---
license: cc-by-4.0
language:
- en
- vi
- ja
size_categories:
- 100K<n<1M
task_categories:
- translation
configs:
- config_name: default
  data_files:
  - split: train
    path: train.parquet
  - split: validation
    path: validation.parquet
  - split: test
    path: test.parquet
---

# EN-VI-JA Curated 500K Triplets

High-quality English-Vietnamese-Japanese translation triplets curated from OPUS parallel corpora.

## Dataset Statistics

| Split | Count |
|-------|-------|
| Train | 446,252 |
| Validation | 24,792 |
| Test | 24,792 |
| **Total** | **495,836** |

## Quality Metrics

- **Average Quality Score**: 0.794
- **Average LaBSE Score**: 0.859
- **LaBSE Threshold**: 0.6

### LaBSE Score Distribution

| Range | Count |
|-------|-------|
| 0.9-1.0 | 177,022 |
| 0.8-0.9 | 217,463 |
| 0.7-0.8 | 76,727 |
| 0.5-0.7 | 24,624 |

## Source Distribution

| Source | Count | Percentage |
|--------|-------|------------|
| CCAligned | 370,871 | 74.8% |
| HPLT | 98,412 | 19.8% |
| WikiMatrix | 13,887 | 2.8% |
| KDE4 | 8,364 | 1.7% |
| LaboroParaCorpus | 1,934 | 0.4% |
| Tatoeba | 1,178 | 0.2% |
| Ubuntu | 1,159 | 0.2% |
| GNOME | 11 | <0.1% |
| AMIMeeting | 11 | <0.1% |
| KFTT | 9 | <0.1% |

## Quality Pipeline (19 Steps)

### V2 Steps (10)
1. Language validation (FastText + script)
2. Deduplication
3. Cross-lingual coherence
4. Text normalization
5. Content quality filters
6. Japanese tokenization
7. Quality-based selection
8. Source quality weighting
9. Triplet length ratio
10. Encoding handling

### V3 Steps (9)
11. LaBSE semantic alignment
12. Broken English detection
13. Web artifact removal
14. B2B spam filtering
15. NSFW content filtering
16. Fragment detection
17. MT artifact detection
18. Script ratio validation
19. Character ratio validation

## Quality Thresholds

| Parameter | Value |
|-----------|-------|
| FastText confidence | 0.65 |
| Vietnamese diacritic ratio | 0.02 |
| Japanese script ratio | 0.10 |
| LaBSE threshold | 0.60 |
| Min tokens | 3 |
| Max tokens | 100 |
| Max length ratio | 2.5 |

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("sotalab/en-vi-ja-curated-500k-triplets")

# Access splits
train = dataset["train"]
val = dataset["validation"]
test = dataset["test"]

# Example
print(train[0])
# {'en': '...', 'vi': '...', 'ja': '...', 'source': '...', 'quality_score': 0.85, 'labse_score': 0.92}
```

## License

This dataset is released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{sotalab2024envija500k,
  title={EN-VI-JA Curated 500K Triplets},
  author={SotaLab},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/sotalab/en-vi-ja-curated-500k-triplets}
}
```