quickmt/quickmt-en-fa

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quickmt-en-fa Neural Machine Translation Model

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

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.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.

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

Finally, use the model in 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-fa", 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)

'دکتر ایهود اور، استاد پزشکی در دانشگاه دالهوزی در هالیفاکس، نوا اسکوشیا و رئیس بخش بالینی و علمی انجمن دیابت کانادا هشدار داد که این تحقیق هنوز در روزهای اولیه خود است.'

# 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)

'آقای دکتر اهود اور، استاد دارو در دانشگاه دالهوزي در هلیفکس، نوا اسکوشیا و رئیس بخش پزشکی و علمی جمعیت دیابت کانادا هشدار داد که تحقیقات انجام شده هنوز در روزهای اولیه هستند.'

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 ("engLatn"->"pesArab"). 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).