# lda-project/lda

Topic modeling with latent Dirichlet allocation using Gibbs sampling

Repository: https://github.com/lda-project/lda
Canonical: https://ross.abutalabs.com/products/lda
Homepage: https://lda.readthedocs.io/
Language: Python
License: MPL-2.0
License Family: copyleft
Last push: 2024-07-29T19:05:40+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4377, "days_push": 765, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1315, forks 390 (observed 2026-08-28T04:04:20.559643+00:00)

## What it is
A Python library implementing latent Dirichlet allocation (LDA) topic modeling via collapsed Gibbs sampling, with a scikit-learn-style API. It accepts document-term matrices (including sparse) and is fast and cross-platform.

## Use cases
- discover topics in a corpus of documents
- run LDA topic modeling on a document-term matrix
- extract topic-word distributions from text data
- cluster news articles by latent themes
- unsupervised text analysis in Python
- fit an LDA model with a scikit-learn-like fit/transform interface

## When to choose
- you need classic Gibbs-sampled LDA with a simple scikit-learn-compatible API
- you want a fast, well-tested Python topic modeling library for document-term matrices
- your project needs stable, maintenance-mode behavior without API churn

## When to avoid
- you need actively developed features or newer topic modeling methods (e.g., neural topic models, BERTopic)
- you need online/stochastic variational inference for very large streaming corpora
- you need GPU acceleration

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, nlp, data-science
- domain: machine-learning, data-science
- platform: python, windows, cross-platform
- tags: topic-modeling, latent-dirichlet-allocation, gibbs-sampling, scikit-learn-compatible, unsupervised-learning, natural-language-processing, linux, macos

## Member repositories
- lda-project/lda (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:20.559643+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 2026-08-30T04:48:41.622899+00:00, confidence not recorded.
  - readme: https://github.com/lda-project/lda (fetched 2026-08-28T04:04:20.559643+00:00, sha 41829458e347)
- Data as of 2026-08-30T08:39:29.467469+00:00.