nhuvo/En-ViMedNER
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How to use nhuvo/umt5-base-en-vimedner-mt-en2vi with Transformers:
# Use a pipeline as a high-level helper
# Warning: Pipeline type "translation" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline
pipe = pipeline("translation", model="nhuvo/umt5-base-en-vimedner-mt-en2vi") # Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("nhuvo/umt5-base-en-vimedner-mt-en2vi")
model = AutoModelForSeq2SeqLM.from_pretrained("nhuvo/umt5-base-en-vimedner-mt-en2vi", device_map="auto")google/umt5-base fine-tuned for English → Vietnamese biomedical translation on En-ViMedNER.
Dataset details, splits, and citation: nhuvo/En-ViMedNER.
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
repo = "nhuvo/umt5-base-en-vimedner-mt-en2vi"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSeq2SeqLM.from_pretrained(repo)
text = "Patients with type 2 diabetes mellitus were enrolled."
inputs = tok(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-mt-vi2enBase model
google/umt5-base