Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
arrow
Languages:
Nepali
Size:
10M - 100M
License:
Uploading GEC dataset with 10 tags (incl. Swap/Merge/Split)
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- README.md +76 -0
- dataset_dict.json +1 -0
- gec_vocabulary.json +26 -0
- test/data-00000-of-00003.arrow +3 -0
- test/data-00001-of-00003.arrow +3 -0
- test/data-00002-of-00003.arrow +3 -0
- test/dataset_info.json +106 -0
- test/state.json +25 -0
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README.md
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---
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language:
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- ne
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license: mit
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task_categories:
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- token-classification
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tags:
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- grammatical-error-correction
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- gec
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- nepali
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- gector
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- sequence-tagging
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size_categories:
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- 1M<n<10M
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---
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# Nepali GEC (gector style) Token Tagging Dataset
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This is a processed version of the [sumitaryal/nepali_grammatical_error_correction](https://huggingface.co/datasets/sumitaryal/nepali_grammatical_error_correction) dataset,
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designed for training GEC-ToR-style sequence tagging models.
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This dataset has been processed with a robust, multi-pass, content-aware alignment algorithm
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to generate high-fidelity correction tags, **including complex and adjacent SWAP operations**.
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## Total Examples: 16,260,992
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* **Training:** 13,008,711
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* **Validation:** 2,439,231
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* **test:** 813,050
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## Key Features
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- **Token-Level Tags:** Word-level corrections are mapped to subword (token) labels.
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- **Correct Sentences Included:** The dataset contains both incorrect and correct sentences (tagged with `$KEEP`) for model stability.
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- **Stratified Splits:** Splits are stratified by the dominant error tag to ensure balanced evaluation.
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## Tag Vocabulary (10 Tags)
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This dataset uses an enhanced 10-tag system:
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1. **`$KEEP`**: Token is correct. -> label -> 0
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2. **`$DELETE`**: Token should be deleted. -> label -> 1
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3. **`$REPLACE`**: Token should be replaced (e.g., by a Transformer's MLM head). -> label -> 2
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4. **`$APPEND`**: A new token should be inserted *after* this token. -> label -> 3
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5. **`$SWAP_NEXT`**: Token is part of a swap (first word). -> label -> 4
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6. **`$SWAP_PREV`**: Token is part of a swap (second word). -> label -> 5
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7. **`$MERGE_NEXT`**: This token should be merged with the next token. -> label -> 6
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8. **`$MERGE_PREV`**: This token should be merged with the previous token. -> label -> 7
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9. **`$SPLIT`**: This token should be split into multiple tokens. -> label -> 8
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10. **`$UNKNOWN`**: Fallback tag (should not be present). -> label -> 9
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## Usage
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```python
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from datasets import load_dataset
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import json
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from huggingface_hub import hf_hub_download
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REPO_ID = "DipeshChaudhary/muril-nepali-gector-style-token-level-tag-for-ged"
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# Load dataset
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dataset = load_dataset(REPO_ID)
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# Load vocabulary
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vocab_file = hf_hub_download(
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repo_id=REPO_ID,
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filename="gec_vocabulary.json",
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repo_type="dataset"
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)
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with open(vocab_file, 'r', encoding='utf-8') as f:
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vocabulary = json.load(f)
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print(f"Splits: {list(dataset.keys())}")
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print(f"Tags: {vocabulary['tag_to_id']}")
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```
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dataset_dict.json
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{"splits": ["train", "validation", "test"]}
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gec_vocabulary.json
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{
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"tag_to_id": {
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"$KEEP": 0,
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"$DELETE": 1,
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"$REPLACE": 2,
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"$APPEND": 3,
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"$SWAP_NEXT": 4,
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"$SWAP_PREV": 5,
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"$MERGE_NEXT": 6,
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"$MERGE_PREV": 7,
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"$SPLIT": 8,
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"$UNKNOWN": 9
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},
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"id_to_tag": {
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"0": "$KEEP",
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"1": "$DELETE",
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"2": "$REPLACE",
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"3": "$APPEND",
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"4": "$SWAP_NEXT",
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"5": "$SWAP_PREV",
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"6": "$MERGE_NEXT",
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"7": "$MERGE_PREV",
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"8": "$SPLIT",
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"9": "$UNKNOWN"
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}
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}
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test/data-00000-of-00003.arrow
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version https://git-lfs.github.com/spec/v1
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size 461545816
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test/data-00001-of-00003.arrow
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test/dataset_info.json
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