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quickmt/
quickmt-en-vi

Name: quickmt-en-vi
Rating: 2.5 (74 reviews)

translation

quickmt/quickmt-train.vi-en

cc-by-4.0

model-index

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`quickmt-en-vi` Neural Machine Translation Model

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

Model Information

Trained using eole
195M 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.fa-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-en-vi", ignore_patterns="eole-model/*"),
    device="cpu"
)

# 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)

'Tiến sĩ Ehud Ur, giáo sư y khoa tại Đại học Dalhousie ở Halifax, Nova Scotia và chủ tịch bộ phận lâm sàng và khoa học của Hiệp hội Tiểu đường Canada cảnh báo rằng nghiên cứu vẫn còn trong những ngày đầu.'

python

# 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)

'Tiến sĩ Ehud Ur, Giáo sư Y tại Đại học Dalhousie ở Halifax, Nova Scotia và là chủ tịch bộ phận lâm sàng và khoa học của Hiệp hội Tiểu đường Canada đã cảnh báo rằng nghiên cứu mới này vẫn còn trong những ngày đầu.'

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 ("eng_Latn"->"vie_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 with a larger batch size).

	bleu	chrf2	comet22	Time (s)
quickmt/quickmt-en-vi	43.68	60.75	87.52	1.5
Helsinki-NLP/opus-mt-en-vi	27.57	46.45	76.22	4.67
facebook/nllb-200-distilled-600M	39.06	57.27	86.95	24.12
facebook/nllb-200-distilled-1.3B	41.27	59.04	88.11	42.3
facebook/m2m100_418M	33.95	52.82	82.53	20.32
facebook/m2m100_1.2B	39.46	57.45	86.56	38.65

quickmt/quickmt-en-vi

quickmt-en-vi Neural Machine Translation Model

Model Information

Usage with quickmt

Metrics

quickmt/
quickmt-en-vi

`quickmt-en-vi` Neural Machine Translation Model

Usage with `quickmt`