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--- |
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language: "pt" |
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widget: |
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- text: "O principal [MASK] da COVID-19 é tosse seca." |
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- text: "O vírus da gripe apresenta um [MASK] constituído por segmentos de ácido ribonucleico." |
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datasets: |
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- biomedical literature from Scielo and Pubmed |
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thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/logo-biobertpr1.png" |
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--- |
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<img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/logo-biobertpr1.png" alt="Logo BioBERTpt"> |
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# BioBERTpt - Portuguese Clinical and Biomedical BERT |
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The [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/) paper contains clinical and biomedical BERT-based models for Portuguese Language, initialized with BERT-Multilingual-Cased & trained on clinical notes and biomedical literature. |
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This model card describes the BioBERTpt(bio) model, a biomedical version of BioBERTpt, trained on Portuguese biomedical literature from scientific papers from Pubmed and Scielo. |
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## How to use the model |
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Load the model via the transformers library: |
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``` |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("pucpr/biobertpt-bio") |
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model = AutoModel.from_pretrained("pucpr/biobertpt-bio") |
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``` |
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## More Information |
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Refer to the original paper, [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/) for additional details and performance on Portuguese NER tasks. |
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## Questions? |
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Post a Github issue on the [BioBERTpt repo](https://github.com/HAILab-PUCPR/BioBERTpt). |
