metadata

language:
  - ca
license: '???'
tags:
  - catalan
  - textual entailment
  - teca
  - CaText
  - Catalan Textual Corpus
datasets:
  - projecte-aina/teca
metrics:
  - accuracy
model-index:
  - name: roberta-base-ca-cased-te
    results:
      - task:
          type: text-classification
        dataset:
          type: projecte-aina/teca
          name: teca
        metrics:
          - type: accuracy
            value: 0.7912139892578125
widget:
  - text: M'agrades. T'estimo.
  - text: M'agrada el sol i la calor. A la Garrotxa plou molt.
  - text: El llibre va caure per la finestra. El llibre va sortir volant.
  - text: El meu aniversari és el 23 de maig. Faré anys a finals de maig.

Catalan RoBERTa-base trained on Catalan Textual Corpus fine-tuned for Catalan Textual Entailment.

The roberta-base-ca-cased-te is a Textual Entailment (TE) 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 TE dataset in Catalan called TECA for training and evaluation.

Evaluation and results

Below, the evaluation result on the TECA test set:

Task	TECA (accuracy)
BERTa	79.12
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",
}

Funding

TODO

Disclaimer

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