cemoody/lda2vec
None observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3905
- days_rel: n/a
- days_push: 1751
- n_releases_24m: 0
Adoption not part of the score
3169 stars · 622 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
A Python library implementing lda2vec, a hybrid topic model that combines word2vec word embeddings with LDA-style interpretable document topics. It supports supervised topics and topics over metadata like clients, times, and regions.
Use cases
- discover interpretable topics in a document corpus
- combine word embeddings with topic modeling
- model topics over documents, time, and metadata
- explore research topic models beyond LDA and word2vec
- analyze twenty newsgroups style text datasets
When to choose
- you need interpretable document topics plus word-relationship structure
- you want a research starting point for custom topic models
- both plain LDA and word2vec are inadequate for your task
When to avoid
- production systems - the author explicitly warns it is research software
- Windows environments
- you need well-maintained, heavily documented tooling
- modern GPU/PyTorch stacks - the codebase is dated
Facets
library · maturity maintenance
nlp machine-learning data-science machine-learning data-science python topic-modeling word-embeddings lda word2vec research-software unsupervised-learning natural-language-processing linux macos
1 source
- readme: https://github.com/cemoody/lda2vec · fetched 2026-08-28 · 97602b7efb95
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| cemoody/lda2vec | main | 32 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem