column_name stringclasses 4 values | id_faker_arguments dict | column_content sequencelengths 50 50 |
|---|---|---|
uplift_loan_id | {
"args": {
"letters": null,
"text": "############"
},
"type": "id"
} | [
"594564793936",
"422645724655",
"142688374180",
"151546611521",
"685542688520",
"041197636946",
"742485071901",
"259581023351",
"242310937846",
"161331443479",
"089946558053",
"892937709085",
"371747353204",
"130825763690",
"715314093651",
"199735005780",
"776005192229",
"533330763... |
uplift_account_id | {
"args": {
"letters": "ABCDEFGHIJKLMNOPQRSTUVWXYZ",
"text": "?############"
},
"type": "id"
} | [
"X325164240523",
"O752543013521",
"H504838151804",
"L448379892732",
"F355827490093",
"I360428303252",
"V202232546213",
"R020670834224",
"V487745083524",
"G867996993524",
"S692673613595",
"D924286343407",
"J790012080981",
"C693341514536",
"M881945518587",
"H520579491468",
"W8137745259... |
ssn9 | {
"args": {
"letters": null,
"text": null
},
"type": "ssn"
} | [
"585-12-8690",
"752-90-0360",
"473-39-5426",
"315-66-1628",
"270-71-0861",
"800-37-3213",
"850-61-1480",
"270-72-1360",
"256-04-1255",
"083-05-5455",
"692-59-9303",
"451-14-6797",
"381-10-1879",
"009-87-9975",
"070-01-0700",
"764-09-2460",
"687-16-0549",
"334-21-6504",
"834-92-51... |
uuid | {
"args": {
"letters": null,
"text": null
},
"type": "uuid"
} | [
"45ccf878-24b6-4db3-8046-a93e458bb814",
"7d5ea0c4-dc66-414f-b8bb-91ae55c08136",
"da1ed280-8613-47f8-a4bc-d760bc8d2f78",
"499ebe50-342d-469a-9427-680279b8e1c8",
"423c02a0-f49c-417f-8fda-457a49c02140",
"330d718b-514b-4973-a0d0-58ef65386674",
"7c004cd2-7084-4f67-a33d-3f9ca28c0f4a",
"da083c5d-8dee-43fb-bf... |
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": [
"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:
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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