
**⚠️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-pos

# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset
RoBERTa-base-bne is a transformer-based masked language model for the Spanish language. It is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) 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)](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-base-bne

## Dataset
The dataset used is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 2).

## Evaluation and results
F1 Score: 0.9846 (average of 5 runs).

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", "pos"], "datasets": ["bne", "capitel"], "metrics": ["f1"], "widget": [{"text": "Festival de San Sebasti\u00e1n: Johnny Depp recibir\u00e1 el premio Donostia en pleno rifirrafe judicial con Amber Heard"}, {"text": "El alcalde de Vigo, Abel Caballero, ha comenzado a colocar las luces de Navidad en agosto."}, {"text": "Gracias a los datos de la BNE, se ha podido lograr este modelo del lenguaje."}, {"text": "El Tribunal Superior de Justicia se pronunci\u00f3 ayer: \"Hay base legal dentro del marco jur\u00eddico actual\"."}]}
transformers pytorch roberta token-classification national library of spain spanish bne capitel pos 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 #pos #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-base trained on BNE finetuned for CAPITEL Part of Speech (POS) 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: URL

## Dataset
The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 2).

## Evaluation and results
F1 Score: 0.9846 (average of 5 runs).

For evaluation details visit our GitHub repository.


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

# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) 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: URL ## Dataset
The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 2). ## Evaluation and results
F1 Score: 0.9846 (average of 5 runs).

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 #pos #es #dataset-bne #dataset-capitel #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) 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: URL ## Dataset
The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 2). ## Evaluation and results
F1 Score: 0.9846 (average of 5 runs).

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 #pos #es #dataset-bne #dataset-capitel #arxiv-1907.11692 #arxiv-2107.07253 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) 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: URL## Dataset
The dataset used is the one from the CAPITEL competition at IberLEF 2020 (sub-task 2).## Evaluation and results
F1 Score: 0.9846 (average of 5 runs).

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