--- 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 dtype: string splits: - name: train num_bytes: 12700 num_examples: 200 download_size: 7382 dataset_size: 12700 configs: - config_name: default data_files: - split: train path: data/train-* tags: - synthetic - distilabel - rlaif ---

Built with Distilabel

# 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:
Configuration: default
```json { "column_content": "138924849166", "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") ```