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---
language:
- en
size_categories: n<1K
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence:
class_label:
names:
'0': feedback
'1': question
'2': greeting
'3': complaint
splits:
- name: train
num_bytes: 99754
num_examples: 499
download_size: 43026
dataset_size: 99754
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for intent-classification
This dataset has been created with [distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/Dev523/intent-classification/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/Dev523/intent-classification/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: default </summary><hr>
```json
{
"labels": [
1,
3
],
"text": "I recently purchased a product from your company, but it didn\u0027t meet my expectations. The material was flimsy and the design was not as sleek as I thought it would be."
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("Dev523/intent-classification", "default")
```
Or simply as it follows, since there's only one configuration and is named `default`:
```python
from datasets import load_dataset
ds = load_dataset("Dev523/intent-classification")
```
</details>