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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-base-spanish-wwm-uncased-finetuned-NER-medical
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This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an adaptation of eHealth-KD Challenge 2020 dataset
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It achieves the following results on the evaluation set:
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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# bert-base-spanish-wwm-uncased-finetuned-NER-medical
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This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an adaptation of [eHealth-KD Challenge 2020 dataset](https://knowledge-learning.github.io/ehealthkd-2020/), filtered only for the task of NER. The dataset annotations for NER are ['Concept', 'Action', 'Predicate', 'Reference'].
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Before the training process, the dataset had processed to label it with the BIO annotation format. Some cleaning and adaptations were needed, for example, to work with overlapped entities.
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It achieves the following results on the evaluation set:
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## Training procedure
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The chapter [“Token classification”]( https://huggingface.co/course/chapter7/2) in the Hugging Face online course was used as starting point for the training process. We made some adaptions because our dataset follows a slightly different structure. Moreover, a conversion between string labels and integers labels was needed.
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### Training hyperparameters
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The following hyperparameters were used during training:
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