Principle:Openai Openai python Training Data Preparation
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Fine_Tuning |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
A data validation and remediation process that ensures training data conforms to the required format for fine-tuning language models.
Description
Training data preparation validates JSONL files containing prompt-completion pairs or chat-format messages. The validation framework checks format correctness, identifies duplicates, analyzes prompt/completion lengths, detects common prefix/suffix patterns, and suggests remediations. Properly formatted data is essential for successful fine-tuning jobs.
Usage
Use this principle before uploading training data for fine-tuning. Run the validation framework on your JSONL file to catch formatting issues, duplicates, and other problems that would cause the fine-tuning job to fail or produce poor results.
Theoretical Basis
Data preparation follows a Validation Pipeline:
# Validation flow
data = load_jsonl(file)
checks = [
check_format(data), # Correct JSONL structure
check_duplicates(data), # Remove duplicate entries
check_lengths(data), # Prompt/completion token lengths
check_prefix_suffix(data), # Common patterns to strip
check_whitespace(data), # Leading/trailing whitespace
]
if any_issues(checks):
remediated_data = apply_fixes(data, checks)
write_jsonl(remediated_data, output_file)