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--- |
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pretty_name: FormosanBank Machine Translation |
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license: cc-by-4.0 |
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task_categories: |
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- translation |
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language: |
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- ami |
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- bnn |
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- ckv |
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- dru |
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- pwn |
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- pyu |
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- ssf |
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- sxr |
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- szy |
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- tao |
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- tay |
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- trv |
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- tsu |
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- xnb |
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- xsy |
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- en |
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- zh |
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size_categories: |
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- 100K<n<1M |
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tags: |
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- translation |
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- machine-translation |
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- low-resource |
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- endangered-languages |
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- formosan-languages |
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- text |
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library_name: datasets |
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configs: |
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- config_name: formosan-en |
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data_files: "formosan_en_hf.csv" |
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- config_name: formosan-zh |
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data_files: "formosan_zh_hf.csv" |
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--- |
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# FormosanBank Machine Translation |
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Parallel corpora for 15 Indigenous Formosan languages aligned to English and Mandarin Chinese, prepared for use with the Hugging Face `datasets` library. |
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The dataset aggregates processed sentence- and phrase-level corpora into two CSV files: |
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- **Formosan → English** (`formosan_en_hf.csv`) |
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- **Formosan → Chinese** (`formosan_zh_hf.csv`) |
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Each row is a single bilingual sentence pair with language, dialect, split, and provenance metadata. The dataset is designed for training and evaluating neural machine translation (NMT) and related models for low-resource Formosan languages. |
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> **IMPORTANT DISCLAIMER:** |
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> Our Machine Translation models published on HuggingFace and in our papers were trained on this data in addition to private data not available to the public due to content restrictions. |
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> |
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--- |
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## Dataset Summary |
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- **Total sentence pairs:** 393,634 |
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- **Formosan → English:** 85,144 |
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- **Formosan → Chinese:** 308,490 |
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- **Languages (15):** Amis, Bunun, Kavalan, Rukai, Paiwan, Puyuma, Thao, Saaroa, Sakizaya, Yami/Tao, Atayal, Seediq/Truku, Tsou, Kanakanavu, Saisiyat |
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- **Targets:** English (`en`), Mandarin Chinese (`zh`) |
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- **Splits (all languages, both targets combined):** |
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- Train: 334,772 |
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- Validate: 29,412 |
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- Test: 29,450 |
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- **License:** CC BY 4.0 |
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- **Format:** UTF-8 CSV, one sentence pair per row |
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The dataset is intended to support research on low-resource MT, cross-lingual transfer, and documentation of endangered Formosan languages. |
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--- |
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## Supported Tasks and Use Cases |
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**Primary task** |
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- `translation` |
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- Formosan language → English |
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- Formosan language → Chinese |
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**Example use cases** |
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- Training NMT systems (e.g. NLLB / encoder–decoder models) for individual Formosan languages. |
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- Cross-lingual pretraining and evaluation for multilingual models. |
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- Dialect-aware MT experiments using the `dialect` field. |
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- Lexicon / dictionary-style MT from short phrases and headwords. |
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--- |
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## Languages and Coverage |
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High-level sentence counts per language (summing both directions: Formosan→English and Formosan→Chinese): |
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| Language | Formosan→English | Formosan→Chinese | Total | |
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|---------------|------------------|------------------|--------| |
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| Amis | 10,523 | 30,646 | 41,169 | |
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| Bunun | 9,006 | 30,878 | 39,884 | |
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| Kavalan | 2,098 | 14,682 | 16,780 | |
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| Rukai | 11,850 | 39,360 | 51,210 | |
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| Paiwan | 9,806 | 24,015 | 33,821 | |
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| Puyuma | 7,199 | 26,154 | 33,353 | |
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| Thao | 2,086 | 11,633 | 13,719 | |
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| Saaroa | 2,130 | 9,819 | 11,949 | |
