Code implementation of Transformer Model in "Attention is All You Need" in PyTorch.
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Updated
Jun 3, 2024 - Jupyter Notebook
Code implementation of Transformer Model in "Attention is All You Need" in PyTorch.
Transformers - Basics
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.
Comparison of positional encoding schemes in transformer
Positional encoding example
Transformer based chatbot based on "Attention is all you need"
Transformer translator website with multithreaded web server in Rust
Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding - Tensorflow
The Positional Encoder Decoder is a Visual Basic .NET class that provides functionality for encoding and decoding tokens and sentences using positional embeddings. It allows you to convert between string tokens and their corresponding embeddings, and vice versa.
A PyTorch Implementation of PGL-SUM from "Combining Global and Local Attention with Positional Encoding for Video Summarization", Proc. IEEE ISM 2021
Annotated vanilla implementation in PyTorch of the Transformer model introduced in 'Attention Is All You Need'.
Basis invariance synthetic experiment in Appendix D of NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
A Basic Corpus Object , Giving Positional Encoding / Decoding . ,A Fully Loaded Corpus = Corpus > Document > Sentences > Clauses > Words
This repo contains a basic transformer implementation
完整的原版transformer程序,complete origin transformer program
Codebase of paper "Balancing structure and position information in Graph Transformer network with a learnable node embedding"
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
Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
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