---
language:
- ja
- en
license: cc-by-4.0
library_name: transformers
pipeline_tag: translation
tags:
- translation
- japanese
- english
- seq2seq

datasets:
- Yokii2/patchouli-jaen
- Yokii2/kosuzu-jaen
---


<img width="1200" src="https://cdn-uploads.huggingface.co/production/uploads/693023df59c620db3c423915/3mETpcdzz2jUyyDbtrLm_.png">

# ScarletMT-Nano
A Japanese → English translation model (BART-based seq2seq), trained from scratch using the Hugging Face Trainer.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Yokii2/ScarletMT-Nano")
model = AutoModelForSeq2SeqLM.from_pretrained("Yokii2/ScarletMT-Nano")

inputs = tokenizer("ノバスコシア州ハリファックスにあるダルハウジー大学医学部教授でカナダ糖尿病協会の臨床・科学部門の責任者を務めるエフード・ウル博士は、この研究はまだ初期段階にあるとして注意を促しました。", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Evaluation

The model was evaluated on the `Yokii2/jmdict-ja-en-bench` benchmark (divided into short, medium, and long splits) using `sacrebleu` (BLEU, chrF2) and `COMET22`. 

The table below displays the performance comparison with other models, ordered from highest to lowest overall performance:

| Model | Model Path | Benchmark | BLEU | chrF2 | COMET22 |
| :--- | :--- | :--- | ---: | ---: | ---: |
| Custom (v2) | `Yokii2/quickmt-ja-en-v2` | short | **40.68** | **61.71** | **87.50** |
| Custom (v2) | `Yokii2/quickmt-ja-en-v2` | medium | **39.95** | **65.36** | **89.86** |
| Custom (v2) | `Yokii2/quickmt-ja-en-v2` | long | **37.52** | **64.64** | **88.38** |
| Base | `quickmt/quickmt-ja-en` | short | 33.45 | 53.83 | 84.35 |
| Base | `quickmt/quickmt-ja-en` | medium | 33.88 | 59.42 | 87.81 |
| Base | `quickmt/quickmt-ja-en` | long | 31.82 | 59.87 | 86.75 |
| **ScarletMT-Nano** | `Yokii2/ScarletMT-Nano` | short | 31.20 | 53.99 | 82.07 |
| **ScarletMT-Nano** | `Yokii2/ScarletMT-Nano` | medium | 30.15 | 58.87 | 86.50 |
| **ScarletMT-Nano** | `Yokii2/ScarletMT-Nano` | long | 29.03 | 58.80 | 85.33 |
| OPUS-MT | `Helsinki-NLP/opus-mt-ja-en` | short | 25.91 | 46.92 | 80.42 |
| OPUS-MT | `Helsinki-NLP/opus-mt-ja-en` | medium | 22.15 | 49.13 | 82.84 |
| OPUS-MT | `Helsinki-NLP/opus-mt-ja-en` | long | 20.26 | 48.65 | 81.15 |