Bsc Bio Es

🚀 Biomedical language model for Spanish

A pre - trained biomedical language model tailored for Spanish, offering solutions for masked language modelling and potential fine - tuning for downstream tasks.

🚀 Quick Start

The model is ready - to - use only for masked language modelling to perform the Fill Mask task. You can try the inference API or refer to the How to use section.

✨ Features

• Specialized for Spanish: A biomedical pretrained language model designed specifically for the Spanish language.
• Potential for Fine - Tuning: Intended to be fine - tuned on downstream tasks such as Named Entity Recognition or Text Classification.

📦 Installation

No installation steps are provided in the original document.

💻 Usage Examples

No code examples are provided in the original document.

📚 Documentation

Model description

Biomedical pretrained language model for Spanish. For more details about the corpus, the pretraining and the evaluation, check the official repository.

Intended uses and limitations

The model is ready - to - use only for masked language modelling to perform the Fill Mask task (try the inference API or read the next section). However, it is intended to be fine - tuned on downstream tasks such as Named Entity Recognition or Text Classification.

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

Tokenization and model pretraining

This model is a RoBERTa - based model trained on a biomedical corpus in Spanish collected from several sources (see next section). The training corpus has been tokenized using a byte version of [Byte - Pair Encoding (BPE)](https://github.com/openai/gpt - 2) used in the original RoBERTA model with a vocabulary size of 52,000 tokens. The pretraining consists of a masked language model training at the subword level following the approach employed for the RoBERTa base model with the same hyperparameters as in the original work. The training lasted a total of 48 hours with 16 NVIDIA V100 GPUs of 16GB DDRAM, using Adam optimizer with a peak learning rate of 0.0005 and an effective batch size of 2,048 sentences.

Training corpora and preprocessing

The training corpus is composed of several biomedical corpora in Spanish, collected from publicly available corpora and crawlers. To obtain a high - quality training corpus, a cleaning pipeline with the following operations has been applied:

• data parsing in different formats
  • sentence splitting
  • language detection
  • filtering of ill - formed sentences
  • deduplication of repetitive contents
  • keep the original document boundaries

Finally, the corpora are concatenated and further global deduplication among the corpora has been applied. The result is a medium - size biomedical corpus for Spanish composed of about 963M tokens. The table below shows some basic statistics of the individual cleaned corpora:

Evaluation

The model has been fine - tuned on three Named Entity Recognition (NER) tasks using three clinical NER datasets:

• PharmaCoNER: is a track on chemical and drug mention recognition from Spanish medical texts (for more info see: https://temu.bsc.es/pharmaconer/).
• CANTEMIST: is a shared task specifically focusing on named entity recognition of tumour morphology, in Spanish (for more info see: https://zenodo.org/record/3978041#.YTt5qH2xXbQ).
• ICTUSnet: consists of 1,006 hospital discharge reports of patients admitted for stroke from 18 different Spanish hospitals. It contains more than 79,000 annotations for 51 different kinds of variables.

We addressed the NER task as a token classification problem using a standard linear layer along with the BIO tagging schema. We compared our models with the general - domain Spanish [roberta - base - bne](https://huggingface.co/PlanTL - GOB - ES/roberta - base - bne), the general - domain multilingual model that supports Spanish [mBERT](https://huggingface.co/bert - base - multilingual - cased), the domain - specific English model [BioBERT](https://huggingface.co/dmis - lab/biobert - base - cased - v1.2), and three domain - specific models based on continual pre - training, mBERT - Galén, XLM - R - Galén and BETO - Galén.

The table below shows the F1 scores obtained:

The fine - tuning scripts can be found in the official 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.

Citation 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.