Instructions to use NitzanBar/umls-spanbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NitzanBar/umls-spanbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NitzanBar/umls-spanbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NitzanBar/umls-spanbert") model = AutoModelForSequenceClassification.from_pretrained("NitzanBar/umls-spanbert") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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Based ob the paper: "UmlsBERT: Augmenting Contextual Embeddings with a Clinical Metathesaurus" (https://aclanthology.org/2021.naacl-main.139.pdf).
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license: apache-2.0
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- medical
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Based ob the paper: "UmlsBERT: Augmenting Contextual Embeddings with a Clinical Metathesaurus" (https://aclanthology.org/2021.naacl-main.139.pdf).
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