metadata

language:
  - ca
tags:
  - catalan
  - text classification
  - tecla
  - CaText
  - Catalan Textual Corpus
datasets:
  - projecte-aina/tecla
metrics:
  - accuracy
model-index:
  - name: roberta-base-ca-cased-tc
    results:
      - task:
          type: text-classification
        dataset:
          name: TeCla
          type: projecte-aina/tecla
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.740388810634613
widget:
  - text: Els Pets presenten el seu nou treball al Palau Sant Jordi.
  - text: >-
      Els barcelonins incrementen un 23% l’ús del cotxe des de l’inici de la
      pandèmia.
  - text: >-
      Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de
      Catalunya.
  - text: >-
      Majors de 60 anys i sanitaris començaran a rebre la tercera dosi de la
      vacuna covid els propers dies.
  - text: Els cinemes Verdi estrenen Verdi Classics, un nou canal de televisió.

Catalan BERTa (roberta-base-ca) finetuned for Text Classification.

Model Description
Intended Uses and Limitations
How to Use
Training
- Training Data
- Training Procedure
Evaluation
- Variable and Metrics
- Evaluation Results
Licensing Information
Citation Information
Funding
Contributions
Disclaimer

Model description

The roberta-base-ca-cased-tc is a Text Classification (TC) model for the Catalan language fine-tuned from the roberta-base-ca model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers.

Intended Uses and Limitations

roberta-base-ca-cased-tc model can be used to classify texts. The model is limited by its training dataset and may not generalize well for all use cases.

How to Use

Here is how to use this model:

from transformers import pipeline
from pprint import pprint

nlp = pipeline("text-classification", model="projecte-aina/roberta-base-ca-cased-tc")
example = "Retards a quatre línies de Rodalies per una avaria entre Sants i plaça de Catalunya."

tc_results = nlp(example)
pprint(tc_results)

Training

Training data

We used the TC dataset in Catalan called TeCla for training and evaluation.

Training Procedure

The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.

Evaluation

Variable and Metrics

This model was finetuned maximizing accuracy.

Evaluation results

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

Model	TeCla (accuracy)
roberta-base-ca-cased-tc	74.04
mBERT	70.56
XLM-RoBERTa	71.68
WikiBERT-ca	73.22

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

Licensing Information

Apache License, Version 2.0

Citation Information

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

This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.

Contributions

[N/A]

Disclaimer

Click to expand

The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.

When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.

In no event shall the owner and creator of the models (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.

projecte-aina
/

roberta-base-ca-cased-tc