In this quickstart, you’ll learn how to use the Datasets Server’s REST API to:
Each feature is served through an endpoint summarized in the table below:
Endpoint | Method | Description | Query parameters |
---|---|---|---|
/is-valid | GET | Check whether a specific dataset is valid. | dataset : name of the dataset |
/splits | GET | Get the list of configurations and splits of a dataset. | dataset : name of the dataset |
/first-rows | GET | Get the first rows of a dataset split. | - dataset : name of the dataset- config : name of the config- split : name of the split |
/rows | GET | Get a slice of rows of a dataset split. | - dataset : name of the dataset- config : name of the config- split : name of the split- offset : offset of the slice- length : length of the slice (maximum 100) |
/search | GET | Search text in a dataset split. | - dataset : name of the dataset- config : name of the config- split : name of the split- query : text to search for |
/filter | GET | Filter rows in a dataset split. | - dataset : name of the dataset- config : name of the config- split : name of the split- where : filter query- offset : offset of the slice- length : length of the slice (maximum 100) |
/parquet | GET | Get the list of parquet files of a dataset. | dataset : name of the dataset |
/size | GET | Get the size of a dataset. | dataset : name of the dataset |
/statistics | GET | Get statistics about a dataset split. | - dataset : name of the dataset- config : name of the config- split : name of the split |
/croissant | GET | Get Croissant metadata about a dataset. | - dataset : name of the dataset |
There is no installation or setup required to use Datasets Server.
Feel free to try out the API in Postman, ReDoc or RapidAPI. This quickstart will show you how to query the endpoints programmatically.
The base URL of the REST API is:
https://datasets-server.huggingface.co
For private and gated datasets, you’ll need to provide your user token in headers
of your query. Otherwise, you’ll get an error message to retry with authentication.
Datasets Server supports private datasets owned by a PRO user or an Enterprise Hub organization.
import requests
headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = "https://datasets-server.huggingface.co/is-valid?dataset=allenai/WildChat-nontoxic"
def query():
response = requests.get(API_URL, headers=headers)
return response.json()
data = query()
You’ll see the following error if you’re trying to access a gated dataset without providing your user token:
print(data)
{'error': 'The dataset does not exist, or is not accessible without authentication (private or gated). Please check the spelling of the dataset name or retry with authentication.'}
To check whether a specific dataset is valid, for example, Rotten Tomatoes, use the /is-valid
endpoint:
import requests
API_URL = "https://datasets-server.huggingface.co/is-valid?dataset=rotten_tomatoes"
def query():
response = requests.get(API_URL)
return response.json()
data = query()
This returns whether the dataset provides a preview (see /first-rows), the viewer (see /rows), the search (see /search) and the filter (see /filter):
{ "preview": true, "viewer": true, "search": true, "filter": true }
The /splits
endpoint returns a JSON list of the splits in a dataset:
import requests
API_URL = "https://datasets-server.huggingface.co/splits?dataset=rotten_tomatoes"
def query():
response = requests.get(API_URL)
return response.json()
data = query()
This returns the available configuration and splits in the dataset:
{
"splits": [
{ "dataset": "rotten_tomatoes", "config": "default", "split": "train" },
{
"dataset": "rotten_tomatoes",
"config": "default",
"split": "validation"
},
{ "dataset": "rotten_tomatoes", "config": "default", "split": "test" }
],
"pending": [],
"failed": []
}
The /first-rows
endpoint returns a JSON list of the first 100 rows of a dataset. It also returns the types of data features (“columns” data types). You should specify the dataset name, configuration name (you can find out the configuration name from the /splits
endpoint), and split name of the dataset you’d like to preview:
import requests
API_URL = "https://datasets-server.huggingface.co/first-rows?dataset=rotten_tomatoes&config=default&split=train"
def query():
response = requests.get(API_URL)
return response.json()
data = query()
This returns the first 100 rows of the dataset:
{
"dataset": "rotten_tomatoes",
"config": "default",
"split": "train",
"features": [
{
"feature_idx": 0,
"name": "text",
"type": { "dtype": "string", "_type": "Value" }
},
{
"feature_idx": 1,
"name": "label",
"type": { "names": ["neg", "pos"], "_type": "ClassLabel" }
}
],
"rows": [
{
"row_idx": 0,
"row": {
"text": "the rock is destined to be the 21st century's new \" conan \" and that he's going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .",
"label": 1
},
"truncated_cells": []
},
{
"row_idx": 1,
"row": {
"text": "the gorgeously elaborate continuation of \" the lord of the rings \" trilogy is so huge that a column of words cannot adequately describe co-writer/director peter jackson's expanded vision of j . r . r . tolkien's middle-earth .",
"label": 1
},
"truncated_cells": []
},
...,
...
]
}
The /rows
endpoint returns a JSON list of a slice of rows of a dataset at any given location (offset).
It also returns the types of data features (“columns” data types).
You should specify the dataset name, configuration name (you can find out the configuration name from the /splits
endpoint), the split name and the offset and length of the slice you’d like to download:
import requests
API_URL = "https://datasets-server.huggingface.co/rows?dataset=rotten_tomatoes&config=default&split=train&offset=150&length=10"
def query():
response = requests.get(API_URL)
return response.json()
data = query()
You can download slices of 100 rows maximum at a time.
