model_cards/article.md

# Model documentation & parameters

**Algorithm version**: The model version to use. Note that *any* HF model can be wrapped to a `KeyBERT` model.

**Text**: The main text prompt to "understand", i.e., generate keywords.

**Minimum keyphrase ngram**: Lower bound for phrase size. Each keyword will have at least this many words.

**Maximum keyphrase ngram**: Upper bound for phrase size. Each keyword will have at least this many words.

**Stop words**: Stopwords to remove from the document. If not provided, no stop words removal.

**Use MaxSum**: To diversify the results, we take the `2 x MaxSum candidates` most similar words/phrases to the document. Then, we take all top_n combinations from the `2 x MaxSum candidates` and extract the combination that are the least similar to each other by cosine similarity. Control usage of max sum similarity for keywords generated.

**MaxSum candidates**: Candidates considered when enabling `Use MaxSum`.

**Use Max. marginal relevance**: To diversify the results, we can use Maximal Margin Relevance (MMR) to create keywords / keyphrases which is also based on cosine similarity.

**Diversity**: Diversity for the results when enabling `max. marginal relevance`.

**Number of keywords**: How many keywords should be generated (maximal 50).


# Model card -- KeywordBERT

**Model Details**: KeyBERT is a minimal and easy-to-use keyword extraction technique that leverages BERT embeddings to create keywords and keyphrases that are most similar to a document.

**Developers**: Maarten Grootendorst.

**Distributors**: Original developer's code from [https://github.com/MaartenGr/KeyBERT](https://github.com/MaartenGr/KeyBERT).

**Model date**: 2020.

**Model type**: Different BERT and SciBERT models, trained on [CIRCA data](https://circa.res.ibm.com/index.html).

**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: 
N.A.

**Paper or other resource for more information**: 
The [KeyBERT GitHub repo](https://github.com/MaartenGr/KeyBERT).

**License**: MIT

**Where to send questions or comments about the model**: Open an issue on [GT4SD repository](https://github.com/GT4SD/gt4sd-core).

**Intended Use. Use cases that were envisioned during development**: N.A.

**Primary intended uses/users**: N.A.

**Out-of-scope use cases**: Production-level inference.

**Metrics**: N.A.

**Datasets**: N.A.

**Ethical Considerations**: Unclear, please consult with original authors in case of questions.

**Caveats and Recommendations**: Unclear, please consult with original authors in case of questions.

Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)

## Citation
```bib
@misc{grootendorst2020keybert,
  author       = {Maarten Grootendorst},
  title        = {KeyBERT: Minimal keyword extraction with BERT.},
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v0.3.0},
  doi          = {10.5281/zenodo.4461265},
  url          = {https://doi.org/10.5281/zenodo.4461265}
}
```