size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif datacraft
---
size_categories: n<1K
dataset_info:
  features:
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': functional
          '1': optimized
          '2': reusable
          '3': maintainable
          '4': bug-free
          '5': efficient
          '6': well-structured
          '7': readable
          '8': scalable
          '9': testable
  splits:
  - name: train
    num_bytes: 27682
    num_examples: 100
  download_size: 13514
  dataset_size: 27682
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 PythonCode

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/HugScriptKitty/PythonCode/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/HugScriptKitty/PythonCode/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "labels": [
        0,
        1,
        5,
        6,
        8
    ],
    "text": "This implementation utilizes memoization to store the results of expensive function calls, thereby reducing redundant computations and improving performance in scenarios where the function is invoked multiple times with the same input."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("HugScriptKitty/PythonCode", "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("HugScriptKitty/PythonCode")
```


</details>




