quickmt-en-zh Neural Machine Translation Model

quickmt-en-zh is a reasonably fast and reasonably accurate neural machine translation model for translation from en into zh.

Model Information

• Trained using eole
• 200M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
• Separate source and target Sentencepiece tokenizers
• Exported for fast inference to CTranslate2 format
• Training data: https://huggingface.co/datasets/quickmt/quickmt-train.zh-en/tree/main

See the eole model configuration in this repository for further details.

Usage with quickmt

First, install quickmt and download the model

git clone https://github.com/quickmt/quickmt.git
pip install ./quickmt/

quickmt-model-download quickmt/quickmt-en-zh ./quickmt-en-zh

Next use the model in python:

from quickmt import Translator

# Auto-detects GPU, set to "cpu" to force CPU inference
t = Translator("./quickmt-en-zh/", device="auto")

# Translate - set beam size to 5 for higher quality (but slower speed)
t(["The Boot Monument is an American Revolutionary War memorial located in Saratoga National Historical Park in the state of New York."], beam_size=1)

# Get alternative translations by sampling
# You can pass any cTranslate2 `translate_batch` arguments
t(["The Boot Monument is an American Revolutionary War memorial located in Saratoga National Historical Park in the state of New York."], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9)

The model is in ctranslate2 format, and the tokenizers are sentencepiece, so you can use ctranslate2 directly instead of through quickmt. It is also possible to get this model to work with e.g. LibreTranslate which also uses ctranslate2 and sentencepiece.

Metrics

chrf2 is calculated with sacrebleu on the Flores200 devtest test set ("eng_Latn"->"zho_Hans"). comet22 with the comet library and the default model. "Time (s)" is the time in seconds to translate (using ctranslate2) the flores-devtest dataset (1012 sentences) on an RTX 4070s GPU with batch size 32.

Model	chrf2	comet22	Time (s)
quickmt/quickmt-en-zh	35.22	85.39	0.96
Helsinki-NLP/opus-mt-en-zh	29.20	82.36	3.41
facebook/m2m100_418M	25.86	73.76	16.71
facebook/m2m100_1.2B	28.94	78.38	31.09
facebook/nllb-200-distilled-600M	24.52	78.41	19.01
facebook/nllb-200-distilled-1.3B	26.79	79.87	32.03

quickmt-en-zh is the fastest and highest quality.