# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("NitzanBar/umls-bert")
model = AutoModelForSequenceClassification.from_pretrained("NitzanBar/umls-bert")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Based ob the paper: "UmlsBERT: Augmenting Contextual Embeddings with a Clinical Metathesaurus" (https://aclanthology.org/2021.naacl-main.139.pdf).
and the github repo: https://github.com/gmichalo/UmlsBERT
BERT base model.
Trained from scratch on MIMIC dataset, using the UMLS dataset to mask words within the text.
We achived better accuracy on MedNLI dataset.
Bert Model accuracy: 83%
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NitzanBar/umls-bert")