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README.md
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This model adapts the pre-trained model [bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es), presented in [Pio Carriño et al. (2022)](https://aclanthology.org/2022.bionlp-1.19/).
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It is fine-tuned to conduct temporal named entity recognition on Spanish texts about clinical trials.
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The model is fine-tuned on the [CT-EBM-
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## Intended uses & limitations
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- 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO)
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- 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos
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If you use the CT-EBM-
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```
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@article{campillosetal-midm2021,
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This model adapts the pre-trained model [bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es), presented in [Pio Carriño et al. (2022)](https://aclanthology.org/2022.bionlp-1.19/).
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It is fine-tuned to conduct temporal named entity recognition on Spanish texts about clinical trials.
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The model is fine-tuned on the [CT-EBM-SP corpus (Campillos-Llanos et al. 2021)](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01395-z).
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## Intended uses & limitations
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- 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO)
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- 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos
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If you use the CT-EBM-SP resource, please, cite as follows:
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```
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@article{campillosetal-midm2021,
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