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quickmt/
quickmt-ko-en

Name: quickmt-ko-en
Rating: 2.5 (31 reviews)

translation

quickmt/quickmt-train.ko-en

cc-by-4.0

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`quickmt-ko-en` Neural Machine Translation Model

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

bash

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

Finally, use the model in python:

python

from quickmt import Translator
from huggingface_hub import snapshot_download

# Download Model (if not downloaded already) and return path to local model
# Device is either 'auto', 'cpu' or 'cuda'
t = Translator(
    snapshot_download("quickmt/quickmt-ko-en", ignore_patterns="eole-model/*"),
    device="cpu"
)

# Translate - set beam size to 5 for higher quality (but slower speed)
sample_text = '노바스코샤주 핼리팩스의 댈하우지대학교 의과 교수이자 캐나다 당뇨 협회 임상과학부 의장인 Ehud Ur 박사는 이 연구가 아직 초기 단계라고 경고했습니다.'
t(sample_text, beam_size=5)

> 'Dr. Ehud Ur, a medical professor at Dalhousie University in Halifax, Nova Scotia and chair of the Canadian Diabetes Association Clinical Sciences Department, warned 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 Ur, professor of medicine and professor of medicine from the Dalhowes Institute and chair of the Canadian Diabetes Association Clinical Science Department in Halifax, Nova Scotia, warned the study is still an early step forward.'

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-ko-en	27.03	56.25	86.11	1.05
Helsink-NLP/opus-mt-ko-en	20.78	50.39	83.06	3.62
facebook/nllb-200-distilled-600M	26.53	55.04	85.83	21.28
facebook/nllb-200-distilled-1.3B	29.61	57.58	87.24	37.42
facebook/m2m100_418M	20.75	50.65	82.07	18.21
facebook/m2m100_1.2B	24.59	54.17	85.15	34.82

quickmt-ko-en is the fastest and is higher quality than opus-mt-ko-en, m2m100_418m, m2m100_1.2B and nllb-200-distilled-600M but lower quality than nllb-200-distilled-1.3B.

quickmt/quickmt-ko-en

quickmt-ko-en Neural Machine Translation Model

Model Information

Usage with quickmt

Metrics

quickmt/
quickmt-ko-en

`quickmt-ko-en` Neural Machine Translation Model

Usage with `quickmt`