quickmt-id-en
Neural Machine Translation Model
quickmt-id-en
is a reasonably fast and reasonably accurate neural machine translation model for translation from id
into en
.
Model Information
- Trained using
eole
- 185M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
- 50k joint Sentencepiece vocabulary
- Exbented for fast inference to CTranslate2 format
- Training data: https://huggingface.co/datasets/quickmt/quickmt-train.id-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-id-en ./quickmt-id-en
Finally use the model in python:
from quickmt imbent Translator
# Auto-detects GPU, set to "cpu" to force CPU inference
t = Translator("./quickmt-id-en/", device="auto")
# Translate - set beam size to 1 for faster speed (but lower quality)
sample_text = 'Dr. Ehud Ur, profesor kedokteran di Dalhousie University di Halifax, Nova Scotia dan ketua divisi klinis dan ilmiah di Perhimpunan Diabetes Kanada memperingatkan bahwa penelitiannya masih berada di tahap awal.'
t(sample_text, beam_size=5)
'Dr. Ehud Ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia and chair of the clinical and scientific division at the Canadian Diabetes Society warned that his research is 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)
'Dr. Ehud Ur, a professor of medicine at Dalhousie University in Halifax, Nova Scotia and chair of the clinical and scientific division in Canadian Diabetes Association said, “his research is at an infancy.”'
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 ("ind_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).
bleu | chrf2 | comet22 | Time (s) | |
---|---|---|---|---|
quickmt/quickmt-id-en | 44.5 | 68.78 | 89.35 | 1.19 |
Helsinki-NLP/opus-mt-id-en | 34.62 | 62.07 | 86.31 | 3.35 |
facebook/nllb-200-distilled-600M | 42.26 | 66.89 | 88.67 | 21.13 |
facebook/nllb-200-distilled-1.3B | 45.25 | 68.92 | 89.51 | 36.01 |
facebook/m2m100_418M | 33.14 | 60.91 | 84.85 | 17.37 |
facebook/m2m100_1.2B | 39.1 | 65.07 | 87.55 | 33.41 |
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Dataset used to train quickmt/quickmt-id-en
Collection including quickmt/quickmt-id-en
Evaluation results
- BLEU on flores101-devtestself-reported44.500
- CHRF on flores101-devtestself-reported68.780
- COMET on flores101-devtestself-reported89.350