nhuvo/En-ViMedNER
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How to use nhuvo/umt5-base-en-vimedner-ner-en with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="nhuvo/umt5-base-en-vimedner-ner-en") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("nhuvo/umt5-base-en-vimedner-ner-en")
model = AutoModelForSeq2SeqLM.from_pretrained("nhuvo/umt5-base-en-vimedner-ner-en", device_map="auto")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.
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))
nhuvo/umt5-base-en-vimedner-ner-viBase model
google/umt5-base