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quickmt/
quickmt-da-en

Name: quickmt-da-en
Rating: 2.5 (17 reviews)

translation

quickmt/quickmt-train.da-en

cc-by-4.0

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`quickmt-da-en` Neural Machine Translation Model

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

Try it on our Huggingface Space

Give it a try before downloading here: https://huggingface.co/spaces/quickmt/QuickMT-gui

Model Information

Trained using eole
200M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
32k separate Sentencepiece vocabs
Exported for fast inference to CTranslate2 format
Training data: https://huggingface.co/datasets/quickmt/quickmt-train.da-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-da-en", ignore_patterns="eole-model/*"),
    device="cpu"
)

# Translate - set beam size to 1 for faster speed (but lower quality)
sample_text = 'Dr. Ehud Ur, professor i medicin på Dalhousie University i Halifax, Nova Scotia, og formand for den kliniske og videnskabelige afdeling af Canadian Diabetes Association, advarede om at forskningen stadig er i dens tidlige stadier.'

t(sample_text, beam_size=5)

'Dr. Ehud Ur, a professor of medicine at Dalhousie University in Halifax, Nova Scotia, and chairman of the clinical and scientific department of the Canadian Diabetes Association, warned that the research is still in its early stages.'

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)

'Dr Ehud Ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia, and chairman of the clinical and scientific branch of the Canadian Diabetes Association, warned that the research is still 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. A model in safetensors format to be used with eole is also provided.

Metrics

bleu and chrf2 are calculated with sacrebleu on the Flores200 devtest test set ("dan_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.

da -> en flores-devtest metrics

	bleu	chrf2	comet22	Time (s)
quickmt/quickmt-da-en	49.02	71.78	90	1.18
facebook/nllb-200-distilled-600M	47.44	70.14	89.46	20.96
facebook/nllb-200-distilled-1.3B	49.64	71.61	90.2	36.46
facebook/m2m100_418M	39.23	65.42	85.85	17.51
facebook/m2m100_1.2B	45.24	69.44	89	33.9

quickmt/quickmt-da-en

quickmt-da-en Neural Machine Translation Model