KeyBERT — BERT-Powered Keyword Extraction

KeyBERT — BERT-Powered Keyword Extraction

KeyBERT is a minimal, easy-to-use keyword extraction library that leverages BERT embeddings to find the keywords and keyphrases most similar to the overall document embedding. It is ideal for SEO keyword discovery, content gap analysis, tag generation, and topic modeling from marketing copy, blog posts, and product descriptions.

Key Features

• Single-function API: model.extract_keywords(doc)
• MMR (Maximal Marginal Relevance) for diverse, non-redundant keyword sets
• Max Sum Distance for global keyword diversity
• Candidate keyphrases via KeyphraseVectorizers or CountVectorizer
• Multilingual support via sentence-transformers multilingual models
• Compatible with any HuggingFace sentence-transformer checkpoint

Quick Start

pip install keybert

from keybert import KeyBERT

doc = """Supervised machine learning is used to classify marketing emails
         by topic, improving open rates and conversion funnels."""
kw_model = KeyBERT()
keywords = kw_model.extract_keywords(doc, keyphrase_ngram_range=(1, 2),
                                      stop_words='english', top_n=5)
print(keywords)
# [('machine learning', 0.68), ('marketing emails', 0.61), ...]

npx ai-supply add keybert-keyword-extraction

Curated mirror of the open-source KeyBERT (MIT). Get it from the source.