https://github.com/maxent-ai/lda2vec

Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec from this paper https://arxiv.org/abs/1605.02019
https://github.com/maxent-ai/lda2vec

chainer deep-learning embeddings lda nlp python3 sklearn text text-mining topic-modeling word-embeddings word2vec

Last synced: 27 days ago
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Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec from this paper https://arxiv.org/abs/1605.02019

• Host: GitHub
• URL: https://github.com/maxent-ai/lda2vec
• Owner: maxent-ai
• License: mit
• Archived: true
• Created: 2019-02-11T07:23:36.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-02-14T04:58:21.000Z (about 6 years ago)
• Last Synced: 2025-01-06T21:52:40.095Z (about 2 months ago)
• Topics: chainer, deep-learning, embeddings, lda, nlp, python3, sklearn, text, text-mining, topic-modeling, word-embeddings, word2vec
• Language: Jupyter Notebook
• Size: 89.8 KB
• Stars: 29
• Watchers: 3
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE-MIT

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README

        
# lda2vec

-----

**Table of Contents**

* [Installation](#installation)

* [License](#license)

[![Downloads](https://pepy.tech/badge/pylda2vec)](https://pepy.tech/project/pylda2vec)

## Installation

lda2vec is distributed on [PyPI](https://pypi.org) as a universal

wheel and is available on Linux/macOS and Windows and supports

Python 3.6+.

```bash

$ pip install pylda2vec

```

## License

lda2vec is distributed under the terms of the

[MIT License](https://choosealicense.com/licenses/mit).