ddangelov/Top2Vec

Health v2 · maintenance only

Adoption not part of the score

3102 stars · 374 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Top2Vec is a Python library that learns jointly embedded topic, document, and word vectors for topic modeling and semantic search. It supports transformer-based embedding models and a contextual mode that finds multiple topics and topic segments within documents.

Use cases

• discover topics in a document corpus
• find semantically similar documents
• search documents by topic
• generate word and document embeddings for text
• segment a long document into topic spans
• get topic distributions per document

When to choose

• you need unsupervised topic modeling without specifying the number of topics
• you want joint topic, document, and word embeddings
• you want semantic search over a text corpus
• you need multi-topic or per-document topic segmentation

When to avoid

• you need a simple keyword or full-text search engine
• you need heavily supervised text classification
• you cannot run transformer embedding models locally

Facets

library · maturity stable

nlp machine-learning search-engine data-science machine-learning data-science python cross-platform topic-modeling word-embeddings document-embedding semantic-search sentence-transformers bert natural-language-processing search

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markdown · JSON · MCP: product_card(name="ddangelov/Top2Vec")