cbowdon/doc2vec-pytorch
Tutorial: implementing doc2vec (paragraph vectors) from scratch in PyTorch
This project helps you understand how document vectors are created from text. You put in a collection of text documents, and it shows you how to convert them into numerical representations that capture their meaning. This is for data scientists or researchers who want to learn the underlying mechanics of document embeddings.
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Use this if you are learning how to implement document embedding models from scratch and want to see a basic, step-by-step example in PyTorch.
Not ideal if you need a robust, production-ready tool for converting documents into vectors for serious analysis or application development.
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AGPL-3.0
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Apr 26, 2019
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