# piskvorky/gensim

Topic Modelling for Humans

Repository: https://github.com/piskvorky/gensim
Canonical: https://ross.abutalabs.com/products/gensim
Homepage: https://radimrehurek.com/gensim
Language: Python
License: LGPL-2.1
License Family: copyleft
Topics: gensim, topic-modeling, information-retrieval, machine-learning, natural-language-processing, nlp, data-science, python, data-mining, word2vec, word-embeddings, neural-network, document-similarity, word-similarity, fasttext
Last push: 2025-11-01T12:01:39+00:00

## Health v2 (maintenance only)
Score: 50/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 50, release rhythm 20, longevity 100
- inputs: {"age_days": 5683, "days_push": 305, "days_rel": 321, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 16481, forks 4409 (observed 2026-08-28T04:11:16.550842+00:00)

## What it is
Gensim is a Python library for topic modelling, document indexing, and similarity retrieval over large text corpora. It provides memory-independent, parallelized implementations of algorithms like LDA, LSI, Word2Vec, Doc2Vec, and FastText for unsupervised semantic analysis of plain text.

## Use cases
- train word2vec embeddings on a large text corpus
- discover topics in documents with LDA
- find semantically similar documents to a query
- compute document similarity with doc2vec
- stream a corpus larger than RAM for training
- compare word similarity with fasttext embeddings
- summarize and analyze unstructured text

## When to choose
- you need battle-tested, memory-efficient topic modelling or embedding training in Python
- your corpus is too large to fit in RAM and needs streaming or distributed training
- you want mature, well-documented NLP algorithms with optimized C backends

## When to avoid
- you need cutting-edge transformer-based models or GPU acceleration
- you expect new features - the project is in stable maintenance mode accepting only bug fixes
- you need deep learning pipelines beyond classic vector-space algorithms

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, machine-learning, search-engine, data-science
- domain: machine-learning, data-science
- platform: python, windows, cross-platform
- tags: topic-modeling, word2vec, word-embeddings, lda, fasttext, doc2vec, document-similarity, information-retrieval, unsupervised-learning, streaming-corpus, natural-language-processing, search, linux, macos

## Member repositories
- piskvorky/gensim (main) score 50

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:16.550842+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:04:54.298317+00:00, confidence not recorded.
  - readme: https://github.com/piskvorky/gensim (fetched 2026-08-28T04:11:16.550842+00:00, sha a620a76873cc)
  - homepage: https://radimrehurek.com/gensim (fetched 2026-08-29T08:02:18.973219+00:00, sha 039a7c40d570)
  - site_page: https://radimrehurek.com/gensim/auto_examples/index.html (fetched 2026-08-29T08:02:18.982364+00:00, sha 67fdb6f0ffe5)
  - site_page: https://radimrehurek.com/gensim/intro.html (fetched 2026-08-29T08:02:18.984199+00:00, sha c4119e5e08ee)
- Data as of 2026-08-30T08:39:29.467469+00:00.