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| Sakizaya | 2,132 | 11,318 | 13,450 | |
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| Yami/Tao | 3,009 | 12,792 | 15,801 | |
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| Atayal | 11,724 | 35,471 | 47,195 | |
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| Seediq/Truku | 7,244 | 29,840 | 37,084 | |
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| Tsou | 2,117 | 8,861 | 10,978 | |
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| Kanakanavu | 2,105 | 11,904 | 14,009 | |
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| Saisiyat | 2,115 | 11,117 | 13,232 | |
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| **TOTAL** | **85,144** | **308,490** | **393,634** | |
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Many languages also include **dialect labels**, for example: |
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- Amis: UNKNOWN, Southern, Malan, Coastal, Xiuguluan, Hengchun |
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- Bunun: UNKNOWN, Junqun, Luanqun, Kaqun, Tanqun, Zhuoqun |
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- Paiwan, Puyuma, Rukai, Atayal, Seediq/Truku: multiple dialects |
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- Others (e.g. Kavalan, Thao, Saaroa, Tsou, Kanakanavu, Saisiyat, Sakizaya, Yami/Tao) currently use `UNKNOWN` dialect |
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Dialect coverage makes it possible to do dialect-specific MT or robustness studies. |
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--- |
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## Dataset Structure |
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### Data Files |
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- `formosan_en_hf.csv` – all Formosan→English pairs |
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- `formosan_zh_hf.csv` – all Formosan→Chinese pairs |
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Each file contains all languages and splits. The **language direction** and **split** are specified per row. |
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### Data Fields |
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All CSVs share the same schema: |
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```text |
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id,source_lang,target_lang,source_sentence,target_sentence,lang_code,dialect,source,split |
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```` |
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* `id` *(int)* – unique row identifier within each file. |
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* `source_lang` *(str)* – language code of the Formosan language (e.g. `"ami"`, `"bnn"`). |
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* `target_lang` *(str)* – target language code (`"en"` or `"zh"`). |
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* `source_sentence` *(str)* – sentence or phrase in the Formosan language. |
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* `target_sentence` *(str)* – translation into the target language. |
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* `lang_code` *(str)* – canonical code for the Formosan language (usually same as `source_lang`). |
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* `dialect` *(str)* – dialect label (e.g. `"Southern"`, `"Malan"`, `"UNKNOWN"`). |
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* `source` *(str)* – provenance string or original file path in the upstream corpora. |
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* `split` *(str)* – one of `"train"`, `"validate"`, `"test"`. |
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### Splits |
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Splits are defined **per row** via the `split` column: |
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* `train` – training data |
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* `validate` – development / validate data |
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* `test` – held-out test data |
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Global totals across all languages and directions: |
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* Train: 334,772 |
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* Validate: 29,412 |
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* Test: 29,450 |
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Users can filter to any language pair and then re-group into a `DatasetDict` by `split`. |
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--- |
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## How to Load the Dataset |
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### 1. Install dependencies |
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```bash |
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pip install datasets |
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# optional, if you plan to fine-tune models: |
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pip install transformers |
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``` |
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### 2. Load the EN and ZH files from the Hub |
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Assume the dataset identifier is: |
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```text |
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FormosanBankDemos/formosan-mt |
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``` |
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Load both CSVs: |
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```python |
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from datasets import load_dataset |
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HF_ID = "FormosanBankDemos/formosan-mt" |
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# Formosan → English |
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ds_en_all = load_dataset( |
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HF_ID, |
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data_files="formosan_en_hf.csv", |
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)["train"] # entire CSV exposed as a 'train' split by default |
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# Formosan → Chinese |
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ds_zh_all = load_dataset( |
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HF_ID, |
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data_files="formosan_zh_hf.csv", |
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)["train"] |
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``` |
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Alternatively, if you rely on the YAML `configs` defined above: |
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```python |
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# Uses config_name: "formosan-en" from the README metadata |
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ds_en_all = load_dataset( |
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HF_ID, |
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name="formosan-en", |
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split="train", |
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) |
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``` |
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### 3. Filter to a specific language pair (example: Amis → English, `ami → en`) |
