Instructions to use medspaner/mdeberta-v3-base-re-ct-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use medspaner/mdeberta-v3-base-re-ct-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, DebertaV2ForRelationExtraction tokenizer = AutoTokenizer.from_pretrained("medspaner/mdeberta-v3-base-re-ct-v2") model = DebertaV2ForRelationExtraction.from_pretrained("medspaner/mdeberta-v3-base-re-ct-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -259,7 +259,9 @@ example = [['Título',
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'monoclonal',
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'humano',
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'anti-TNF',
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'Adalimumab',
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'en',
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'<S:LIV>',
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'sujetos',
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'ulcerosa',
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'moderada',
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'o',
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'grav<O:CHE>',
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'Adalimumab',
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'</O:CHE>blico:',
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'Estudio',
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'multicéntrico,',
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'doble',
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'del',
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'monoclonal',
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'humano',
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'en',
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'sujetos',
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'pediátricos',
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'con',
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'colitis',
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'moderada',
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'o',
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'grave']]
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model = DebertaV2ForRelationExtraction.from_pretrained("medspaner/mdeberta-v3-base-re-ct-v2",8)
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'monoclonal',
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'humano',
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'anti-TNF',
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'<O:CHE>',
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'Adalimumab',
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'</O:CHE>',
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'en',
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'<S:LIV>',
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'sujetos',
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'ulcerosa',
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'moderada',
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'o',
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'grave']]
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model = DebertaV2ForRelationExtraction.from_pretrained("medspaner/mdeberta-v3-base-re-ct-v2",8)
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