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--- |
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language: |
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- zh |
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- en |
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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.zh-en |
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model-index: |
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- name: quickmt-zh-en |
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results: |
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- task: |
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name: Translation zho-eng |
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type: translation |
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args: zho-eng |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: zho_Hans eng_Latn devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 29.36 |
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- name: CHRF |
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type: chrf |
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value: 58.10 |
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--- |
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# `quickmt-zh-en` Neural Machine Translation Model |
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`quickmt-zh-en` is a reasonably fast and reasonably accurate neural machine translation model for translation from `zh` into `en`. |
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## Model Information |
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* Trained using [`eole`](https://github.com/eole-nlp/eole) |
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* 200M parameter transformer 'big' with 8 encoder layers and 2 decoder layers |
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* Separate source and target Sentencepiece tokenizers |
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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.zh-en/tree/main |
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See the `eole` model configuration in this repository for further details. |
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## Usage with `quickmt` |
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First, install `quickmt` 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-zh-en ./quickmt-zh-en |
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``` |
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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-zh-en/", device="auto") |
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# Translate - set beam size to 5 for higher quality (but slower speed) |
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t(["他补充道：“我们现在有 4 个月大没有糖尿病的老鼠，但它们曾经得过该病。”"], beam_size=1) |
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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(["他补充道：“我们现在有 4 个月大没有糖尿病的老鼠，但它们曾经得过该病。”"], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9) |
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``` |
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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 calculated with [sacrebleu](https://github.com/mjpost/sacrebleu) on the [Flores200 `devtest` test set](https://huggingface.co/datasets/facebook/flores) ("zho_Hans"->"eng_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. |
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| Model | bleu | chrf2 | comet22 | Time (s) | |
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| -------------------------------- | ----- | ----- | ---- | ---- | |
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| quickmt/quickmt-zh-en | 29.36 | 58.10 | 0.8655 | 0.88 | |
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| Helsinki-NLP/opus-mt-zh-en | 23.35 | 53.60 | 0.8426 | 3.78 | |
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| facebook/m2m100_418M | 15.99 | 50.13 | 0.7881 | 16.61 | |
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| facebook/nllb-200-distilled-600M | 26.22 | 55.18 | 0.8507 | 20.89 | |
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| facebook/m2m100_1.2B | 20.30 | 54.23 | 0.8206 | 33.12 | |
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| facebook/nllb-200-distilled-1.3B | 28.56 | 57.35 | 0.8620 | 36.64 | |
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`quickmt-zh-en` is the fastest *and* highest quality. |
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