pytorch roberta token-classification es dataset:PlanTL-GOB-ES/cantemist-ner arxiv:1907.11692 transformers biomedical clinical eHR spanish license:apache-2.0 model-index autotrain_compatible
---
language: 
  - es
tags:
- biomedical
- clinical
- eHR
- spanish
license: apache-2.0
datasets:
- "PlanTL-GOB-ES/cantemist-ner"
metrics:
- f1

model-index:
- name: PlanTL-GOB-ES/bsc-bio-ehr-es-cantemist
  results:
  - task:
      type: token-classification
    dataset:
      name: cantemist-ner
      type: PlanTL-GOB-ES/cantemist-ner
    metrics:
      - name: f1
        type: f1
        value: 0.8340
widget:
- text: "El diagnóstico definitivo de nuestro paciente fue de un Adenocarcinoma de pulmón cT2a cN3 cM1a Estadio IV (por una única lesión pulmonar contralateral) PD-L1 90%, EGFR negativo, ALK negativo y ROS-1 negativo."
- text: "Durante el ingreso se realiza una TC, observándose un nódulo pulmonar en el LII y una masa renal derecha indeterminada. Se realiza punción biopsia del nódulo pulmonar, con hallazgos altamente sospechosos de carcinoma."
- text: "Trombosis paraneoplásica con sospecha de hepatocarcinoma por imagen, sobre hígado cirrótico, en paciente con índice Child-Pugh B."

---


# Spanish RoBERTa-base biomedical model finetuned for the Named Entity Recognition (NER) task on the Cantemist dataset.
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.

## Dataset
The dataset used is [CANTEMIST](https://huggingface.co/datasets/PlanTL-GOB-ES/cantemist-ner), a NER dataset annotated with tumor morphology entities. For further information, check the [official website](https://temu.bsc.es/cantemist/).

## Evaluation and results
F1 Score: 0.8340

For evaluation details visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es).

## Citing 
To be announced soon!

## Funding
This work was partially funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL, and the Future of Computing Center, a Barcelona Supercomputing Center and IBM initiative (2020).

## 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.


Los modelos publicados en este repositorio tienen una finalidad generalista y están a disposición de terceros. Estos modelos pueden tener sesgos y/u otro tipo de distorsiones indeseables.

Cuando terceros desplieguen o proporcionen sistemas y/o servicios a otras partes usando alguno de estos modelos (o utilizando sistemas basados en estos modelos) o se conviertan en usuarios de los modelos, deben tener en cuenta que es su responsabilidad mitigar los riesgos derivados de su uso y, en todo caso, cumplir con la normativa aplicable, incluyendo la normativa en materia de uso de inteligencia artificial.

En ningún caso el propietario de los modelos (SEDIA – Secretaría de Estado de Digitalización e Inteligencia Artificial) ni el creador (BSC – Barcelona Supercomputing Center) serán responsables de los resultados derivados del uso que hagan terceros de estos modelos.
