Bsc Bio Ehr Es Pharmaconer

🚀 PlanTL-GOB-ES/bsc-bio-ehr-es-pharmaconer

This is a Spanish RoBERTa-base biomedical model fine-tuned for the Named Entity Recognition (NER) task on the PharmaCoNER dataset, aiming to accurately identify entities in biomedical and clinical texts.

🚀 Quick Start

This model is a fine - tuned version of the bsc-bio-ehr-es model, which is based on RoBERTa. It has been pre - trained on a large Spanish biomedical corpus, including biomedical documents, clinical cases, and EHR documents, with a total of 1.1B tokens of clean and deduplicated text.

✨ Features

• Domain - Specific: Trained on a Spanish biomedical corpus, making it suitable for biomedical and clinical NLP tasks.
• Fine - Tuned for NER: Specifically fine - tuned for the Named Entity Recognition task on the PharmaCoNER dataset.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

No code examples are provided in the original document.

📚 Documentation

Model description

A fine - tuned version of the bsc-bio-ehr-es model, a RoBERTa 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

The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.

How to use

No specific usage instructions are provided in the original document.

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, a NER dataset annotated with substances, compounds and proteins entities. For further information, check the official website.

Evaluation

F1 Score: 0.8913

For evaluation details visit our GitHub repository.

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

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:

@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 NER tasks, rendering them more convenient for clinical NLP applications. Furthermore, our findings indicate that when enough data is available, pre-training from scratch is better than continual pre-training when tested on clinical tasks, raising an exciting research question about which approach is optimal. Our models and fine-tuning scripts are publicly available at HuggingFace and GitHub.",
}

Disclaimer

The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.

When third parties deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of artificial intelligence.

In no event shall the owner of the models (SEDIA – State Secretariat for digitalization and artificial intelligence) nor the creator (BSC – Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties of these models.

Information Table

Widget Examples

• "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."
• " 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."
• "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."