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Catalan BERTa (RoBERTa-base) finetuned for Part-of-speech-tagging (POS)

The roberta-base-ca-cased-pos is a Part-of-speech-tagging (POS) 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).


We used the POS dataset in Catalan from the Universal Dependencies Treebank we refer to Ancora-ca-pos for training and evaluation.

Evaluation and results

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

Model Ancora-ca-pos (F1)
roberta-base-ca-cased-pos 98.93
mBERT 98.82
XLM-RoBERTa 98.89
WikiBERT-ca 97.60

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


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

    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 = "",
    doi = "10.18653/v1/2021.findings-acl.437",
    pages = "4933--4946",

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