Zhkeybert

*Release date: 28 September, 2021*

**Highlights**:

* Added Guided KeyBERT
* kw_model.extract_keywords(doc, seed_keywords=seed_keywords)
* Thanks to [zolekode](https://github.com/zolekode) for the inspiration!
* Use the newest all-* models from SBERT

**Miscellaneous**:

* Added instructions in the FAQ to extract keywords from Chinese documents

*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

*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](https://github.com/MaartenGr/KeyBERT/issues/25))
* KeyBERT().extract_keywords(doc, candidates)
* Spacy, Gensim, USE, and Custom Backends were added (see documentation [here](https://maartengr.github.io/KeyBERT/guides/embeddings.html))

**Fixes**:

* Improved imports
* Fix encoding error when locally installing KeyBERT ([30](https://github.com/MaartenGr/KeyBERT/issues/30))

**Miscellaneous**:

* Improved documentation (ReadMe & MKDocs)
* Add the main tutorial as a shield
* Typos ([31](https://github.com/MaartenGr/KeyBERT/pull/31), [#35](https://github.com/MaartenGr/KeyBERT/pull/35))

*Release date: 9 Feb, 2021*

**Highlights**:

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

*Release date: 28 Oct, 2020*

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

*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.