v0.8 (#180)

* Added `KeyLLM` to extract keywords from text with LLMs across five use cases:
1. Create Keywords with KeyLLM
2. Extract Keywords with KeyLLM
3. Fine-tune Candidate Keywords
4. Efficient KeyLLM
5. Efficient KeyLLM + KeyBERT
* Integrated different LLM backends (OpenAI, Cohere, HF, LangChain, LiteLLM)

v0.7 (#135)

* Added option to extract and pass word/document embeddings for faster iteration
* Focused on making the documentation a bit nicer (visualizations, etc. )
* Fixed #71
* Fixed #122, #136

v0.6.0 (#120)

* 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
* 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
* Fix #106 
* Fix #116

v0.5.1 (#97)

* Added CountVectorizer tips and tricks page, including `KeyphraseVectorizers`
* Added general styling: `black`, `flake8`, `pre-commit`

v0.5 (#58)

* Guided KeyBERT
* Update default SBERT model

v0.4 (#43)

* Use paraphrase-MiniLM-L6-v2 as the default embedding model
* Highlight a document's keywords
* Added FAQ

v0.3 (#32)

* Use candidate words instead of extracting those from the documents
* Spacy, Gensim, USE, and Custom Backends were added
* Improved imports
* Fix encoding error when locally installing KeyBERT #30
* Improved documentation (ReadMe & MKDocs)
* Add the main tutorial as a shield
* Typos #31, #35

v0.2 (#23)

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

Include n_gram_range examples

Add diversity parameter in max sum sim (#7)

* Add diversity parameter in max sum sim
* Remove 3.7 testing due to timeout errors