计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
topic-modeling【效率与协作】Build, fit, query, and mutate BERTopic topic models.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this sub-skill for BERTopic lifecycle work when the task is about building or changing a topic model rather than choosing embeddings, tuning c-TF-IDF/vectorizers, labeling topics, plotting, or saving models.
fit_transform, transform, and partial_fit on documents, precomputed embeddings, or image-aligned inputs.get_params.fit / fit_transform accept documents, optional embeddings, optional images, and optional y.partial_fit is the streaming path; use a reducer and clusterer that support partial_fit.transform only works on fitted models with an inference-capable cluster path.find_topics needs a live embedding_model; if you only have precomputed embeddings, provide a local offline embedder or route the request elsewhere.reduce_outliers returns a new topic list; if you need refreshed representations afterward, hand that job to the vectorizer/representation sub-skill.merge_topics, delete_topics, and reduce_topics mutate the fitted model in place and update topic_mapper_.partial_fit tracks the current batch in topics_; keep your own history if you need a cumulative stream view.images are supported for fit-time and transform-time multimodal workflows; partial_fit does not take images.references/api-reference.mdreferences/workflows.mdreferences/troubleshooting.mdscripts/smoke_topic_model.py