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---
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.
|