Datasets:
File size: 2,053 Bytes
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size_categories: n<1K
task_categories:
- text-classification
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': concerning
'1': unconcerning
'2': unsure
splits:
- name: train
num_bytes: 34797
num_examples: 92
download_size: 19669
dataset_size: 34797
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 Data
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/valcarese/Data/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/valcarese/Data/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: default </summary><hr>
```json
{
"label": 0,
"text": "The living room is cluttered with dirty laundry and old newspapers. There are broken appliances and torn furniture scattered all over the floor. The air is stale and smells of mold. The residents seem disorganized and confused."
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("valcarese/Data", "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("valcarese/Data")
```
</details>
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