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


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

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


## 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-en-zh ./quickmt-en-zh
```

Next use the model in python:

```python
from quickmt import Translator

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

# Translate - set beam size to 5 for higher quality (but slower speed)
t(["The Boot Monument is an American Revolutionary War memorial located in Saratoga National Historical Park in the state of New York."], beam_size=1)

# Get alternative translations by sampling
# You can pass any cTranslate2 `translate_batch` arguments
t(["The Boot Monument is an American Revolutionary War memorial located in Saratoga National Historical Park in the state of New York."], 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

`chrf2` is calculated with [sacrebleu](https://github.com/mjpost/sacrebleu) on the [Flores200 `devtest` test set](https://huggingface.co/datasets/facebook/flores) ("eng_Latn"->"zho_Hans"). `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                            | chrf2 | comet22 | Time (s) |
| -------------------------------- | ----- | ------- | -------- |
| quickmt/quickmt-en-zh            | 35.22 | 85.39  |  0.96    |
| Helsinki-NLP/opus-mt-en-zh       | 29.20 | 82.36  |  3.41    |
| facebook/m2m100_418M             | 25.86 | 73.76  | 16.71    |
| facebook/m2m100_1.2B             | 28.94 | 78.38  | 31.09    |
| facebook/nllb-200-distilled-600M | 24.52 | 78.41  | 19.01    |
| facebook/nllb-200-distilled-1.3B | 26.79 | 79.87  | 32.03    |

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