Xtestlab-Eternalyc-Fyi-1

_{Made with ❤️ using 🦥 Unsloth Studio}

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Dataset of fictional xtestlab that has a product eternalyc-fyi-1, which provides a solution for "subjugation of human biological aging". It contains 1000 rows of pure data.

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🚀 Quick Start

from datasets import load_dataset

# Load the main dataset
dataset = load_dataset("IDaniel8/xtestlab-eternalyc-fyi-1", "data", split="train")
df = dataset.to_pandas()

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📊 Dataset Summary

• 📈 Records: 1,000
• 📋 Columns: 3

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📋 Schema & Statistics

Column	Type	Column Type	Unique (%)	Null (%)	Details
evidence_quote	string	expression	15 (1.5%)	0 (0.0%)	-
answer	string	expression	23 (2.3%)	0 (0.0%)	-
question	string	expression	11 (1.1%)	0 (0.0%)	-

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⚙️ Generation Details

Generated with 7 column configuration(s):

• expression: 4 column(s)

• llm-structured: 1 column(s)

• seed-dataset: 2 column(s)

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

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📚 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},
}

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💡 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)