| --- |
| 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`) |