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```python |
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ami_en = ds_en_all.filter( |
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lambda ex: ex["source_lang"] == "ami" and ex["target_lang"] == "en" |
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) |
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print(ami_en) |
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# Dataset({ |
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# features: ['id', 'source_lang', 'target_lang', 'source_sentence', ...], |
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# num_rows: ... |
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# }) |
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``` |
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### 4. Get train / validation / test splits |
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```python |
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from datasets import DatasetDict |
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def split_by_column(ds): |
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return DatasetDict({ |
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"train": ds.filter(lambda ex: ex["split"] == "train"), |
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"validate": ds.filter(lambda ex: ex["split"] == "validate"), |
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"test": ds.filter(lambda ex: ex["split"] == "test"), |
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}) |
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ami_en_splits = split_by_column(ami_en) |
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print(ami_en_splits) |
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# DatasetDict({ |
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# train: Dataset({ ... }) |
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# validate: Dataset({ ... }) |
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# test: Dataset({ ... }) |
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# }) |
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``` |
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### 5. (Optional) Add a `translation` column |
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Many translation training scripts expect a `translation` field like `{"ami": "...", "en": "..."}`. You can construct it from existing columns: |
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```python |
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def add_translation(batch): |
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translations = [] |
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for src, tgt, sl, tl in zip( |
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batch["source_sentence"], |
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batch["target_sentence"], |
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batch["source_lang"], |
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batch["target_lang"], |
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): |
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translations.append({sl: src, tl: tgt}) |
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return {"translation": translations} |
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ami_en_splits = ami_en_splits.map(add_translation, batched=True) |
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print(ami_en_splits["train"][0]["translation"]) |
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# {'ami': "sa'osi", 'en': 'true'} |
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``` |
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You can reuse the same pattern for any other language pair: |
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```python |
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# Example: Paiwan → English |
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pwn_en = ds_en_all.filter( |
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lambda ex: ex["source_lang"] == "pwn" and ex["target_lang"] == "en" |
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) |
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pwn_en_splits = split_by_column(pwn_en) |
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``` |
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--- |
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## Intended Uses, Limitations, and Risks |
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### Intended Uses |
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* Research on **low-resource machine translation** for Formosan languages. |
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* Studies of **dialect variation** in MT via the `dialect` field. |
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* Baseline and benchmark datasets for multilingual models focusing on Austronesian languages. |
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### Limitations |
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* Domain coverage is heterogeneous (dictionary-style entries, short phrases, and some longer sentences); performance may not generalize to all real-world text genres. |
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* Dialect labels are not always available; some corpora use `UNKNOWN` for dialect. |
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* The dataset currently encodes translations only **into** English and Chinese, not between Formosan languages. |
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### Risks and Biases |
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* Source corpora may contain historical, religious, or culturally specific content that is not representative of contemporary language use. |
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* Translations may include inconsistencies or legacy orthography; users should verify quality before high-stakes use. |
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* As with any MT dataset for endangered languages, there is a risk of misinterpretation or over-reliance on automatically produced translations in sensitive cultural contexts. |
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Users should avoid deploying models trained on this dataset in critical or high-stakes settings without human expert review. |
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--- |
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## Citation |
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If you use this dataset in academic work, please cite the FormosanBank project and this dataset page. A generic citation format is: |
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FormosanBank annotations and metadata are CC-BY-4.0. This means you must cite the source in any redistributed or derived products. |
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For code packages, you may refer to the GitHub repository. For academic publications, you should cite Mohamed, W., Le Ferrand, É., Sung, L.-M., Prud'hommeaux, E., & Hartshorne, J. K. (2024). |
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FormosanBank. Electronic Resource. |
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> FormosanBankDemos. *FormosanBank Machine Translation Dataset*. Hugging Face Datasets. |
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> Available at: [https://huggingface.co/datasets/FormosanBankDemos/formosan-mt](https://huggingface.co/datasets/FormosanBankDemos/formosan-mt) |
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