Gensim
Topic modeling and document similarity library for unsupervised NLP.
About
Gensim is a Python library for unsupervised topic modeling, document indexing, and similarity retrieval over large text collections. It implements Word2Vec, FastText, Doc2Vec, Latent Semantic Analysis, Latent Dirichlet Allocation, Random Projections, and Hierarchical Dirichlet Process, with multicore implementations of the popular algorithms and support for distributed computing. A defining design choice is data streaming: corpora are processed out of core, so models can train on collections larger than RAM. Created by Radim Rehurek and released under the LGPL-2.1 license, it depends mainly on NumPy plus an optimized BLAS for speed. The project is now in stable maintenance mode, accepting fixes rather than new features, and has been used in production at organizations including Amazon, Cisco, Capital One, and the National Institutes of Health for document similarity, fraud detection, and research on word embeddings.
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Details
- Category
- Natural Language Processing
- Price
- Free
- Platform
- Local/Desktop
- Difficulty
- Easy (2/5)
- License
- LGPL-2.1
- Added
- Apr 3, 2026
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