readme: ---
  language:
  
  - es
  
  license: apache-2.0
  
  tags:
  
  - "national library of spain"
  
  - "spanish"
  
  - "bne"
  
  - "capitel"
  
  - "ner"
  
  datasets:
  
  - "bne"
  
  - "capitel"  
  
  
  metrics:
  
  - "f1"
  
  inference:
    parameters:
      aggregation_strategy: "first"
  
  model-index:
  - name: roberta-base-bne-capiter-ner
    results:
    - task: 
        type: token-classification
      dataset:
        type:  ner
        name: CAPITEL-NERC
      metrics:
        - name: F1
          type: f1
          value: 0.8960
          
  
  widget:
  
  - "Me llamo Francisco Javier y vivo en Madrid."
  
  - "Mi hermano Ramón y su mejor amigo Luis trabajan en el BSC."
  
  ---
  
  # Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
  
  ## Table of contents
  <details>
  <summary>Click to expand</summary>
  
  - [Model description](#model-description)
  - [Intended uses and limitations](#intended-use)
  - [How to use](#how-to-use)
  - [Limitations and bias](#limitations-and-bias)
  - [Training](#training)
  - [Training](#training)
    - [Training data](#training-data)
    - [Training procedure](#training-procedure)
  - [Evaluation](#evaluation)
  - [Evaluation](#evaluation)
     - [Variable and metrics](#variable-and-metrics)
     - [Evaluation results](#evaluation-results)
  - [Additional information](#additional-information)
    - [Author](#author)
    - [Contact information](#contact-information)
    - [Copyright](#copyright)
    - [Licensing information](#licensing-information)
    - [Funding](#funding)
    - [Citing information](#citing-information)
    - [Disclaimer](#disclaimer)
    
  </details>
  
  ## Model description
  The **roberta-base-bne-capitel-ner** is a Named Entity Recognition (NER) model for the Spanish language fine-tuned from the [roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model 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.
  
  ## Intended uses and limitations
  
  **roberta-base-bne-capitel-ner** model can be used to recognize Named Entities (NE). The model is limited by its training dataset and may not generalize well for all use cases.
  
  ## How to use
  
  ```python
  from transformers import pipeline
  from pprint import pprint
  
  nlp = pipeline("ner", model="PlanTL-GOB-ES/roberta-base-bne-capitel-ner")
  example = "Me llamo Francisco Javier y vivo en Madrid."
  
  ner_results = nlp(example)
  pprint(ner_results)
  ```
  
  ## Limitations and bias
  At the time of submission, no measures have been taken to estimate the bias embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this model card will be updated.
  
  ## Training
  
  The dataset used for training and evaluation is the one from the [CAPITEL competition at IberLEF 2020](https://sites.google.com/view/capitel2020) (sub-task 1).
  
  ### Training procedure
  The model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.
  
  ## Evaluation 
  
  
  ### Variable and metrics
  This model was finetuned maximizing F1 score.
  
  ## Evaluation results
  We evaluated the **roberta-base-bne-capitel-ner** on the CAPITEL-NERC test set against standard multilingual and monolingual baselines:
  
  
  | Model        | CAPITEL-NERC (F1) | 
  | ------------|:----|
  | roberta-large-bne-capitel-ner | **90.51** |
  | roberta-base-bne-capitel-ner | 89.60|
  | BETO       | 87.72 |
  | mBERT       | 88.10 |
  | BERTIN | 88.56 |
  | ELECTRA | 80.35 |
  
  For more details, check the fine-tuning and evaluation scripts in the official [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-spanish).
  
  
  ## 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 <plantl-gob-es@bsc.es>
  
  ### Copyright
  Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
  
  ### Licensing information
  [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
  
  ### Funding
  This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
  
  ### Citing information
  If you use this model, please cite our [paper](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405):
  ```
  @article{,
     abstract = {We want to thank the National Library of Spain for such a large effort on the data gathering and the Future of Computing Center, a
  Barcelona Supercomputing Center and IBM initiative 