---
size_categories: n<1K
dataset_info:
  features:
  - name: column_name
    dtype: string
  - name: id_faker_arguments
    struct:
    - name: args
      struct:
      - name: letters
        dtype: string
      - name: text
        dtype: string
    - name: type
      dtype: string
  - name: uplift_loan_id
    dtype: 'null'
  - name: uplift_account_id
    dtype: 'null'
  - name: ssn9
    dtype: 'null'
  - name: uuid
    dtype: 'null'
  splits:
  - name: train
    num_bytes: 167
    num_examples: 4
  download_size: 4093
  dataset_size: 167
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<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 faker-example

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/ninaxu/faker-example/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/ninaxu/faker-example/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "column_name": "uplift_loan_id",
    "id_faker_arguments": {
        "args": {
            "letters": null,
            "text": "############"
        },
        "type": "id"
    },
    "ssn9": null,
    "uplift_account_id": null,
    "uplift_loan_id": null,
    "uuid": null
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("ninaxu/faker-example", "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("ninaxu/faker-example")
```


</details>