.ipynb_checkpoints/README-checkpoint.md

---
language:
- en
- ja
tags:
- translation
license: cc-by-4.0
datasets:
- quickmt/quickmt-train.ja-en
model-index:
- name: quickmt-en-ja
  results:
  - task:
      name: Translation eng-jpn
      type: translation
      args: eng-jpn
    dataset:
      name: flores101-devtest
      type: flores_101
      args: eng_Latn jpn_Jpan devtest
    metrics:
    - name: CHRF
      type: chrf
      value: 42.04
    - name: COMET
      type: comet
      value: 89.08
---


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

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


## Model Information

* Trained using [`eole`](https://github.com/eole-nlp/eole)
* 185M parameter transformer 'big' with 8 encoder layers and 2 decoder layers
* 20k sentencepiece vocabularies
* Exported for fast inference to [CTranslate2](https://github.com/OpenNMT/CTranslate2) format
* Training data: https://huggingface.co/datasets/quickmt/quickmt-train.ar-en/tree/main

See the `eole` model configuration in this repository for further details and the `eole-model` for the raw `eole` (pytorch) model.


## Usage with `quickmt`

You must install the Nvidia cuda toolkit first, if you want to do GPU inference.

Next, install the `quickmt` python library and download the model:

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

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

Finally use the model in python:

```python
from quickmt import Translator

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

# Translate - set beam size to 5 for higher quality (but slower speed)
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.'
t(sample_text, beam_size=5)

> 'ノバスコシア州ハリファックスのダルハウジー大学の医学教授で、カナダ糖尿病協会の臨床および科学部門の議長であるEhud Ur博士は、研究はまだ初期段階にあると警告しました。'

# Get alternative translations by sampling
# You can pass any cTranslate2 `translate_batch` arguments
t([sample_text], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9)

> 'ノバスコシア州ハリファックスのダルハウジー大学教授で、カナダ糖尿病学会の臨床、科学部会長のエフド・ウル博士がこの研究はまだその初期段階にあると警告した。'

```

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"->"jpn_Jpan"). `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 using a large batch size).

|                                  |   chrf2 |   comet22 |   Time (s) |
|:---------------------------------|--------:|----------:|-----------:|
| quickmt/quickmt-en-ja            |   42.04 |     89.08 |       1.38 |
| Helsink-NLP/opus-mt-en-ja        |    6.41 |     62.91 |       7.09 |
| facebook/nllb-200-distilled-600M |   30.00 |     86.64 |      26.05 |
| facebook/nllb-200-distilled-1.3B |   32.38 |     88.02 |      46.04 |
| facebook/m2m100_418M             |   32.73 |     85.09 |      23.29 |
| facebook/m2m100_1.2B             |   35.83 |     87.78 |      43.89 |

`quickmt-en-ja` is the fastest and highest quality by a fair margin.