TheHorme/my-distiset-57479ac0

---
size_categories: n<1K
task_categories:
- text-classification
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': development
'1': debugging
'2': documentation
'3': deployment
'4': testing
'5': maintenance
splits:
- name: train
num_bytes: 49156
num_examples: 100
download_size: 26718
dataset_size: 49156
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 my-distiset-57479ac0

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/TheHorme/my-distiset-57479ac0/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/TheHorme/my-distiset-57479ac0/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:

<details><summary> Configuration: default </summary><hr>

```json
{
"label": 0,
"text": "This code snippet in Python utilizes the NumPy library to perform a Fast Fourier Transform (FFT) on a 2D array, followed by a filtering operation using a Butterworth filter. The code then applies a Least Absolute Shrinkage and Selection Operator (LASSO) regression model to a dataset, selecting the most relevant features for prediction. Furthermore, it leverages the scikit-learn library to implement a decision tree classifier and evaluate its performance using cross-validation techniques. Additionally, the code includes a function to generate a histogram of the frequency domain representation of the input signal, demonstrating its ability to visualize the frequency content of the signal."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("TheHorme/my-distiset-57479ac0", "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("TheHorme/my-distiset-57479ac0")
```

</details>