Using SimpleStrat block for generating diverse data

StratifiedGenerator: Generate Balanced Question-Answer Pairs

Overview

The StratifiedGenerator is a powerful tool for creating high-quality question-answer (QA) pairs with balanced and diverse coverage across your input questions. It ensures your generated dataset avoids biases and provides comprehensive representation of your input space.

📝 Research Background: For a comprehensive understanding of the methodology and theoretical foundation, see the paper: Stratified Generation for Artificial Data in Question Answering

Installation

pip install bespokelabs-curator

Quick Start Example

from datasets import Dataset
from bespokelabs.curator.blocks.simplestrat import StratifiedGenerator

# Create a simple dataset of questions
questions = Dataset.from_dict({"question": [f"{i}. Name a periodic element" for i in range(20)]})

# Initialize the generator with your preferred model
generator = StratifiedGenerator(model_name="gpt-4o-mini")

# Generate stratified QA pairs
qa_pairs = generator(questions).dataset

# Examine the results
print(f"Generated {len(qa_pairs)} QA pairs")
print(qa_pairs[0])  # View the first QA pair

How StratifiedGenerator Works

1. Input: A Dataset containing questions you want to generate answers for

2. Stratification Process: The algorithm:

   • Clusters similar questions together

   • Ensures balanced coverage across different question types

   • Prevents overrepresentation of common patterns

3. Model Integration: Uses your specified LLM to generate high-quality answers

4. Output: Returns a new dataset of QA pairs with well-distributed coverage

Advanced Usage

Customizing the Generator

# With custom parameters
generator = StratifiedGenerator(
    model_name="gpt-4o",  # Use a more powerful model
    generation_params={
        "temperature":0.7,      # Adjust creativity
    }
)

Saving and Loading Results

# Save your generated QA pairs
qa_pairs.push_to_hub('hf_org/dataset_name')

Performance Considerations

• Model Selection: Larger models (e.g., GPT-4) produce higher quality answers but cost more

Common Applications

• Creating balanced training datasets for QA systems

• Generating diverse test sets for robustness evaluation

• Augmenting existing datasets with additional QA pairs

• Creating instruction-tuning datasets with varied coverage

Troubleshooting

Q: My generated answers seem too similar across different questions. A: Try increasing the temperature parameter or the number of clusters.

Q: I'm getting API errors during generation. A: Try changing the model or backend params. You can check the API reference here.

Additional Resources

• BespokeLabs Documentation

Happy data generation!