{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"BERTopic performs topic modeling on text documents using transformer embeddings and c-TF-IDF clustering to identify and label coherent topics with interpretable keywords.","skillfed_tags":["nlp","clustering","embeddings"],"use_cases":["Discover latent topics in large document collections to understand content themes without manual labeling.","Track how topics evolve over time in streaming or time-series text data using dynamic topic modeling.","Build supervised or semi-supervised topic models when partial labels or guidance are available for training.","Generate automatic topic summaries and labels using LLM integration for business intelligence.","Cluster and organize multimodal data to find cross-modal semantic patterns.","Extract interpretable topics from domain-specific corpora with custom embedding models."],"what_it_does":"BERTopic is a topic modeling library that combines transformer-based embeddings with density-based clustering to discover and label topics in text collections. It uses sentence-transformers to encode documents into dense vectors, applies umap-learn for dimensionality reduction, and hdbscan for clustering, then generates interpretable topic descriptions using c-TF-IDF. The library supports multiple modeling variants including supervised, semi-supervised, hierarchical, dynamic, and multimodal approaches, making it flexible for different use cases from exploratory analysis to production systems.\n\nThe package integrates with pandas, numpy, and plotly for data handling and visualization. It can work with custom embedding models and supports fine-tuning topic representations through pluggable representation strategies, including integration with LLMs for generating human-readable topic summaries. Installation is straightforward with optional extras for alternative embedding backends or vision capabilities.","worth_installing":"Yes. BERTopic is actively maintained, has low install friction, carries permissive MIT licensing, and no known vulnerabilities. It is well-suited for anyone needing interpretable topic discovery from text\u2014from research prototyping to production systems. The dependency set is standard in data science workflows and adds no unusual risk. Start with it if you need flexible, modern topic modeling."},"id":"bertopic","links":{"html":"https://skillfed.io/packages/bertopic","md":"https://skillfed.io/packages/bertopic.md","pypi":"https://pypi.org/project/bertopic/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-12-03","license_spdx":null,"license_treatment":"permissive","name":"bertopic","python_support":"supports_current","summary":"BERTopic performs topic Modeling with state-of-the-art transformer models."},"popularity":{"monthly_downloads":529219,"position":6161,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.17.4"}