bsc-temu
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README.md
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---
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# Catalan RoBERTa-base
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metrics:
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- f1
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model-index:
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- name: roberta-base-ca-cased-te
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results:
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- task:
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type: token-classification
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dataset:
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type: projecte-aina/ancora-ca-ner
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name: ancora-ca-ner
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metrics:
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- type: f1
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value: 0.8963029756537422
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---
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# Catalan BERTa (RoBERTa-base) finetuned for Named Entity Recognition.
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The **roberta-base-ca-cased-ner** is a Named Entity Recognition (NER) model for the Catalan language fine-tuned from the [BERTa](https://huggingface.co/PlanTL-GOB-ES/roberta-base-ca) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the BERTa model card for more details).
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## Datasets
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We used the NER dataset in Catalan called [Ancora-NER](https://huggingface.co/datasets/projecte-aina/ancora-ca-ner) for training and evaluation.
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## Evaluation and results
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Below, the evaluation result on the Ancora-NER test set:
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| Task | TECA (accuracy) |
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| ------------|:----|
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| BERTa | **89.63** |
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For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/projecte-aina/berta).
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## Citing
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If you use any of these resources (datasets or models) in your work, please cite our latest paper:
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```bibtex
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@inproceedings{armengol-estape-etal-2021-multilingual,
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title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
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author = "Armengol-Estap{\'e}, Jordi and
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Carrino, Casimiro Pio and
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Rodriguez-Penagos, Carlos and
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de Gibert Bonet, Ona and
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Armentano-Oller, Carme and
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Gonzalez-Agirre, Aitor and
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Melero, Maite and
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Villegas, Marta",
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booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
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month = aug,
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year = "2021",
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address = "Online",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2021.findings-acl.437",
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doi = "10.18653/v1/2021.findings-acl.437",
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pages = "4933--4946",
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}
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```
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## Funding
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TODO
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## Disclaimer
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TODO
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