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bertopic

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill
설치 방법 보기

포함된 파일(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(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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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