Preparing High-Quality Datasets for Fine-Tuning
Preparing High-Quality Datasets for Fine-Tuning
Model quality depends heavily on dataset quality. Garbage data leads to poor model behavior.
1) Data Collection
- Internal documents
- Customer interactions
- Domain-specific FAQs
2) Data Cleaning
- Remove duplicates
- Fix formatting issues
- Eliminate biased samples
3) Structuring Input-Output Pairs
Each training sample should clearly map instruction to ideal output.
4) Dataset Validation
Split into train and validation sets. Measure generalization accuracy.
5) Summary
Careful dataset design determines fine-tuning success.
