jayparks / Transformer

Neural Machine Translation with Keras

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

Witwicky: An implementation of Transformer in PyTorch.

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

My implementation of the original transformer model (Vaswani et al.). I've additionally included the playground.py file for visualizing otherwise seemingly hard concepts. Currently included IWSLT pretrained models.

A seq2seq with attention dialogue/MT model implemented by TensorFlow.

Transformers without Tears: Improving the Normalization of Self-Attention

A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN. (LARNN)

Implementations for a family of attention mechanisms, suitable for all kinds of natural language processing tasks and compatible with TensorFlow 2.0 and Keras.

Machine translation is the task of converting one language to other. Unlike the traditional phrase-based translation system which consists of many small sub-components that are tuned separately, neural machine translation attempts to build and train a single, large neural network that reads a sentence and outputs a correct translation.

Implementation of Linformer for Pytorch

Visualization for simple attention and Google's multi-head attention.

a simple and complete pytorch implementation of neural machine translation system

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

Code for Paper Abstract Writing through Editing Mechanism

Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GanFormer and TransGan paper

一些不同的Attention机制代码

Pytorch implementation of "Dynamic Coattention Networks For Question Answering"

🔖 An Attention Layer in Keras