brandokoch/pytorch-sequence-models
This repository implements a variety of sequence model architectures from scratch in PyTorch. Effort has been put to make the code well structured so that it can serve as learning material.
This project offers clear, structured implementations of sequence models like RNN, LSTM, and GRU in PyTorch. It helps machine learning students and practitioners understand how these models work by providing well-commented code for tasks such as sentiment classification and language modeling. You input text data and get a trained model that can classify sentiment or predict the next word.
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Use this if you are learning about recurrent neural networks and want to see practical, from-scratch implementations of core architectures for common text-based tasks.
Not ideal if you need a production-ready library for large-scale natural language processing or are not interested in the underlying implementation details.
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Jun 25, 2021
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