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
The original transformer implementation from scratch. It contains informative comments on each block
Code for the runners up entry on the English subtask on the Shared-Task-On-Fighting the COVID-19 Infodemic, NLP4IF workshop, NAACL'21.
Simple character level Transformer
完整的原版transformer程序,complete origin transformer program
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
Transformer translator website with multithreaded web server in Rust
A Transformer Classifier implemented from Scratch.
Machine learning development toolkit built upon Transformer encoder network architectures and tailored for the realm of high-energy physics and particle-collision event analysis.
This project aims to implement the Scaled-Dot-Product Attention layer and the Multi-Head Attention layer using various Positional Encoding methods.
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.
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
Decoding Attention is specially optimized for multi head attention (MHA) using CUDA core for the decoding stage of LLM inference.
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.
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