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Changelog

Version 0.9.0

Release date: 05 Februari, 2025

Model2Vec

You can use Model2Vec for blazingly fast embedding as follows:

from keybert import KeyBERT
from model2vec import StaticModel

embedding_model = StaticModel.from_pretrained("minishlab/potion-base-8M")
kw_model = KeyBERT(embedding_model)

Light-weight KeyBERT

You can now install a light-weight KeyBERT with:

pip install keybert --no-deps scikit-learn model2vec

Fixes

Version 0.8.5

Release date: 14 June, 2024

Version 0.8.4

Release date: 15 Februari, 2024

  • Update default Cohere model to command by @sam-frampton in #194
  • Fix KeyLLM fails when no GPU is available by @igor-pechersky in #201
  • Fix AttributeError: 'tuple' object has no attribute 'page_content' in LangChain in #199

Version 0.8.3

Release date: 29 November, 2023

  • Fix support for openai>=1

You can now use it as follows:

import openai
from keybert.llm import OpenAI
from keybert import KeyLLM

# Create your LLM
client = openai.OpenAI(api_key=MY_API_KEY)
llm = OpenAI(client)

# Load it in KeyLLM
kw_model = KeyLLM(llm)

Version 0.8.2

Release date: 29 September, 2023

  • Fixed cuda error when using pre-calculated embeddings with KeyBERT + KeyLLM

Version 0.8.1

Release date: 29 September, 2023

  • Remove unnecessary print statements

Version 0.8.0

Release date: 29 September, 2023

Highlights:

  • Use KeyLLM to leverage LLMs for extracting keywords
  • Use it either with or without candidate keywords generated through KeyBERT
  • Multiple LLMs are integrated: OpenAI, Cohere, LangChain, HF, and LiteLLM
import openai
from keybert.llm import OpenAI
from keybert import KeyLLM

# Create your LLM
openai.api_key = "sk-..."
llm = OpenAI()

# Load it in KeyLLM
kw_model = KeyLLM(llm)

See here for full documentation on use cases of KeyLLM and here for the implemented Large Language Models.

Fixes:

  • Enable Guided KeyBERT for seed keywords differing among docs by @shengbo-ma in #152

Version 0.7.0

Release date: 3 November, 2022

Highlights:

  • Cleaned up documentation and added several visual representations of the algorithm (excluding MMR / MaxSum)
  • Added function to extract and pass word- and document embeddings which should make fine-tuning much faster
from keybert import KeyBERT

kw_model = KeyBERT()

# Prepare embeddings
doc_embeddings, word_embeddings = kw_model.extract_embeddings(docs)

# Extract keywords without needing to re-calculate embeddings
keywords = kw_model.extract_keywords(docs, doc_embeddings=doc_embeddings, word_embeddings=word_embeddings)

Do note that the parameters passed to .extract_embeddings for creating the vectorizer should be exactly the same as those in .extract_keywords.

Fixes:

  • Redundant documentation was removed by @mabhay3420 in #123
  • Fixed Gensim backend not working after v4 migration (#71)
  • Fixed candidates not working (#122)

Version 0.6.0

Release date: 25 July, 2022

Highlights:

  • Major speedup, up to 2x to 5x when passing multiple documents (for MMR and MaxSum) compared to single documents
  • Same results whether passing a single document or multiple documents
  • MMR and MaxSum now work when passing a single document or multiple documents
  • Improved documentation
  • Added 🤗 Hugging Face Transformers
from keybert import KeyBERT
from transformers.pipelines import pipeline

hf_model = pipeline("feature-extraction", model="distilbert-base-cased")
kw_model = KeyBERT(model=hf_model)
  • Highlighting support for Chinese texts
    • Now uses the CountVectorizer for creating the tokens
    • This should also improve the highlighting for most applications and higher n-grams

image

NOTE: Although highlighting for Chinese texts is improved, since I am not familiar with the Chinese language there is a good chance it is not yet as optimized as for other languages. Any feedback with respect to this is highly appreciated!

Fixes:

Version 0.5.1

Release date: 31 March, 2022

  • Added a page about leveraging CountVectorizer and KeyphraseVectorizers
    • Shoutout to @TimSchopf for creating and optimizing the package!
    • The KeyphraseVectorizers package can be found here
  • Fixed Max Sum Similarity returning incorrect similarities #92
  • Fixed out of bounds condition in MMR
  • Started styling with Flake8 and Black (which was long overdue)
    • Added pre-commit to make following through a bit easier with styling

Version 0.5.0

Release date: 28 September, 2021

Highlights:

  • Added Guided KeyBERT
    • kw_model.extract_keywords(doc, seed_keywords=seed_keywords)
    • Thanks to @zolekode for the inspiration!
  • Use the newest all-* models from SBERT

Miscellaneous:

  • Added instructions in the FAQ to extract keywords from Chinese documents
  • Fix typo in ReadMe by @koaning in #51

Version 0.4.0

Release date: 23 June, 2021

Highlights:

  • Highlight a document's keywords with:
    • keywords = kw_model.extract_keywords(doc, highlight=True)
  • Use paraphrase-MiniLM-L6-v2 as the default embedder which gives great results!

Miscellaneous:

  • Update Flair dependencies
  • Added FAQ

Version 0.3.0

Release date: 10 May, 2021

The two main features are candidate keywords and several backends to use instead of Flair and SentenceTransformers!

Highlights:

  • Use candidate words instead of extracting those from the documents (#25)
    • KeyBERT().extract_keywords(doc, candidates)
  • Spacy, Gensim, USE, and Custom Backends were added (see documentation here)

Fixes:

  • Improved imports
  • Fix encoding error when locally installing KeyBERT (#30)

Miscellaneous:

  • Improved documentation (ReadMe & MKDocs)
  • Add the main tutorial as a shield
  • Typos (#31, #35)

Version 0.2.0

Release date: 9 Feb, 2021

Highlights:

  • Add similarity scores to the output
  • Add Flair as a possible back-end
  • Update documentation + improved testing

Version 0.1.2

Release date: 28 Oct, 2020

Added Max Sum Similarity as an option to diversify your results.

Version 0.1.0

Release date: 27 Oct, 2020

This first release includes keyword/keyphrase extraction using BERT and simple cosine similarity. There is also an option to use Maximal Marginal Relevance to select the candidate keywords/keyphrases.