metadata
language:
- zh
- vi
pipeline_tag: translation
tags:
- translation
- zh-vi
- chinese-vietnamese
- marianmt
- machine-translation
library_name: transformers
base_model: none
datasets:
- chi-vi/hirashiba-mt-zh2vi-b-filtered
Hachimi MT 30 zh-vi
Fast Chinese to Vietnamese Marian-style machine translation model.
Intended Use
- Chinese -> Vietnamese web novel / fiction translation.
- Fast local or server inference where a small model is preferred.
- This is an experimental release; output should still be reviewed for high-stakes or publication use.
Model Details
- Architecture: Marian seq2seq
- Parameters: ~32M
- Tokenizer: SentencePiece source/target tokenizer
- Suggested decoding:
num_beams=1,max_length=512
Benchmark Snapshot
FLORES-200 zho_Hans -> vie_Latn devtest, decoded with num_beams=1,
max_length=512.
| Evaluation set | Rows | BLEU | chrF | Chinese-character leak rows |
|---|---|---|---|---|
| FLORES-200 devtest | 1,012 | 14.29 | 37.30 | 0 |
This is a general-domain benchmark; it is useful for public comparability but does not fully reflect web-novel style or domain terminology.
Metric notes:
- BLEU and chrF are automatic reference-based metrics. Higher is generally better, but human quality may differ, especially for fiction/web-novel style.
- Chinese-character leak rows counts outputs that still contain Chinese characters after translation. Lower is better.
Quick Start
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_id = "ngocdang83/HachimiMT-30-zh-vi"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
text = "他抬头看向远处的山门。"
inputs = tok(text, return_tensors="pt", truncation=True, max_length=512)
out = model.generate(**inputs, max_length=512, num_beams=1)
print(tok.decode(out[0], skip_special_tokens=True))
Notes
- This model prioritizes speed and small footprint.
- A CTranslate2 INT8 runtime is available in
ct2-int8_float32/for faster CPU inference. - Known hard cases include rare proper nouns, idioms, and highly domain-specific OOD terminology.
- For production-style usage, pair with reviewed glossary/guard layers where appropriate.