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Datasets:
ninaxu
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faker-example

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rlaif
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faker-example
File size: 3,292 Bytes 
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---
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: column_content
    sequence: string
  splits:
  - name: train
    num_bytes: 4583
    num_examples: 4
  download_size: 7535
  dataset_size: 4583
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_content": [
        "594564793936",
        "422645724655",
        "142688374180",
        "151546611521",
        "685542688520",
        "041197636946",
        "742485071901",
        "259581023351",
        "242310937846",
        "161331443479",
        "089946558053",
        "892937709085",
        "371747353204",
        "130825763690",
        "715314093651",
        "199735005780",
        "776005192229",
        "533330763559",
        "133642433775",
        "400474040702",
        "236402665456",
        "359951161260",
        "858505534111",
        "035009831008",
        "909566483105",
        "849472289056",
        "234702877781",
        "264888822024",
        "047437476067",
        "482031650266",
        "275058435264",
        "042763642003",
        "504739016897",
        "052402347800",
        "661215629471",
        "346545308924",
        "790927754992",
        "927973073123",
        "500126151170",
        "989947453568",
        "769940564398",
        "043814193121",
        "215740713849",
        "301021291360",
        "322580292726",
        "033918946671",
        "482122191043",
        "637850719148",
        "368826758961",
        "267609231778"
    ],
    "column_name": "uplift_loan_id",
    "id_faker_arguments": {
        "args": {
            "letters": null,
            "text": "############"
        },
        "type": "id"
    }
}
```

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>