Datasets:

8Opt
/

vn-text-correction-0001

dataset_info:
  features:
    - name: correct_text
      dtype: string
    - name: error_text
      dtype: string
    - name: __index_level_0__
      dtype: int64
  splits:
    - name: train
      num_bytes: 895314409
      num_examples: 3175684
    - name: test
      num_bytes: 111815675
      num_examples: 396961
    - name: validation
      num_bytes: 112054245
      num_examples: 396961
  download_size: 742034799
  dataset_size: 1119184329
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: validation
        path: data/validation-*
license: mit
task_categories:
  - fill-mask
  - text-generation
  - token-classification
language:
  - vi

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

Text Collection: Gathered raw Vietnamese text from diverse sources
Error Injection: Applied various error types (spelling, grammar, typos)
Human Annotation: Native speakers corrected the erroneous texts
Quality Filtering: Removed low-quality or nonsensical examples
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:

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

Contact

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