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
Input: A
Dataset
containing questions you want to generate answers forStratification Process: The algorithm:
Clusters similar questions together
Ensures balanced coverage across different question types
Prevents overrepresentation of common patterns
Model Integration: Uses your specified LLM to generate high-quality answers
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
Happy data generation!
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