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Catalan BERTa (RoBERTa-base) finetuned for Named Entity Recognition.

The roberta-base-ca-cased-ner is a Named Entity Recognition (NER) model for the Catalan language fine-tuned from the BERTa model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).

Datasets

We used the NER dataset in Catalan called Ancora-ca-ner for training and evaluation.

Evaluation and results

We evaluated the roberta-base-ca-cased-ner on the Ancora-ca-ner test set against standard multilingual and monolingual baselines:

Model Ancora-ca-ner (F1)
roberta-base-ca-cased-ner 88.13
mBERT 86.38
XLM-RoBERTa 87.66
WikiBERT-ca 77.66

For more details, check the fine-tuning and evaluation scripts in the official GitHub repository.

Citing

If you use any of these resources (datasets or models) in your work, please cite our latest paper:

@inproceedings{armengol-estape-etal-2021-multilingual,
    title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
    author = "Armengol-Estap{\'e}, Jordi  and
      Carrino, Casimiro Pio  and
      Rodriguez-Penagos, Carlos  and
      de Gibert Bonet, Ona  and
      Armentano-Oller, Carme  and
      Gonzalez-Agirre, Aitor  and
      Melero, Maite  and
      Villegas, Marta",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.437",
    doi = "10.18653/v1/2021.findings-acl.437",
    pages = "4933--4946",
}
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Dataset used to train projecte-aina/roberta-base-ca-cased-ner

Evaluation results