metadata

language:
  - en
  - ja
tags:
  - translation
license: cc-by-4.0
datasets:
  - quickmt/quickmt-train.ja-en
model-index:
  - name: quickmt-ja-en
    results:
      - task:
          name: Translation jpn-eng
          type: translation
          args: jpn-eng
        dataset:
          name: flores101-devtest
          type: flores_101
          args: jpn_Japn eng_Latn devtest
        metrics:
          - name: CHRF
            type: chrf
            value: 57.06
          - name: BLEU
            type: bleu
            value: 27.91
          - name: COMET
            type: comet
            value: 87.29

`quickmt-ja-en` Neural Machine Translation Model

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

Model Information

Trained using eole
185M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
20k sentencepiece vocabularies
Exported for fast inference to CTranslate2 format
Training data: https://huggingface.co/datasets/quickmt/quickmt-train.ja-en/tree/main

See the eole model configuration in this repository for further details and the eole-model for the raw eole (pytorch) model.

Usage with `quickmt`

You must install the Nvidia cuda toolkit first, if you want to do GPU inference.

Next, install the quickmt python library and download the model:

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

quickmt-model-download quickmt/quickmt-ja-en ./quickmt-ja-en

Finally use the model in python:

from quickmt import Translator

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

# Translate - set beam size to 5 for higher quality (but slower speed)
sample_text = 'ノバスコシア州ハリファックスにあるダルハウジー大学医学部教授でカナダ糖尿病協会の臨床・科学部門の責任者を務めるエフード・ウル博士は、この研究はまだ初期段階にあるとして注意を促しました。'
t(sample_text, beam_size=5)

> 'Dr. Ehud Ulu, a professor of medicine at Dalhousie University in Halifax, Nova Scotia and head of the clinical and scientific division of the Canadian Diabetes Association, cautioned that the study is still in its early stages.'

# Get alternative translations by sampling
# You can pass any cTranslate2 `translate_batch` arguments
t([sample_text], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9)

> 'Dr Ehud Ul, professor of medicine at the University of Dalhousie’s Halifax, Nova Scotia and head of the clinical and scientific division of the Canadian Diabetes Society, noted the study is in its early stages.'

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

bleu and chrf2 are calculated with sacrebleu on the Flores200 devtest test set ("jpn_Jpan"->"eng_Latn"). 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 (faster speed is possible using a large batch size).

	bleu	chrf2	comet22	Time (s)
quickmt/quickmt-ja-en	27.91	57.06	87.29	1.00
Helsink-NLP/opus-mt-ja-en	19.22	49.15	82.64	3.54
facebook/nllb-200-distilled-600M	24.05	53.39	85.84	22.5
facebook/nllb-200-distilled-1.3B	28.4	56.96	87.47	37.15
facebook/m2m100_418M	18.82	49.55	82.59	18.27
facebook/m2m100_1.2B	23.32	53.46	85.43	35.44

quickmt-ja-en is the fastest and nearly as high-quality as facebook/nllb-200-distilled-1.3B.

quickmt-ja-en Neural Machine Translation Model

Model Information

Usage with quickmt

Metrics

`quickmt-ja-en` Neural Machine Translation Model

Usage with `quickmt`