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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
---
<div style="display: flex; justify-content: space-between; align-items: flex-end; width: 100%; margin-bottom: 1rem;">
<h1 style="flex: 1; margin: 0;">Intent-Classifier-Dataset</h1>
<sub style="white-space: nowrap;">Made with ❤️ using 🦥 Unsloth Studio</sub>
</div>
---
intent-classifier was generated with Unsloth Recipe Studio. It contains 1,000 generated records.
---
## 🚀 Quick Start
```python
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 (%) | Null (%) | Details |
|--------|------|-------------|------------|----------|---------|
| `label` | `string` | sampler | 6 (0.6%) | 0 (0.0%) | category |
| `question` | `string` | llm-text | 998 (99.8%) | 0 (0.0%) | Tokens: 57 out / 801 in |
| `uuid` | `string` | sampler | 1000 (100.0%) | 0 (0.0%) | 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`](builder_config.json) and detailed metadata in [`metadata.json`](metadata.json).
---
## 📚 Citation
If you use Data Designer in your work, please cite the project as follows:
```bibtex
@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](https://github.com/NVIDIA-NeMo/DataDesigner) (`pip install data-designer`) |