Attention is all you need: Discovering the Transformer model
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Updated
Dec 22, 2021 - Jupyter Notebook
Attention is all you need: Discovering the Transformer model
HydraViT is a PyTorch implementation of the HydraViT model, an adaptive multi-branch transformer for multi-label disease classification from chest X-ray images. The repository provides the necessary code to train and evaluate the HydraViT model on the NIH Chest X-ray dataset.
Code for the runners up entry on the English subtask on the Shared-Task-On-Fighting the COVID-19 Infodemic, NLP4IF workshop, NAACL'21.
This repository contains code for implementing Vision Transformer (ViT) model for image classification
The Transformer model implemented from scratch using PyTorch. The model uses weight sharing between the embedding layers and the pre-softmax linear layer. Training on the Multi30k machine translation task is shown.
Collection of different types of transformers for learning purposes
完整的原版transformer程序,complete origin transformer program
Simple character level Transformer
Machine learning development toolkit built upon Transformer encoder network architectures and tailored for the realm of high-energy physics and particle-collision event analysis.
Transformer translator website with multithreaded web server in Rust
A Transformer Classifier implemented from Scratch.
This project aims to implement the Scaled-Dot-Product Attention layer and the Multi-Head Attention layer using various Positional Encoding methods.
Text matching using several deep models.
Image Captioning with Encoder as Efficientnet and Decoder as Decoder of Transformer combined with the attention mechanism.
A Basic Multi layered Neural Network, With Attention Masking Features
Pytorch Implementation of Transformers
Code and Datasets for the paper "A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing", published on Nature Machine Intelligence in 2021.
TensorFlow implementation of AlexNet with multi-headed Attention mechanism
EMNLP 2018: Multi-Head Attention with Disagreement Regularization; NAACL 2019: Information Aggregation for Multi-Head Attention with Routing-by-Agreement
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