017 paper "Attentional Pooling for Action Recognition"

Pointer-generator reinforced seq2seq summarization in PyTorch

Keras implementation of the graph attention networks (GAT) by Veličković et al. (2017; https://arxiv.org/abs/1710.10903)

A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).

Sparse and structured neural attention mechanisms