BSC-LT/roberta-base-bne-capitel-ner

⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner

Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.

RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa base model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019.

Original pre-trained model can be found here: https://huggingface.co/BSC-TeMU/roberta-base-bne

Dataset

The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 1).

Evaluation and results

F1 Score: 0.8960

For evaluation details visit our GitHub repository.

Citing

Check out our paper for all the details: https://arxiv.org/abs/2107.07253

@misc{gutierrezfandino2021spanish,
      title={Spanish Language Models}, 
      author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquín Silveira-Ocampo and Casimiro Pio Carrino and Aitor Gonzalez-Agirre and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Marta Villegas},
      year={2021},
      eprint={2107.07253},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}