List splits and configurations

List splits and configurations

Datasets typically have splits and may also have configurations. A split is a subset of the dataset, like train and test, that are used during different stages of training and evaluating a model. A configuration is a sub-dataset contained within a larger dataset. Configurations are especially common in multilingual speech datasets where there may be a different configuration for each language. If you’re interested in learning more about splits and configurations, check out the Load a dataset from the Hub tutorial!

This guide shows you how to use Datasets Server’s /splits endpoint to retrieve a dataset’s splits and configurations programmatically. Feel free to also try it out with Postman, RapidAPI, or ReDoc

The /splits endpoint accepts the dataset name as its query parameter:

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import requests
headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = "https://datasets-server.boincai.com/splits?dataset=duorc"
def query():
    response = requests.get(API_URL, headers=headers)
    return response.json()
data = query()

The endpoint response is a JSON containing a list of the dataset’s splits and configurations. For example, the duorc dataset has six splits and two configurations:

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{
  "splits": [
    { "dataset": "duorc", "config": "ParaphraseRC", "split": "train" },
    { "dataset": "duorc", "config": "ParaphraseRC", "split": "validation" },
    { "dataset": "duorc", "config": "ParaphraseRC", "split": "test" },
    { "dataset": "duorc", "config": "SelfRC", "split": "train" },
    { "dataset": "duorc", "config": "SelfRC", "split": "validation" },
    { "dataset": "duorc", "config": "SelfRC", "split": "test" }
  ],
  "pending": [],
  "failed": []
}