8Opt/vn-text-correction-0001

Dataset Card for Vietnamese Text Correction Dataset

Dataset Description

This dataset contains Vietnamese text pairs for training and evaluating text correction models. Each example consists of an erroneous text and its corrected version, making it ideal for:

• Grammar correction
• Spelling correction
• Text normalization
• Language model fine-tuning

Dataset Summary

• Language: Vietnamese (vi)
• Format: Text correction pairs
• Size: ~4.0M examples across train/validation/test splits
• Domain: General Vietnamese text from various sources
• Collection Method: Automated collection and human verification

Dataset Structure

Data Instances

A typical data point looks like this:

{
  "correct_text": "Đây là một câu tiếng Việt chuẩn xác.",
  "error_text": "Đây là một câu tieengs Việt chuẩn xác.",
  "__index_level_0__": 42
}

Data Fields

• correct_text: The corrected Vietnamese text (string)
• error_text: The original text with errors (string)

Data Splits

Split	Examples	Size
train	3,175,684	895 MB
test	396,961	112 MB
validation	396,961	112 MB

Dataset Creation

Source Data

The dataset was created by collecting Vietnamese text from multiple sources including:

• Web crawling of Vietnamese websites
• Social media posts
• News articles
• User-generated content

Processing Steps

1. Text Collection: Gathered raw Vietnamese text from diverse sources
2. Error Injection: Applied various error types (spelling, grammar, typos)
3. Human Annotation: Native speakers corrected the erroneous texts
4. Quality Filtering: Removed low-quality or nonsensical examples
5. Deduplication: Ensured no duplicate text pairs

Usage

Loading the Dataset

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("8Opt/vn-text-correction-0001")

# Access different splits
train_data = dataset['train']
test_data = dataset['test']
val_data = dataset['validation']

# Example usage
example = train_data[0]
print(f"Original: {example['error_text']}")
print(f"Corrected: {example['correct_text']}")

For Training

# Prepare for training a correction model
def preprocess_function(examples):
    inputs = [f"Correct this text: {err}" for err in examples['error_text']]
    targets = examples['correct_text']
    return {'input_text': inputs, 'target_text': targets}

# Apply preprocessing
tokenized_datasets = dataset.map(preprocess_function, batched=True)

Evaluation

Metrics

Common evaluation metrics for this dataset include:

• BLEU Score: Measures n-gram overlap with reference corrections
• ROUGE Score: Evaluates summary-quality corrections
• Character/Word Error Rate: Traditional correction metrics
• Human Evaluation: Manual assessment of correction quality

Limitations and Bias

• Domain Coverage: Dataset may not cover all Vietnamese domains equally
• Error Types: Focuses on common errors; rare error types may be underrepresented
• Regional Variations: Northern/Central/Southern Vietnamese differences may exist
• Contemporary Usage: May not capture very recent slang or terminology

Ethical Considerations

• All text data is publicly available Vietnamese content
• No personally identifiable information included
• Dataset intended for research and educational purposes only

Contributing

We welcome contributions! If you find issues or want to improve the dataset:

1. Open an issue on the https://huggingface.co/datasets/8Opt/vn-text-correction-0001.
2. Submit a pull request with improvements
3. Report any data quality issues or annotation errors

Contact

For questions or feedback about this dataset, please contact: [minh.leduc.0210@gmail.com]