inejc/paragraph-vectors
A PyTorch implementation of Paragraph Vectors (doc2vec) for generating vector representations of text documents.
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This library implements the Paragraph Vectors algorithm (also known as doc2vec) for creating dense vector representations of variable-length text documents. It minimizes the Negative Sampling objective as proposed by Mikolov et al., enabling efficient sparse updates during training. The implementation supports parallel batch generation on CPU while training on GPU, providing a flexible tool for unsupervised document embedding generation.
Frequently asked
- What is inejc/paragraph-vectors?
- A PyTorch implementation of Paragraph Vectors (doc2vec) for generating vector representations of text documents.
- Is paragraph-vectors open source?
- Yes — inejc/paragraph-vectors is open source, released under the MIT license.
- What language is paragraph-vectors written in?
- inejc/paragraph-vectors is primarily written in Python.
- How popular is paragraph-vectors?
- inejc/paragraph-vectors has 415 stars on GitHub.
- Where can I find paragraph-vectors?
- inejc/paragraph-vectors is on GitHub at https://github.com/inejc/paragraph-vectors.