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bertopic

Route BERTopic topic modeling, embedding, vectorizer, labeling, visualization, and serialization workflows.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill
See how to install

Files included (36)

  • SKILL.md2.4 KB
  • references/repo-provenance.md1.4 KB
  • references/repo-routing-metadata.json408 B
  • references/troubleshooting.md4.5 KB
  • references/workflows.md2.5 KB
  • scripts/check_env.py6.7 KB
  • sub-skills/analysis-visualization/references/api-reference.md11.6 KB
  • sub-skills/analysis-visualization/references/troubleshooting.md7.2 KB
  • sub-skills/analysis-visualization/references/workflows.md6.3 KB
  • sub-skills/analysis-visualization/scripts/smoke_visualization.py11.1 KB
  • sub-skills/analysis-visualization/SKILL.md3.7 KB
  • sub-skills/embeddings-backends/references/api-reference.md6.2 KB
  • sub-skills/embeddings-backends/references/troubleshooting.md3.9 KB
  • sub-skills/embeddings-backends/references/workflows.md4.2 KB
  • sub-skills/embeddings-backends/scripts/inventory_backends.py11.5 KB
  • sub-skills/embeddings-backends/SKILL.md3.7 KB
  • sub-skills/representations-labeling/references/api-reference.md9.0 KB
  • sub-skills/representations-labeling/references/troubleshooting.md3.4 KB
  • sub-skills/representations-labeling/references/workflows.md2.6 KB
  • sub-skills/representations-labeling/scripts/smoke_representations.py8.8 KB
  • sub-skills/representations-labeling/SKILL.md3.3 KB
  • sub-skills/serialization/references/api-reference.md7.5 KB
  • sub-skills/serialization/references/troubleshooting.md7.5 KB
  • sub-skills/serialization/references/workflows.md7.7 KB
  • sub-skills/serialization/scripts/smoke_serialization.py10.3 KB
  • sub-skills/serialization/SKILL.md4.6 KB
  • sub-skills/topic-modeling/references/api-reference.md7.1 KB
  • sub-skills/topic-modeling/references/troubleshooting.md4.6 KB
  • sub-skills/topic-modeling/references/workflows.md6.0 KB
  • sub-skills/topic-modeling/scripts/smoke_topic_model.py7.8 KB
  • sub-skills/topic-modeling/SKILL.md2.4 KB
  • sub-skills/vectorizers-ctfidf/references/api-reference.md4.1 KB
  • sub-skills/vectorizers-ctfidf/references/troubleshooting.md4.7 KB
  • sub-skills/vectorizers-ctfidf/references/workflows.md5.7 KB
  • sub-skills/vectorizers-ctfidf/scripts/smoke_vectorizers.py9.6 KB
  • sub-skills/vectorizers-ctfidf/SKILL.md3.3 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

BERTopic

BERTopic turns documents, precomputed embeddings, or multimodal inputs into topic models you can fit, inspect, label, visualize, and save.

Install

python -m pip install bertopic

Use only the optional packages that the chosen workflow needs. For example, multimodal image workflows use bertopic[vision], while label and backend workflows may require openai, litellm, langchain, llama-cpp-python, spacy, fastembed, model2vec, gensim, flair, safetensors, or datamapplot.

If you are working from a local checkout to inspect the package, editable install is also fine:

python -m pip install -e .

Quick check

Run the bundled environment check first:

python scripts/check_env.py

Add --smoke for a tiny no-download fit/load-style smoke that uses synthetic documents and precomputed embeddings.

Route map

  • sub-skills/topic-modeling/ — build BERTopic models, fit and transform data, run partial_fit, mutate topics, and combine or reduce fitted models.
  • sub-skills/embeddings-backends/ — choose embedding backends, build custom embedders, inventory optional backend imports, and handle precomputed or multimodal embeddings.
  • sub-skills/vectorizers-ctfidf/ — tune ClassTfidfTransformer, CountVectorizer, and OnlineCountVectorizer for better topic words.
  • sub-skills/representations-labeling/ — rerank keywords, generate labels, chain representation models, and manage multi-aspect topic outputs.
  • sub-skills/analysis-visualization/ — inspect fitted models with topic tables, hierarchies, distributions, and plots.
  • sub-skills/serialization/ — save, reload, and share fitted models locally or through the Hugging Face Hub.

When a task spans more than one route, start with the earliest route in the pipeline and move forward: embeddings → model building → topic-word tuning → labels → analysis → serialization.

Read next

  • references/workflows.md for the fastest route through common BERTopic tasks.
  • references/troubleshooting.md when imports, optional dependencies, plotting, or save/load fail.
  • references/repo-provenance.md before deciding whether this skill matches the current checkout.

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