metadata

language:
  - en
  - ko
tags:
  - translation
license: cc-by-4.0
datasets:
  - quickmt/quickmt-train.ko-en
model-index:
  - name: quickmt-en-ko
    results:
      - task:
          name: Translation eng-kor
          type: translation
          args: eng-kor
        dataset:
          name: flores101-devtest
          type: flores_101
          args: eng_Latn kor_Hang devtest
        metrics:
          - name: CHRF
            type: chrf
            value: 56.25
          - name: BLEU
            type: bleu
            value: 27.03
          - name: COMET
            type: comet
            value: 86.11

`quickmt-en-ko` Neural Machine Translation Model

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

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.ko-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-en-ko ./quickmt-en-ko

Finally use the model in python:

from quickmt import Translator

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

# Translate - set beam size to 5 for higher quality (but slower speed)
sample_text = 'Dr. Ehud Ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia and chair of the clinical and scientific division of the Canadian Diabetes Association cautioned that the research is still in its early days.'
t(sample_text, beam_size=5)

# 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)

> '노바 스코샤 주 핼리팩스에 있는 댈하우지 대학교 의과 교수인 에후드 우르 박사와 캐나다 당뇨병 협회의 임상 및 과학 부문 의장은 이 연구가 아직 초기 단계에 있다고 경고했다.'

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 ("kor_Hang"->"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-en-ko	14.97	36.96	87.10	1.36
facebook/nllb-200-distilled-600M	12.15	33.66	87.39	24.75
facebook/nllb-200-distilled-1.3B	13.23	35.62	88.39	40.29
facebook/m2m100_418M	9.91	30.69	83.20	22.23
facebook/m2m100_1.2B	11.35	33.26	85.65	41.15

quickmt-en-ko is the fastest and is higher quality than m2m100_418m, m2m100_1.2B and nllb-200-distilled-600M.

quickmt-en-ko Neural Machine Translation Model

Model Information

Usage with quickmt

Metrics

`quickmt-en-ko` Neural Machine Translation Model

Usage with `quickmt`