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--- |
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language: |
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- en |
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- id |
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tags: |
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- translation |
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license: cc-by-4.0 |
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datasets: |
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- quickmt/quickmt-train.id-en |
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model-index: |
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- name: quickmt-en-id |
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results: |
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- task: |
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name: Translation eng-ind |
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type: translation |
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args: eng-ind |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: eng_Latn ind_Latn devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 48.69 |
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- name: CHRF |
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type: chrf |
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value: 71.95 |
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- name: COMET |
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type: comet |
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value: 91.02 |
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--- |
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# `quickmt-en-id` Neural Machine Translation Model |
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`quickmt-en-id` is a reasonably fast and reasonably accurate neural machine translation model for translation from `en` into `id`. |
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## Model Information |
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* Trained using [`eole`](https://github.com/eole-nlp/eole) |
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* 185M parameter transformer 'big' with 8 encoder layers and 2 decoder layers |
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* 20k sentencepiece vocabularies |
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* Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format |
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* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.id-en/tree/main |
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See the `eole` model configuration in this repository for further details and the `eole-model` for the raw `eole` (pytorch) model. |
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## Usage with `quickmt` |
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You must install the Nvidia cuda toolkit first, if you want to do GPU inference. |
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Next, install the `quickmt` python library and download the model: |
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```bash |
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git clone https://github.com/quickmt/quickmt.git |
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pip install ./quickmt/ |
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quickmt-model-download quickmt/quickmt-en-id ./quickmt-en-id |
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``` |
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Finally use the model in python: |
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```python |
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from quickmt import Translator |
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# Auto-detects GPU, set to "cpu" to force CPU inference |
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t = Translator("./quickmt-en-id/", device="auto") |
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# Translate - set beam size to 5 for higher quality (but slower speed) |
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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.' |
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t(sample_text, beam_size=5) |
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``` |
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> 'Dr Ehud Ur, profesor kedokteran di Universitas Dalhousie di Halifax, Nova Scotia dan ketua divisi klinis dan ilmiah dari Asosiasi Diabetes Kanada memperingatkan bahwa penelitian ini masih dalam hari-hari awal.' |
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```python |
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# Get alternative translations by sampling |
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# You can pass any cTranslate2 `translate_batch` arguments |
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t([sample_text], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9) |
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``` |
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> 'Prof. Ehud Ur, Profesor kedokteran di Dalhousie University, Halifax, Nova Scotia dan pimpinan divisi klinis dan ilmiah di Canadian Diabetes Association, memperingatkan bahwa penelitian ini masih belum mencapai tahap awal perkembangannya.' |
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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](https://libretranslate.com/) which also uses `ctranslate2` and `sentencepiece`. |
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## Metrics |
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`bleu` and `chrf2` are calculated with [sacrebleu](https://github.com/mjpost/sacrebleu) on the [Flores200 `devtest` test set](https://huggingface.co/datasets/facebook/flores) ("eng_Latn"->"ind_Latn"). `comet22` with the [`comet`](https://github.com/Unbabel/COMET) library and the [default model](https://huggingface.co/Unbabel/wmt22-comet-da). "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). |
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| | bleu | chrf2 | comet22 | Time (s) | |
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|:---------------------------------|-------:|--------:|----------:|-----------:| |
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| quickmt/quickmt-en-id | 48.69 | 71.95 | 91.02 | 1.13 | |
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| Helsinki-NLP/opus-mt-en-id | 39.71 | 66.5 | 88.24 | 3.08 | |
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| facebook/nllb-200-distilled-600M | 43.74 | 69.06 | 90.47 | 19.16 | |
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| facebook/nllb-200-distilled-1.3B | 46.14 | 70.7 | 91.39 | 33.09 | |
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| facebook/m2m100_418M | 36.77 | 63.8 | 87.13 | 16.93 | |
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| facebook/m2m100_1.2B | 42.62 | 68.04 | 89.76 | 32.4 | |
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