metadata

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

Dataset Card for faker-example

This dataset has been created with distilabel.

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:

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

or explore the configuration:

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:

Configuration: default

{
    "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:

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:

from datasets import load_dataset

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