bihungba1101/Vocab-CoEdIT

<div style="display: flex; justify-content: space-between; align-items: flex-end; width: 100%; margin-bottom: 1rem;"> <h1 style="flex: 1; margin: 0;">Vocab-Coedit</h1> <sub style="white-space: nowrap;">Made with ❤️ using 🦥 Unsloth Studio</sub> </div>

Vocab-CoEdIT contains 59,949 records of vocabulary suggestions

🚀 Quick Start

from datasets import load_dataset

# Load the main dataset
dataset = load_dataset("bihungba1101/Vocab-CoEdIT", "data", split="train")
df = dataset.to_pandas()

📊 Dataset Summary

• —📈 Records: 59,949
• —📋 Columns: 7

• —✅ Completion: 99.9% (60,000 requested)

📋 Schema & Statistics

⚙️ Generation Details

Generated with 7 column configuration(s):

• —expression: 2 column(s)

• —llm-text: 1 column(s)

• —seed-dataset: 4 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)