projecte-aina / roberta-base-ca-v2-cased-ner

Catalan BERTa-v2 (roberta-base-ca-v2) finetuned for Named Entity Recognition.

Table of Contents

Model description

The roberta-base-ca-v2-cased-ner is a Named Entity Recognition (NER) model for the Catalan language fine-tuned from the roberta-base-ca-v2 model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details).

Intended uses and limitations

roberta-base-ca-v2-cased-ner model can be used to recognize Named Entities in the provided 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("ner", model="projecte-aina/roberta-base-ca-v2-cased-ner")
example = "Em dic Lluïsa i visc a Santa Maria del Camí."

ner_results = nlp(example)
pprint(ner_results)

[{'entity_group': 'person-other', 'score': 0.90749526, 'word': ' George Smith Patton', 'start': 0, 'end': 19}, {'entity_group': 'organization-other', 'score': 0.9260813, 'word': ' Ejército de los Estados Unidos', 'start': 39, 'end': 69}, {'entity_group': 'location-other', 'score': 0.992481, 'word': ' Europa', 'start': 73, 'end': 79}, {'entity_group': 'event-attack/terrorism/militaryconflict', 'score': 0.99319077, 'word': ' Segunda Guerra Mundial', 'start': 91, 'end': 113}]

Limitations and bias

At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.

Training

Training data

We used the NER dataset in Catalan called AnCora-Ca-NER 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-v2-cased-ner on the AnCora-Ca-NER test set against standard multilingual and monolingual baselines:

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

Additional information

Author

Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ( [email protected] )

Contact information

For further information, send an email to [email protected]

Copyright

Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center

Licensing information

Apache License, Version 2.0

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 .

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",
}

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