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--- |
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language: |
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- vi |
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license: mit |
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task_categories: |
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- text-generation |
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- text2text-generation |
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tags: |
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- spelling-correction |
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- grammatical-error-correction |
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- synthetic-data |
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- vietnam |
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size_categories: |
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- 100K<n<1M |
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--- |
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# Vietnamese Spelling Correction Dataset |
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This dataset contains **978,417** pairs of noisy (source) and clean (target) Vietnamese sentences, designed for training spelling correction models. |
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The dataset was synthetically generated by injecting realistic noise into a clean Vietnamese corpus. |
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## Dataset Structure |
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The dataset is divided into training and testing sets: |
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- **Train**: 880,575 examples |
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- **Test**: 97,842 examples |
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### Data Fields |
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- `source`: The text with injected errors (input). |
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- `target`: The original clean text (label). |
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### Sample |
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| Source (Noisy) | Target (Clean) | Error Type | |
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| :--- | :--- | :--- | |
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| Căn **bật** hai của số âm có thể được **ban** luận... | Căn **bậc** hai của số âm có thể được **bàn** luận... | Phonological + Unaccented | |
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| ...xuất **hien** **thườg** xuyên **trog** **kák**... | ...xuất **hiện** **thường** xuyên **trong** **các**... | Multiple realistic typos | |
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| ...máy **tín** bỏ túi đều có phím... | ...máy **tính** bỏ túi đều có phím... | Phonological (nh->n) | |
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## Noise Generation Logic |
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The noise was generated using a custom script with a **0.5 noise rate** (approx. 50% of tokens affected) and guaranteed at least one error per sample. The errors mimic real-world Vietnamese typing and spelling mistakes: |
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1. **Teencode & Lexical Variants** (~40%): |
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- Syllable contractions: `ng` $\to$ `g`, `nh` $\to$ `h`, `qu` $\to$ `w`, `yê` $\to$ `i`. |
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- Phonetic substitutions: `ph` $\to$ `f`, `gi` $\to$ `j`, `c/k` $\to$ `k`. |
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- Dictionary slang: `vợ` $\to$ `vk`, `không` $\to$ `ko`. |
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2. **Regional Phonological Errors** (~30%): |
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- **North**: `tr` $\leftrightarrow$ `ch`, `s` $\leftrightarrow$ `x`, `r` $\leftrightarrow$ `d` $\leftrightarrow$ `gi`. |
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- **South**: `n` $\leftrightarrow$ `ng` (final), `t` $\leftrightarrow$ `c`. |
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3. **Typing & Mechanical Errors** (~20%): |
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- **Spatial**: Hitting adjacent keys on QWERTY keyboard. |
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- **Telex**: Wrong accent codes (`s` $\to$ `d`), double typing (`đ` $\to$ `ddd`). |
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- **Operations**: Random insertions, deletions, transpositions. |
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4. **Unaccented** (~10%): |
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- Removing tone marks (e.g., `trường` $\to$ `truong`). |
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## Usage |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("coung21/vi-spelling-correction") |
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print(dataset["train"][0]) |
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# Output: {'source': '...', 'target': '...'} |
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``` |
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## Credits |
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The source data for this dataset was extracted from a **Vietnamese Wikipedia dump**. |
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The noise was synthetically generated using a custom noise injection pipeline to simulate realistic Vietnamese spelling errors. |
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