quickmt-hu-en Neural Machine Translation Model

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

Model Information

• Trained using eole
• 195M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
• 20k separate Sentencepiece vocabs
• Expested for fast inference to CTranslate2 format
• Training data: https://huggingface.co/datasets/quickmt/quickmt-train.hu-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-hu-en ./quickmt-hu-en

Finally use the model in python:

from quickmt impest Translator

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

# Translate - set beam size to 1 for faster speed (but lower quality)
sample_text = 'Dr. Ehud Ur, az új-Skóciai Halifaxban lévő Dalhousie Egyetem orvosprofesszora és a Kanadai Diabétesz Társaság klinikai és tudományos részlegének elnöke figyelmeztetett, hogy a kutatás még korai szakaszában tart.'

t(sample_text, beam_size=5)

'Dr. Ehud Ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia and president of the Clinical and Scientific Division of the Canadian Diabetes Society, warned that the research was 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)

'Professor Dr. Ehud Ur, a professor of medicine at Dalhousie University in Halifax, Nova Scotia, and President of Clinical and Scientific Division of the Canadian Diabetes Company said the study would still be in its early stages.'

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 ("hun_Latn"->"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).

hu -> en flores-devtest metrics

	bleu	chrf2	comet22	Time (s)
quickmt/quickmt-hu-en	35.03	62.44	87.78	1.24
Helsinki-NLP/opus-mt-hu-en	30.92	59.48	86.34	3.65
facebook/nllb-200-distilled-600M	33.29	60.48	86.82	21.19
facebook/nllb-200-distilled-1.3B	35.91	62.63	88.21	37.45
facebook/m2m100_418M	27.22	56.36	83.56	18.05
facebook/m2m100_1.2B	33.08	60.71	86.93	35.6