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--- |
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language: |
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- vi |
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dataset_info: |
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features: |
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- name: text |
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dtype: string |
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- name: id |
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dtype: string |
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- name: domain |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 65506190827 |
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num_examples: 12169131 |
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download_size: 34648619492 |
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dataset_size: 65506190827 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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### Dataset Description |
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Vietnamese Curated Text Dataset. This dataset is collected from multiple open Vietnamese datasets, and curated with [NeMo Curator](https://github.com/NVIDIA/NeMo-Curator) |
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- **Developed by:** Viettel Solution |
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- **Language:** Vietnamese |
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### Details |
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#### Data Collection |
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We utilize a combination of datasets that contain samples in Vietnamese language, ensuring a robust and representative text corpus. These datasets include: |
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- The Vietnamese subset of the [C4 dataset](https://huggingface.co/datasets/allenai/c4/viewer/vi) . |
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- The Vietnamese subset of the [OSCAR dataset, version 23.01](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301/tree/main/vi_meta). |
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- [Wikipedia's Vietnamese articles](https://huggingface.co/datasets/wikimedia/wikipedia/viewer/20231101.vi). |
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- [Binhvq's Vietnamese news corpus](https://huggingface.co/datasets/jetaudio/binhvq_news). |
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#### Preprocessing |
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We use [NeMo Curator](https://github.com/NVIDIA/NeMo-Curator) to curate the collected data. The data curation pipeline includes these key steps: |
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1. Unicode Reformatting: Texts are standardized into a consistent Unicode format to avoid encoding issues. |
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2. Exact Deduplication: Removes exact duplicates to reduce redundancy. |
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3. Quality Filtering: |
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4. Heuristic Filtering: Applies rules-based filters to remove low-quality content. |
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5. Classifier-Based Filtering: Uses machine learning to classify and filter documents based on quality. |
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#### Dataset Statistics |
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**Content diversity** |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/661766c00c68b375f3f0ccc3/mW6Pct3uyP_XDdGmE8EP3.png" alt="Domain proportion in curated dataset" width="500"/> |
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**Character based metrics** |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/661766c00c68b375f3f0ccc3/W9TQjM2vcC7uXozyERHSQ.png" alt="Box plots of percentage of symbols, numbers, and whitespace characters compared to the total characters, word counts and average word lengths" width="900"/> |
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**Token count distribution** |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/661766c00c68b375f3f0ccc3/PDelYpBI0DefSmQgFONgE.png" alt="Distribution of document sizes (in terms of token count)" width="500"/> |
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**Embedding visualization** |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/661766c00c68b375f3f0ccc3/sfeoZWuQ7DcSpbmUOJ12r.png" alt="UMAP visualization of 5% of the dataset" width="650"/> |
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*UMAP visualization of 5% of the dataset* |
