Roberta Base Ca Cased Ner

🚀 roberta-base-ca-cased-ner

A Named Entity Recognition (NER) model for the Catalan language, fine-tuned from the BERTa model.

🚀 Quick Start

The roberta-base-ca-cased-ner is a powerful tool for Named Entity Recognition (NER) in the Catalan language. It is fine-tuned from the BERTa model, which is a RoBERTa base model pre-trained on a medium-size corpus.

✨ Features

• Language Specific: Specifically designed for the Catalan language.
• Fine-Tuned: Fine-tuned from a well-established RoBERTa base model.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

pipe = pipeline("ner", model="projecte-aina/multiner_ceil")
example = "George Smith Patton fué un general del Ejército de los Estados Unidos en Europa durante la Segunda Guerra Mundial. "

ner_entity_results = pipe(example, aggregation_strategy="simple")
print(ner_entity_results)

[{'entity_group': 'PER', 'score': 0.9983406, 'word': ' George Smith Patton', 'start': 0, 'end': 19}, {'entity_group': 'ORG', 'score': 0.99790734, 'word': ' Ejército de los Estados Unidos', 'start': 39, 'end': 69}, {'entity_group': 'LOC', 'score': 0.98424107, 'word': ' Europa', 'start': 73, 'end': 79}, {'entity_group': 'MISC', 'score': 0.9963934, 'word': ' Seg', 'start': 91, 'end': 94}, {'entity_group': 'MISC', 'score': 0.97889286, 'word': 'unda Guerra Mundial', 'start': 94, 'end': 113}]

📚 Documentation

Model Description

The roberta-base-ca-cased-ner is a Named Entity Recognition (NER) 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).

Intended Uses and Limitations

No specific limitations and intended uses details are provided in the original document.

Training

• Training Data: We used the NER dataset in Catalan called Ancora-ca-ner for training and evaluation.
• Training Procedure: No specific training procedure details are provided in the original document.

Evaluation

• Variable and Metrics: The evaluation metric used is F1.
• Evaluation Results: | Model | Ancora-ca-ner (F1)| | ------------|:-------------| | roberta-base-ca-cased-ner | 88.13 | | mBERT | 86.38 | | XLM-RoBERTa | 87.66 | | WikiBERT-ca | 77.66 |

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 (bsc-temu@bsc.es)
• Contact Information: For further information, send an email to aina@bsc.es
• Copyright: Copyright (c) 2021 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](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of Projecte AINA.
• Citation Information:

@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",
}

• Disclaimer: 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.

📄 License

This project is licensed under the Apache License, Version 2.0.