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: label
    dtype:
      class_label:
        names:
          '0': ai-researcher
          '1': ai-architect
          '2': ai-engineer
          '3': ai-developer
          '4': ai-analyst
  splits:
  - name: train
    num_bytes: 2600
    num_examples: 10
  download_size: 4017
  dataset_size: 2600
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-88a662c1

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/BathSalt-1/my-distiset-88a662c1/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/BathSalt-1/my-distiset-88a662c1/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 0,
    "text": "I\u0027ve been experimenting with an ensemble of decision trees and random forests to improve the accuracy of my machine learning model, but I\u0027m concerned that the variance in predictions might be too high. Can someone suggest a way to reduce the variance while still maintaining the model\u0027s ability to generalize?"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("BathSalt-1/my-distiset-88a662c1", "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("BathSalt-1/my-distiset-88a662c1")
```


</details>




