README.md

metadata

size_categories: n<1K
tags:
  - synthetic
  - datadesigner
configs:
  - config_name: data
    data_files: data/*.parquet
    default: true

Test_dataset

_{Made with ❤️ using 🦥 Unsloth Studio}

---

my test dataset was generated with Unsloth Recipe Studio. It contains 100 generated records.

---

🚀 Quick Start

from datasets import load_dataset

# Load the main dataset
dataset = load_dataset("alwang/test_dataset", "data", split="train")
df = dataset.to_pandas()

---

📊 Dataset Summary

• 📈 Records: 100
• 📋 Columns: 2

---

📋 Schema & Statistics

Column	Type	Column Type	Unique (%)	Null (%)	Details
generated_instruction	string	llm-text	100 (100.0%)	0 (0.0%)	Tokens: 18 out / 152 in

---

⚙️ Generation Details

Generated with 4 column configuration(s):

• llm-text: 1 column(s)

• seed-dataset: 3 column(s)

📄 Full configuration available in builder_config.json and detailed metadata in metadata.json.

---

📚 Citation

If you use Data Designer in your work, please cite the project as follows:

@misc{nemo-data-designer,
  author = {The NeMo Data Designer Team, NVIDIA},
  title = {NeMo Data Designer: A framework for generating synthetic data from scratch or based on your own seed data},
  howpublished = {\url{https://github.com/NVIDIA-NeMo/DataDesigner}},
  year = 2026,
  note = {GitHub Repository},
}

---

💡 About NeMo Data Designer

NeMo Data Designer is a general framework for generating high-quality synthetic data that goes beyond simple LLM prompting. It provides:

• Diverse data generation using statistical samplers, LLMs, or existing seed datasets
• Relationship control between fields with dependency-aware generation
• Quality validation with built-in Python, SQL, and custom local and remote validators
• LLM-as-a-judge scoring for quality assessment
• Fast iteration with preview mode before full-scale generation

For more information, visit: https://github.com/NVIDIA-NeMo/DataDesigner (pip install data-designer)