Model trained on the human mentions of the LivingNER corpus.

⚠ We recommend pre-tokenizing the input text into words instead of providing it directly to the model, as this is how the model was trained. Otherwise, the results and performance might get affected.

A usage example can be found here.

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.

NLP4BIA team at the Barcelona Supercomputing Center (nlp4bia@bsc.es).

jan.rodriguez [at] bsc.es

This project was partially funded by the Spanish Plan for the Advancement of Language Technology (Plan TL) in collaboration with the Barcelona Supercomputing Center (BSC) and the Hospital Clinic de Barcelona (HCB). On the BSC's side, we acknowledge additional funding by the Spanish National AI4ProfHealth project (PID2020-119266RA-I00 MICIU/AEI/10.13039/501100011033) and EU Horizon projects (AI4HF 101080430 and DataTools4Heart 101057849). On the HCB's side, the project was also supported by the Instituto de Salud Carlos III (ISCIII).

Please cite the following works:

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.

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.