metadata

language:
  - ca
license: apache-2.0
tags:
  - catalan
  - part of speech tagging
  - pos
  - CaText
  - Catalan Textual Corpus
datasets:
  - universal_dependencies
metrics:
  - f1
inference:
  parameters:
    aggregation_strategy: first
model-index:
  - name: roberta-base-ca-cased-pos
    results:
      - task:
          type: token-classification
        dataset:
          type: universal_dependencies
          name: Ancora-ca-POS
        metrics:
          - name: F1
            type: f1
            value: 0.9893832385244624
widget:
  - text: Em dic Lluïsa i visc a Santa Maria del Camí.
  - text: L'Aina, la Berta i la Norma són molt amigues.
  - text: El Martí llegeix el Cavall Fort.

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

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-pos is a Part-of-speech-tagging (POS) 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-pos model can be used to Part-of-speech-tagging (POS) a text. 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("token-classification", model="projecte-aina/roberta-base-ca-cased-pos")
example = "Em dic Lluïsa i visc a Santa Maria del Camí."

pos_results = nlp(example)
pprint(pos_results)

Training

Training data

We used the POS dataset in Catalan from the Universal Dependencies Treebank we refer to Ancora-ca-pos 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 F1 score.

Evaluation results

We evaluated the roberta-base-ca-cased-pos 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.

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-pos