Synthetic data is artificially generated data that mimics real-world data patterns. It's invaluable for bootstrapping ML projects, testing data pipelines, augmenting training sets, and prototyping before collecting real data.

This generator uses templates with variable placeholders to create diverse training examples for common NLP tasks—helping you quickly build evaluation datasets or demonstrate proof-of-concepts.

Supported Task Types

Question-Answering

Generates question-answer pairs about facts, definitions, and common knowledge.

Sentiment Analysis

Creates text samples with positive, negative, or neutral sentiment labels.

Text Classification

Generates news-like headlines with category labels (business, science, sports).

Use Cases for Synthetic Data

Pipeline testing: Validate data processing before collecting real data.
Prototype development: Build demos and MVPs without waiting for labeled datasets.
Data augmentation: Expand limited training sets with additional examples.
Privacy compliance: Train on synthetic data when real data contains PII.

FAQ

Can I use this for production training?

Template-based synthetic data is great for prototyping but limited in diversity. For production, combine with real data or use LLM-generated synthetic data.

How do I add custom templates?

This tool uses built-in templates. For custom generation, export the JSONL and modify it, or use the patterns as inspiration for your own generator.