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quickmt-zh-en
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---
language:
- zh
- en
tags:
- translation
license: cc-by-4.0
datasets:
- quickmt/quickmt-train.zh-en
model-index:
- name: quickmt-zh-en
  results:
  - task:
      name: Translation zho-eng
      type: translation
      args: zho-eng
    dataset:
      name: flores101-devtest
      type: flores_101
      args: zho_Hans eng_Latn devtest
    metrics:
    - name: BLEU
      type: bleu
      value: 29.36
    - name: CHRF
      type: chrf
      value: 58.10
---


# `quickmt-zh-en` Neural Machine Translation Model 

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


## Model Information

* Trained using [`eole`](https://github.com/eole-nlp/eole)
* 200M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
* Separate source and target Sentencepiece tokenizers
* Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.zh-en/tree/main

See the `eole` model configuration in this repository for further details. 


## Usage with `quickmt`

First, install `quickmt` and download the model

```bash
git clone https://github.com/quickmt/quickmt.git
pip install ./quickmt/

quickmt-model-download quickmt/quickmt-zh-en ./quickmt-zh-en
```

```python
from quickmt import Translator

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

# Translate - set beam size to 5 for higher quality (but slower speed)
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)

# Get alternative translations by sampling
# You can pass any cTranslate2 `translate_batch` arguments
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)
```

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`.


## Metrics

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. 

| Model                            | bleu  | chrf2 | comet22  | Time (s) |
| -------------------------------- | ----- | ----- | ----     |  ----    |
| quickmt/quickmt-zh-en            | 29.36 | 58.10 | 0.8655   | 0.88     |
| Helsinki-NLP/opus-mt-zh-en       | 23.35 | 53.60 | 0.8426   | 3.78     |
| facebook/m2m100_418M             | 15.99 | 50.13 | 0.7881   | 16.61    |
| facebook/nllb-200-distilled-600M | 26.22 | 55.18 | 0.8507   | 20.89    |
| facebook/m2m100_1.2B             | 20.30 | 54.23 | 0.8206   | 33.12    |
| facebook/nllb-200-distilled-1.3B | 28.56 | 57.35 | 0.8620   | 36.64    |

`quickmt-zh-en` is the fastest *and* highest quality.