En-ViMedNER
Collection
English–Vietnamese biomedical NER dataset and finetuned models (MT, NER, direct-trans-ner). • 15 items • Updated
umt5-base-en-vimedner-direct-trans-ner-en2vi
google/umt5-base fine-tuned on En-ViMedNER for M1: Direct Trans.+NER (English → Vietnamese).
What it does
One-shot cross-lingual NER via translation: the model translates and inserts entity tags in the target in a single pass.
| Input | Plain English biomedical sentence (no tags) |
| Output | Vietnamese translation with inline entity markup |
Example shape:
- Input:
Patients with type 2 diabetes mellitus were enrolled. - Output:
Bệnh nhân mắc <BIOLOGIC_FUNCTION>đái tháo đường típ 2</BIOLOGIC_FUNCTION> được tuyển vào nghiên cứu.
Tags follow En-ViMedNER type names (such as <CHEMICAL>...</CHEMICAL>, <BIOLOGIC_FUNCTION>...</BIOLOGIC_FUNCTION>). Full label inventory, splits, and citation: nhuvo/En-ViMedNER.
Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
repo = "nhuvo/umt5-base-en-vimedner-direct-trans-ner-en2vi"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSeq2SeqLM.from_pretrained(repo)
prefix = "translate English to Vietnamese with inline named entity tags: "
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))
Related
- Pair model (VI→EN):
nhuvo/umt5-base-en-vimedner-direct-trans-ner-vi2en
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Model tree for nhuvo/umt5-base-en-vimedner-direct-trans-ner-en2vi
Base model
google/umt5-base