metadata

language:
  - en
  - ru
tags:
  - translation
license: cc-by-4.0
datasets:
  - quickmt/quickmt-train.ru-en
model-index:
  - name: quickmt-ru-en
    results:
      - task:
          name: Translation rus-eng
          type: translation
          args: rus-eng
        dataset:
          name: flores101-devtest
          type: flores_101
          args: rus_Cyrl eng_Latn devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 33.9
          - name: CHRF
            type: chrf
            value: 61.63
          - name: COMET
            type: comet
            value: 85.7

`quickmt-ru-en` Neural Machine Translation Model

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

Model Information

Trained using eole
185M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
50k joint Sentencepiece vocabulary
Exported for fast inference to CTranslate2 format
Training data: https://huggingface.co/datasets/quickmt/quickmt-train.ru-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-ru-en ./quickmt-ru-en

Finally use the model in python:

from quickmt import Translator

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

# Translate - set beam size to 1 for faster speed (but lower quality)
sample_text = 'Согласно предупреждению доктора Эхуда Ура (Ehud Ur), профессора медицины в Университете Дэлхаузи в Галифаксе (Новая Шотландия) и председателя клинико-научного отдела Канадской диабетической ассоциации, исследования все еще находятся на начальной стадии.'

t(sample_text, beam_size=5)

'According to the warning of Dr. Ehud Ur, Professor of Medicine at Dalhousie University in Halifax, Nova Scotia, and Chair of the Clinical Science Division of the Canadian Diabetes Association, the research is still in its infancy.'

# 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)

'According to the warning of Professor Ehud Ur, a Professor of Medicine at Dalhousie University in Halifax, Nova Scotia, and Chair of the Clinical and Scientific Division of the Canadian Diabetes Association, research is still in a very early stage.'

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 ("rus_Cyrl"->"eng_Latn"). comet22 with the comet library and the default model. "Time (s)" is the time in seconds to translate the flores-devtest dataset (1012 sentences) on an RTX 4070s GPU with batch size 32 (faster speed is possible using a larger batch size).

	bleu	chrf2	comet22	Time (s)
quickmt/quickmt-ru-en	33.9	61.63	85.7	1.31
Helsink-NLP/opus-mt-ru-en	30.04	58.23	83.97	3.72
facebook/nllb-200-distilled-600M	34.59	61.26	85.88	21.93
facebook/nllb-200-distilled-1.3B	36.99	63.04	86.59	38.12
facebook/m2m100_418M	26.62	56.31	81.77	18.73
facebook/m2m100_1.2B	32.01	60.3	85.01	35.99

quickmt-ru-en Neural Machine Translation Model

Model Information

Usage with quickmt

Metrics

`quickmt-ru-en` Neural Machine Translation Model

Usage with `quickmt`