The response looks like:
{
"features": [
{
"feature_idx": 0,
"name": "text",
"type": { "dtype": "string", "_type": "Value" }
},
{
"feature_idx": 1,
"name": "label",
"type": { "names": ["neg", "pos"], "_type": "ClassLabel" }
}
],
"rows": [
{
"row_idx": 150,
"row": {
"text": "enormously likable , partly because it is aware of its own grasp of the absurd .",
"label": 1
},
"truncated_cells": []
},
{
"row_idx": 151,
"row": {
"text": "here's a british flick gleefully unconcerned with plausibility , yet just as determined to entertain you .",
"label": 1
},
"truncated_cells": []
},
...,
...
],
"num_rows_total": 8530,
"num_rows_per_page": 100,
"partial": false
}
The /search
endpoint returns a JSON list of a slice of rows of a dataset that match a text query. The text is searched in the columns of type string
, even if the values are nested in a dictionary.
It also returns the types of data features (“columns” data types). The response format is the same as the /rows endpoint.
You should specify the dataset name, configuration name (you can find out the configuration name from the /splits
endpoint), the split name and the search query you’d like to find in the text columns:
import requests
API_URL = "https://datasets-server.huggingface.co/search?dataset=rotten_tomatoes&config=default&split=train&query=cat"
def query():
response = requests.get(API_URL)
return response.json()
data = query()
You can get slices of 100 rows maximum at a time, and you can ask for other slices using the offset
and length
parameters, as for the /rows
endpoint.
The response looks like:
{
"features": [
{
"feature_idx": 0,
"name": "text",
"type": { "dtype": "string", "_type": "Value" }
},
{
"feature_idx": 1,
"name": "label",
"type": { "dtype": "int64", "_type": "Value" }
}
],
"rows": [
{
"row_idx": 9,
"row": {
"text": "take care of my cat offers a refreshingly different slice of asian cinema .",
"label": 1
},
"truncated_cells": []
},
{
"row_idx": 472,
"row": {
"text": "[ \" take care of my cat \" ] is an honestly nice little film that takes us on an examination of young adult life in urban south korea through the hearts and minds of the five principals .",
"label": 1
},
"truncated_cells": []
},
...,
...
],
"num_rows_total": 12,
"num_rows_per_page": 100,
"partial": false
}
Datasets Server converts every dataset on the Hub to the Parquet format. The /parquet
endpoint returns a JSON list of the Parquet URLs for a dataset:
import requests
API_URL = "https://datasets-server.huggingface.co/parquet?dataset=rotten_tomatoes"
def query():
response = requests.get(API_URL)
return response.json()
data = query()
This returns a URL to the Parquet file for each split:
{
"parquet_files": [
{
"dataset": "rotten_tomatoes",
"config": "default",
"split": "test",
"url": "https://huggingface.co/datasets/rotten_tomatoes/resolve/refs%2Fconvert%2Fparquet/default/test/0000.parquet",
"filename": "0000.parquet",
"size": 92206
},
{
"dataset": "rotten_tomatoes",
"config": "default",
"split": "train",
"url": "https://huggingface.co/datasets/rotten_tomatoes/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet",
"filename": "0000.parquet",
"size": 698845
},
{
"dataset": "rotten_tomatoes",
"config": "default",
"split": "validation",
"url": "https://huggingface.co/datasets/rotten_tomatoes/resolve/refs%2Fconvert%2Fparquet/default/validation/0000.parquet",
"filename": "0000.parquet",
"size": 90001
}
],
"pending": [],
"failed": [],
"partial": false
}
The /size
endpoint returns a JSON with the size (number of rows and size in bytes) of the dataset, and for every configuration and split:
import requests
API_URL = "https://datasets-server.huggingface.co/size?dataset=rotten_tomatoes"
def query():
response = requests.get(API_URL)
return response.json()
data = query()
This returns the size of the dataset, and for every configuration and split:
{
"size": {
"dataset": {
"dataset": "rotten_tomatoes",
"num_bytes_original_files": 487770,
"num_bytes_parquet_files": 881052,
"num_bytes_memory": 1345449,
"num_rows": 10662
},
"configs": [
{
"dataset": "rotten_tomatoes",
"config": "default",
"num_bytes_original_files": 487770,
"num_bytes_parquet_files": 881052,
"num_bytes_memory": 1345449,
"num_rows": 10662,
"num_columns": 2
}
],
"splits": [
{
"dataset": "rotten_tomatoes",
"config": "default",
"split": "train",
"num_bytes_parquet_files": 698845,
"num_bytes_memory": 1074806,
"num_rows": 8530,
"num_columns": 2
},
{
"dataset": "rotten_tomatoes",
"config": "default",
"split": "validation",
"num_bytes_parquet_files": 90001,
"num_bytes_memory": 134675,
"num_rows": 1066,
"num_columns": 2
},
{
"dataset": "rotten_tomatoes",
"config": "default",
"split": "test",
"num_bytes_parquet_files": 92206,
"num_bytes_memory": 135968,
"num_rows": 1066,
"num_columns": 2
}
]
},
"pending": [],
"failed": [],
"partial": false
}