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

# Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) large 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)](http://www.bne.es/en/Inicio/index.html) from 2009 to 2019.

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

## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1).

## Evaluation and results
F1 Score: 0.8998

For evaluation details visit our [GitHub repository](https://github.com/PlanTL-SANIDAD/lm-spanish).

## 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}
}
```
{"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "capitel", "ner"], "datasets": ["bne", "capitel"], "metrics": ["f1"]}
transformers pytorch roberta token-classification national library of spain spanish bne capitel ner es dataset:bne dataset:capitel arxiv:1907.11692 arxiv:2107.07253 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #token-classification #national library of spain #spanish #bne #capitel #ner #es #dataset-bne #dataset-capitel #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


️NOTICE️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED: URL

# Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large 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: URL

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

## Evaluation and results
F1 Score: 0.8998

For evaluation details visit our GitHub repository.

## Citing 
Check out our paper for all the details: URL

# Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large 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: URL ## Dataset
The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 1). ## Evaluation and results
F1 Score: 0.8998

For evaluation details visit our GitHub repository. ## Citing 
Check out our paper for all the details: URL
TAGS
#transformers #pytorch #roberta #token-classification #national library of spain #spanish #bne #capitel #ner #es #dataset-bne #dataset-capitel #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large 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: URL ## Dataset
The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 1). ## Evaluation and results
F1 Score: 0.8998

For evaluation details visit our GitHub repository. ## Citing 
Check out our paper for all the details: URL
passage: TAGS
#transformers #pytorch #roberta #token-classification #national library of spain #spanish #bne #capitel #ner #es #dataset-bne #dataset-capitel #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
RoBERTa-large-bne is a transformer-based masked language model for the Spanish language. It is based on the RoBERTa large 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: URL## Dataset
The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 1).## Evaluation and results
F1 Score: 0.8998

For evaluation details visit our GitHub repository.## Citing 
Check out our paper for all the details: URL