column_name
stringclasses 3
values | id_faker_code
stringclasses 3
values | column_content
sequencelengths 50
50
|
|---|---|---|
uplift_loan_id | fake.unique.numerify(text='############') | [
"052-62-5574",
"153-09-4889",
"110-64-4156",
"231-69-6047",
"707-59-9857",
"526-74-5312",
"717-72-9707",
"506-29-4885",
"787-39-5814",
"243-30-5721",
"318-43-0594",
"842-18-9175",
"010-01-4215",
"522-19-4627",
"035-51-3798",
"851-86-9503",
"840-06-2008",
"578-65-7437",
"549-55-0723",
"883-69-5215",
"679-73-4304",
"835-78-4411",
"004-32-2250",
"842-02-6385",
"405-50-0960",
"773-62-0437",
"466-28-5423",
"398-99-9396",
"877-89-2372",
"263-18-4030",
"403-36-0978",
"176-33-9305",
"134-12-1934",
"860-33-2553",
"057-43-5904",
"343-50-2088",
"762-46-9140",
"489-63-1352",
"672-40-4984",
"233-43-6069",
"442-65-0333",
"514-22-7402",
"155-16-4011",
"297-49-5357",
"506-12-8326",
"613-71-0078",
"816-07-9014",
"497-83-8224",
"878-06-3848",
"520-57-3178"
] |
uplift_account_id | fake.unique.bothify(text='?############', letters=string.ascii_uppercase) | [
"013-64-7936",
"505-99-5662",
"490-69-5181",
"771-56-0046",
"682-41-9910",
"090-60-6766",
"234-11-0046",
"234-40-6622",
"179-40-9788",
"292-74-4859",
"708-98-2547",
"328-61-5184",
"065-42-8426",
"799-37-9478",
"199-49-2868",
"547-26-7775",
"055-38-4495",
"647-95-5483",
"218-96-9632",
"425-02-6339",
"496-16-2572",
"185-74-7245",
"566-01-0106",
"239-39-6781",
"381-78-0786",
"134-63-3501",
"460-75-8225",
"206-52-4450",
"855-50-1598",
"062-48-5637",
"519-33-7869",
"417-59-3553",
"031-50-4806",
"734-10-1531",
"220-68-7787",
"247-58-6070",
"305-01-7771",
"043-37-5614",
"176-89-3100",
"509-71-7240",
"316-75-1347",
"714-37-9573",
"702-01-3174",
"372-50-5927",
"652-27-5947",
"794-96-8157",
"105-57-2040",
"370-69-1793",
"064-01-5783",
"570-91-3564"
] |
ssn9 | fake.unique.ssn() | [
"647-40-8029",
"031-11-5355",
"797-43-7586",
"553-12-1863",
"622-97-2426",
"570-73-9179",
"536-97-7337",
"184-42-9595",
"002-39-6521",
"875-77-1838",
"547-40-2341",
"837-52-0805",
"766-55-2964",
"833-96-7619",
"054-40-8315",
"311-28-5517",
"440-97-8939",
"281-44-8869",
"508-65-1494",
"476-76-5467",
"621-28-4549",
"488-86-9274",
"269-01-6024",
"842-25-4811",
"343-34-6477",
"109-71-1463",
"581-65-7852",
"479-25-0047",
"370-77-7180",
"361-65-4882",
"649-44-0421",
"450-27-9198",
"037-65-7934",
"695-97-7788",
"399-75-7863",
"270-82-5986",
"607-35-5006",
"866-41-4553",
"118-13-4665",
"539-57-8594",
"866-81-4857",
"724-57-6442",
"818-62-0761",
"303-53-4602",
"625-04-1567",
"706-84-4897",
"048-75-6061",
"587-52-8962",
"056-14-8126",
"284-72-2952"
] |
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_content": [
"052-62-5574",
"153-09-4889",
"110-64-4156",
"231-69-6047",
"707-59-9857",
"526-74-5312",
"717-72-9707",
"506-29-4885",
"787-39-5814",
"243-30-5721",
"318-43-0594",
"842-18-9175",
"010-01-4215",
"522-19-4627",
"035-51-3798",
"851-86-9503",
"840-06-2008",
"578-65-7437",
"549-55-0723",
"883-69-5215",
"679-73-4304",
"835-78-4411",
"004-32-2250",
"842-02-6385",
"405-50-0960",
"773-62-0437",
"466-28-5423",
"398-99-9396",
"877-89-2372",
"263-18-4030",
"403-36-0978",
"176-33-9305",
"134-12-1934",
"860-33-2553",
"057-43-5904",
"343-50-2088",
"762-46-9140",
"489-63-1352",
"672-40-4984",
"233-43-6069",
"442-65-0333",
"514-22-7402",
"155-16-4011",
"297-49-5357",
"506-12-8326",
"613-71-0078",
"816-07-9014",
"497-83-8224",
"878-06-3848",
"520-57-3178"
],
"column_name": "uplift_loan_id",
"id_faker_code": "fake.unique.numerify(text=\u0027############\u0027)"
}
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")
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