seed-generator

To generate labeled training data for vocabulary canonicalization, you can use automated tools that collect and label examples from sources like OpenAPI specifications, API-Bank, and synthetic variations, outputting stratified JSONL files with action, resource type, and sensitivity labels.

Stratified sampling in dataset creation ensures balanced representation across different categories like action, resource type, and sensitivity labels, preventing model bias and ensuring consistent training quality across diverse vocabulary inputs.

You can create training examples from OpenAPI specifications by systematically collecting and labeling data from those diverse sources, generating synthetic variations, and curating them to produce stratified JSONL files for AI model training.

Yes, this data generation approach works with JSONL and PyYAML, relying on these dependencies to parse configurations and output stratified JSONL files containing the labeled training examples for vocabulary canonicalization tasks.

The best way to ensure data quality when labeling training datasets is through validation scripts that verify the stratified JSONL output, ensuring balanced representation and consistent action, resource type, and sensitivity labels across all collected examples.