Datasets:

razisayyed
/

intent-classifier-dataset

size_categories: 1K<n<10K
tags:
  - synthetic
  - datadesigner
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: label
      dtype: string
    - name: uuid
      dtype: string
    - name: question
      dtype: string
    - name: instructions
      dtype: string
  splits:
    - name: train
      num_bytes: 370899
      num_examples: 800
    - name: validation
      num_bytes: 46581
      num_examples: 100
    - name: test
      num_bytes: 46901
      num_examples: 100
  download_size: 114032
  dataset_size: 464381

Intent-Classifier-Dataset

_{Made with ❤️ using 🦥 Unsloth Studio}

intent-classifier was generated with Unsloth Recipe Studio. It contains 1,000 generated records.

🚀 Quick Start

from datasets import load_dataset

# Load the main dataset
dataset = load_dataset("razisayyed/intent-classifier-dataset", "data", split="train")
df = dataset.to_pandas()

📊 Dataset Summary

📈 Records: 1,000
📋 Columns: 3

📋 Schema & Statistics

Column	Type	Column Type	Unique (%)	Details
`label`	`string`	sampler	6 (0.6%)	category
`question`	`string`	llm-text	998 (99.8%)	Tokens: 57 out / 801 in
`uuid`	`string`	sampler	1000 (100.0%)	uuid

⚙️ Generation Details

Generated with 3 column configuration(s):

llm-text: 1 column(s)
sampler: 2 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)