Python · MIT
MilaNLProc/contextualized-topic-models
A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021 (Bianchi et al.).
★ 1.3k
Stars
155 forks · 11 open issues
flat
Growth · measured
Stars counted between two of our own snapshots roughly a week apart. The repository page gives the exact span.
9.0
Momentum / 100
see the breakdown below
6y ago
Created
last push 15mo ago
Star history
How this score was built
| Component | Weight | Earned | Why |
|---|---|---|---|
| Velocity | 55 | 0.0 | flat, log-scaled against a 750/day ceiling |
| Acceleration | 15 | 0.0 | needs 7-day and 30-day rates side by side |
| Freshness | 15 | 0.0 | last push 15mo ago; decays to zero at 180 days |
| Scale | 15 | 9.0 | 1.3k stars, log-scaled against 150k — stops a 3-star repo topping the board |
| Momentum | 100 | 9.0 | sum of the above |
Detail
| Topics | bertembeddingsmultilingual-modelsmultilingual-topic-modelsneural-topic-modelsnlpnlp-librarynlp-machine-learningtext-as-datatopic-coherencetopic-modelingtransformer |
| Matched on | RAG (embeddings) |
| Short window | +0 stars over 7 days = 0.0/day |
| Long window | +3 stars over 26 days = 0.1/day |
| Lifetime average | 0.53 stars/day over 2,406 days |
| First seen here | 2026-10-11 |
| Last read | 2d ago |