KeyBERT Minimal Keyword Extraction with BERT Embeddings

KeyBERT is a minimal and easy-to-use Python library that leverages BERT embeddings and cosine similarity to extract keywords and keyphrases from documents. It supports multiple embedding backends including sentence-transformers, Flair, and spaCy, with built-in diversity algorithms like Max Sum Similarity and Maximal Marginal Relevance.

KeyBERT Minimal Keyword Extraction with BERT Embeddings

KeyBERT is a minimal and easy-to-use Python library that leverages BERT embeddings and cosine similarity to extract keywords and keyphrases from documents. It supports multiple embedding backends including sentence-transformers, Flair, and spaCy, with built-in diversity algorithms like Max Sum Similarity and Maximal Marginal Relevance.

Installation

Method 1, Agent Skill Exchange

Method 2, Git clone

git clone https://github.com/agentskillexchange/skills.git && cd skills/skills/keybert-keyword-extraction-bert

Method 3, Download ZIP

  • Download the repository ZIP and extract skills/keybert-keyword-extraction-bert.

Method 4, Manual copy

  • Copy this skill folder into your local skills directory, then reload your agent tooling.

Method 5, Fork and sync

  • Fork the repository if you want to maintain local edits while syncing upstream changes.

Source