Implementation of Rotary Embeddings, from the Roformer paper, in Pytorch
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Updated
Sep 2, 2024 - Python
Implementation of Rotary Embeddings, from the Roformer paper, in Pytorch
Transformer based chatbot based on "Attention is all you need"
Transformers - Basics
This repository provides a complete workflow for text processing using Hugging Face Transformers and NLTK. It includes modules for sentence normalization, spelling correction, word embedding generation, positional encoding computation, and English-to-French translation
Code implementation of Transformer Model in "Attention is All You Need" in PyTorch.
Developed the ViViT model for medical video classification, enhancing 3D organ image analysis using transformer-based architectures.
"Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding" Zhenyu Zhang, Runjin Chen, Shiwei Liu, Zhewei Yao, Olatunji Ruwase, Beidi Chen, Xiaoxia Wu, Zhangyang Wang.
PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces (CVPR 2023)
Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding
Unofficial pytorch implementation of the paper "Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding", NeurIPS 2021.
This repo contains a basic transformer implementation
完整的原版transformer程序,complete origin transformer program
Annotated vanilla implementation in PyTorch of the Transformer model introduced in 'Attention Is All You Need'.
Codebase of paper "Balancing structure and position information in Graph Transformer network with a learnable node embedding"
Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
This is a novel Transformer network based approach to distinguish ChatGPT generated Text from Human text. The model was also deployed on local server using Flask where Docker was used to manage all dependencies.
Code for "The Locality and Symmetry of Positional Encodings" EMNLP Findings
Comparison of positional encoding schemes in transformer
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