Keybert Keyword Extraction Bert

---
name: "KeyBERT Minimal Keyword Extraction with BERT Embeddings"
slug: "keybert-keyword-extraction-bert"
description: "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."
github_stars: 4143
verification: "security_reviewed"
source: "https://github.com/MaartenGr/KeyBERT"
category: "Content Writing & SEO"
framework: "Custom Agents"
tool_ecosystem:
  github_repo: "MaartenGr/KeyBERT"
  github_stars: 4143
---

# 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

Use the upstream install or setup path that matches your environment:
- pip install keybert
- pip install keybert[flair]
- pip install keybert[gensim]
- pip install keybert[spacy]

Requirements and caveats from upstream:
- [![PyPI - Python](https://img.shields.io/badge/python-3.10+-blue.svg)](https://pypi.org/project/keybert/)
- python

Basic usage or getting-started notes:
- [Getting Started](#gettingstarted)
- 2.2. [Basic Usage](#usage)
- Thus, the goal was a pip install keybert and at most 3 lines of code in usage.

- Source: https://github.com/MaartenGr/KeyBERT
- Extracted from upstream docs: https://raw.githubusercontent.com/MaartenGr/KeyBERT/HEAD/README.md

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/keybert-keyword-extraction-bert/)