umt5-base-en-vimedner-ner-en

google/umt5-base fine-tuned for English biomedical NER (plain text → inline tagged text) on En-ViMedNER.

Dataset details, label inventory, splits, and citation: nhuvo/En-ViMedNER.

Usage

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

repo = "nhuvo/umt5-base-en-vimedner-ner-en"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSeq2SeqLM.from_pretrained(repo)

prefix = "recognize English named entities: "
text = "Patients with type 2 diabetes mellitus were enrolled."
inputs = tok(prefix + text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tok.decode(outputs[0], skip_special_tokens=True))

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