Input: Bệnh nhân đái tháo đường típ 2 được tuyển vào nghiên cứu.
Output: Patients with <BIOLOGIC_FUNCTION>type 2 diabetes mellitus</BIOLOGIC_FUNCTION> were enrolled.

Tags follow En-ViMedNER type names (such as <CHEMICAL>...</CHEMICAL>, <BIOLOGIC_FUNCTION>...</BIOLOGIC_FUNCTION>). Full label inventory, splits, and citation: nhuvo/En-ViMedNER.

Usage

python

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

repo = "nhuvo/nllb-600m-en-vimedner-direct-trans-ner-vi2en"
tok = AutoTokenizer.from_pretrained(repo, src_lang="vie_Latn")
model = AutoModelForSeq2SeqLM.from_pretrained(repo)

prefix = "translate Vietnamese to English with inline named entity tags: "
text = "Bệnh nhân đái tháo đường típ 2 được tuyển vào nghiên cứu."
inputs = tok(prefix + text, return_tensors="pt")
outputs = model.generate(
    **inputs,
    forced_bos_token_id=tok.convert_tokens_to_ids("eng_Latn"),
    max_new_tokens=256,
)
print(tok.batch_decode(outputs, skip_special_tokens=True)[0])

Pair model (EN→VI): nhuvo/nllb-600m-en-vimedner-direct-trans-ner-en2vi

nhuvo/nllb-600m-en-vimedner-direct-trans-ner-vi2en

nllb-600m-en-vimedner-direct-trans-ner-vi2en

What it does

Usage

Related

nhuvo/
nllb-600m-en-vimedner-direct-trans-ner-vi2en