readme: ---
  language: 
    - es
  tags:
  - biomedical
  - clinical
  - eHR
  - spanish
  license: apache-2.0
  datasets:
  - "PlanTL-GOB-ES/pharmaconer"
  metrics:
  - f1
  
  model-index:
  - name: PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer
    results:
    - task:
        type: token-classification
      dataset:
        name: pharmaconer
        type: PlanTL-GOB-ES/pharmaconer
      metrics:
        - name: f1
          type: f1
          value: 0.8913
  widget:
  - text: "Se realizó estudio analítico destacando incremento de niveles de PTH y vitamina D (103,7 pg/ml y 272 ng/ml, respectivamente), atribuidos al exceso de suplementación de vitamina D."
  - text: " Por el hallazgo de múltiples fracturas por estrés, se procedió a estudio en nuestras consultas, realizándose análisis con función renal, calcio sérico y urinario, calcio iónico, magnesio y PTH, que fueron normales."
  - text: "Se solicitó una analítica que incluía hemograma, bioquímica, anticuerpos antinucleares (ANA) y serologías, examen de orina, así como biopsia de la lesión. Los resultados fueron normales, con ANA, anti-Sm, anti-RNP, anti-SSA, anti-SSB, anti-Jo1 y anti-Scl70 negativos."
  
  ---
  
  # Spanish RoBERTa-base biomedical model finetuned for the Named Entity Recognition (NER) task on the PharmaCoNER 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)
  - [Evaluation](#evaluation)
  - [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
  A fine-tuned version of the [bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) model, a [RoBERTa](https://arxiv.org/abs/1907.11692) base model and has been pre-trained using the largest Spanish biomedical corpus known to date, composed of biomedical documents, clinical cases and EHR documents for a total of 1.1B tokens of clean and deduplicated text processed.
  
  For more details about the corpora and training, check the _bsc-bio-ehr-es_ model card.
  
  ## Intended uses and limitations
  
  ## How to use
  
  ## 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 is [PharmaCoNER](https://huggingface.co/datasets/PlanTL-GOB-ES/pharmaconer), a NER dataset annotated with substances, compounds and proteins entities. For further information, check the [official website](https://temu.bsc.es/pharmaconer/).
  
  ## Evaluation 
  F1 Score: 0.8913
  
  For evaluation details visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es).
  
  ## 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 these models, please cite our work:
  
  ```bibtext
  @inproceedings{carrino-etal-2022-pretrained,
      title = "Pretrained Biomedical Language Models for Clinical {NLP} in {S}panish",
      author = "Carrino, Casimiro Pio  and
        Llop, Joan  and
        P{\`a}mies, Marc  and
        Guti{\'e}rrez-Fandi{\~n}o, Asier  and
        Armengol-Estap{\'e}, Jordi  and
        Silveira-Ocampo, Joaqu{\'\i}n  and
        Valencia, Alfonso  and
        Gonzalez-Agirre, Aitor  and
        Villegas, Marta",
      booktitle = "Proceedings of the 21st Workshop on Biomedical Language Processing",
      month = may,
      year = "2022",
      address = "Dublin, Ireland",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/2022.bionlp-1.19",
      doi = "10.18653/v1/2022.bionlp-1.19",
      pages = "193--199",
      abstract = "This work presents the first large-scale biomedical Spanish language models trained from scratch, using large biomedical corpora consisting of a total of 1.1B tokens and an EHR corpus of 95M tokens. We compared them against general-domain and other domain-specific models for Spanish on three clinical NER tasks. As main results, our models are superior across the N