MilaNLProc/contextualized-topic-models

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1269 stars · 155 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python library implementing Contextualized Topic Models (CTM), which combine pre-trained contextual embeddings like BERT with neural topic models to produce more coherent topics. It offers CombinedTM (embeddings plus bag-of-words) and ZeroShotTM (works with missing words and supports multilingual, zero-shot topic modeling).

Use cases

• discover topics in a large document corpus
• run multilingual topic modeling without translating text
• improve topic coherence over LDA with BERT embeddings
• do zero-shot topic modeling on unseen languages
• analyze survey responses or news articles by theme
• cluster documents into interpretable topics

When to choose

• you need coherent, interpretable topics from text documents
• you want to leverage transformer embeddings for topic modeling
• you need multilingual or zero-shot topic modeling
• you want a pip-installable, research-backed topic modeling library

When to avoid

• you need simple keyword extraction rather than full topic models
• you lack a GPU or patience for training neural models
• you need production-scale low-latency inference
• you want a fully classical statistical topic model like LDA

Facets

library · maturity active

nlp machine-learning search-engine machine-learning data-science python topic-modeling bert embeddings neural-topic-models multilingual zero-shot text-analysis natural-language-processing

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