README.md

---
license: other
license_name: formosanbank-terms-ai-use-addendum
license_link: https://ai4commsci.gitbook.io/formosanbank/additional-resources/terms-of-use
pretty_name: FormosanBank Machine Translation
task_categories:
- translation
language:
- ami
- bnn
- ckv
- dru
- pwn
- pyu
- ssf
- sxr
- szy
- tao
- tay
- trv
- tsu
- xnb
- xsy
- en
- zh
size_categories:
- 100K<n<1M
tags:
- noncommercial
- no-commercial-ai
- translation
- machine-translation
- low-resource
- endangered-languages
- formosan-languages
- leakage-controlled
- hard-split
- synthetic-data
library_name: datasets
configs:
- config_name: formosan-en
  data_files:
  - split: train
    path: formosan_en_hf.csv
- config_name: formosan-zh
  data_files:
  - split: train
    path: formosan_zh_hf.csv
---

# FormosanBank Machine Translation

Public parallel corpora for 15 Indigenous Formosan languages aligned with English and Mandarin Chinese. This release uses canonical MT-standardized Formosan text, excludes `Formosan-Taiwan-Bible-Society-Bibles`, and keeps DeepL pivot translations in training only.

Commercial AI use is prohibited without prior written permission. See the [FormosanBank Terms of Use](https://ai4commsci.gitbook.io/formosanbank/additional-resources/terms-of-use).

## Release Summary

| Config | Rows | Train | Validate | Test | Synthetic train |
|---|---:|---:|---:|---:|---:|
| `formosan-en` | 328,943 | 296,930 | 10,674 | 21,339 | 222,274 |
| `formosan-zh` | 415,361 | 353,961 | 20,469 | 40,931 | 6,123 |
| **Total** | **744,304** | **650,891** | **31,143** | **62,270** | **228,397** |

Human pairs in every language use 70% train, 10% validation, and 20% test for Formosan-English, and 85% train, 5% validation, and 10% test for Formosan-Chinese. Evaluation contains only eligible human sentence references. Synthetic pivots and short entries are added to training, so final augmented-corpus percentages are more train-heavy.

## Languages

| Code | Language | English pairs | Chinese pairs |
|---|---|---:|---:|
| `ami` | Amis | 67,287 | 88,403 |
| `bnn` | Bunun | 27,609 | 36,112 |
| `ckv` | Kavalan | 15,335 | 17,410 |
| `dru` | Rukai | 34,711 | 45,769 |
| `pwn` | Paiwan | 24,518 | 30,265 |
| `pyu` | Puyuma | 23,226 | 29,940 |
| `ssf` | Thao | 10,893 | 12,913 |
| `sxr` | Saaroa | 8,080 | 10,955 |
| `szy` | Sakizaya | 11,635 | 13,754 |
| `tao` | Tao / Yami | 11,765 | 14,064 |
| `tay` | Atayal | 30,581 | 39,664 |
| `trv` | Seediq / Truku | 28,197 | 34,653 |
| `tsu` | Tsou | 9,077 | 10,616 |
| `xnb` | Kanakanavu | 13,647 | 17,050 |
| `xsy` | Saisiyat | 12,382 | 13,793 |

## Schema

The two main files preserve the established nine-column format: `id`, `source_lang`, `target_lang`, `source_sentence`, `target_sentence`, `lang_code`, `dialect`, `source`, and row-level `split`.

```python
from datasets import DatasetDict, load_dataset

rows = load_dataset("FormosanBank/formosan-mt", "formosan-en", split="train")
dataset = DatasetDict({
    split: rows.filter(lambda row: row["split"] == split)
    for split in ("train", "validate", "test")
})
```

Use `formosan-zh` for Chinese. Reverse-direction training can swap the sentence columns. Files under `provenance/` map release IDs to source commits and XML records and include independent validation and TAME-MT reports.

## Split Quality

The builder groups exact normalized pairs and punctuation skeletons, blocks one-edit conflicts, and excludes pair exposure at character 3-5 gram Jaccard similarity 0.95 or above. These are row-level hard splits, not document-held-out splits.

| Direction | Eval rows | TM BLEU | TM chrF2 | Mean source exposure | Source >= 0.70 |
|---|---:|---:|---:|---:|---:|
| English to Formosan | 32,013 | 3.70 | 19.54 | 0.256 | 0.943% |
| Formosan to English | 32,013 | 4.98 | 19.36 | 0.258 | 0.975% |
| Formosan to Chinese | 61,400 | 1.22 | 6.42 | 0.227 | 0.583% |
| Chinese to Formosan | 61,400 | 2.53 | 16.06 | 0.092 | 0.510% |

TM scores measure nearest-neighbor translation-memory retrieval, not model quality. All four directions have zero exact overlap and zero source, target, or pair exposure at 0.95.

## Provenance

- Public FormosanBank commit: `3a3c47c220520113f747e6a2d441494000e13c4b`
- FormosanBank QC commit: `acc3ec9f3137a59b9661e446a750ee1606720394`
- Toolkit commit: `573488b215ee1cf3701519a5bcd1010e8f0c6548`
- MT standardization: `formosan-mt-standard-v3`
- Build completed: 2026-08-24

Artifact hashes and per-language counts are recorded in `provenance/release_metadata.json` and `SHA256SUMS`.

## Limitations

- Sources vary in dialect, genre, translation style, and transcription quality.
- Some human references retain source or linguistic annotations.
- The English training corpus contains substantial synthetic augmentation.
- Similarity controls do not prove semantic or document independence.
- Automatic metrics do not replace evaluation by fluent speakers.

## Citation

```bibtex
@misc{formosanbank_mt_public_v3,
  title        = {FormosanBank Machine Translation Public Corpus},
  author       = {FormosanBank contributors},
  year         = {2026},
  howpublished = {https://huggingface.co/datasets/FormosanBank/formosan-mt}
}
```

See the [Terms of Use](https://ai4commsci.gitbook.io/formosanbank/additional-resources/terms-of-use) and [AI Use Addendum](https://github.com/FormosanBank/FormosanBank/blob/main/AI-USE-ADDENDUM.md).