Model Database's logo

Datasets-server documentation

List Parquet files

Model Database's logo
Join the Model Database community

and get access to the augmented documentation experience

to get started

List Parquet files

Datasets can be published in any format (CSV, JSONL, directories of images, etc.) to the Hub, and they are easily accessed with the 🤗 Datasets library. For a more performant experience (especially when it comes to large datasets), Datasets Server automatically converts every public dataset to the Parquet format. The Parquet files are published to the Hub on a specific ref/convert/parquet branch (like this amazon_polarity branch for example).

In order for Datasets Server to generate a Parquet version of a dataset, the dataset must be public.

This guide shows you how to use Datasets Server’s /parquet endpoint to retrieve a list of a dataset’s files converted to Parquet. Feel free to also try it out with Postman, RapidAPI, or ReDoc.

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

Python
JavaScript
cURL 
import requests
headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = "https://datasets-server.huggingface.co/parquet?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 files in the Parquet format. For example, the duorc dataset has six Parquet files, which corresponds to the test, train and validation splits of its two configurations, ParaphraseRC and SelfRC (see the List splits and configurations guide for more details about splits and configurations).

The endpoint also gives the filename and size of each file:

{
  "parquet_files": [
    {
      "dataset": "duorc",
      "config": "ParaphraseRC",
      "split": "test",
      "url": "https://huggingface.co/datasets/duorc/resolve/refs%2Fconvert%2Fparquet/ParaphraseRC/test/0000.parquet",
      "filename": "0000.parquet",
      "size": 6136590
    },
    {
      "dataset": "duorc",
      "config": "ParaphraseRC",
      "split": "train",
      "url": "https://huggingface.co/datasets/duorc/resolve/refs%2Fconvert%2Fparquet/ParaphraseRC/train/0000.parquet",
      "filename": "0000.parquet",
      "size": 26005667
    },
    {
      "dataset": "duorc",
      "config": "ParaphraseRC",
      "split": "validation",
      "url": "https://huggingface.co/datasets/duorc/resolve/refs%2Fconvert%2Fparquet/ParaphraseRC/validation/0000.parquet",
      "filename": "0000.parquet",
      "size": 5566867
    },
    {
      "dataset": "duorc",
      "config": "SelfRC",
      "split": "test",
      "url": "https://huggingface.co/datasets/duorc/resolve/refs%2Fconvert%2Fparquet/SelfRC/test/0000.parquet",
      "filename": "0000.parquet",
      "size": 3035735
    },
    {
      "dataset": "duorc",
      "config": "SelfRC",
      "split": "train",
      "url": "https://huggingface.co/datasets/duorc/resolve/refs%2Fconvert%2Fparquet/SelfRC/train/0000.parquet",
      "filename": "0000.parquet",
      "size": 14851719
    },
    {
      "dataset": "duorc",
      "config": "SelfRC",
      "split": "validation",
      "url": "https://huggingface.co/datasets/duorc/resolve/refs%2Fconvert%2Fparquet/SelfRC/validation/0000.parquet",
      "filename": "0000.parquet",
      "size": 3114389
    }
  ]
}

Sharded Parquet files

Big datasets are partitioned into Parquet files (shards) of about 500MB each. The filename contains the name of the dataset, the split, the shard index, and the total number of shards (dataset-name-train-0000-of-0004.parquet). For a given split, the elements in the list are sorted by their shard index, in ascending order. For example, the train split of the amazon_polarity dataset is partitioned into 4 shards:

{
  "parquet_files": [
    {
      "dataset": "amazon_polarity",
      "config": "amazon_polarity",
      "split": "test",
      "url": "https://huggingface.co/datasets/amazon_polarity/resolve/refs%2Fconvert%2Fparquet/amazon_polarity/test/0000.parquet",
      "filename": "0000.parquet",
      "size": 117422359
   },
   {
      "dataset": "amazon_polarity",
      "config": "amazon_polarity",
      "split": "train",
      "url": "https://huggingface.co/datasets/amazon_polarity/resolve/refs%2Fconvert%2Fparquet/amazon_polarity/train/0000.parquet",
      "filename": "0000.parquet",
      "size": 320281121
    },
    {
      "dataset": "amazon_polarity",
      "config": "amazon_polarity",
      "split": "train",
      "url": "https://huggingface.co/datasets/amazon_polarity/resolve/refs%2Fconvert%2Fparquet/amazon_polarity/train/0001.parquet",
      "filename": "0001.parquet",
      "size": 320627716
    },
    {
      "dataset": "amazon_polarity",
      "config": "amazon_polarity",
      "split": "train",
      "url": "https://huggingface.co/datasets/amazon_polarity/resolve/refs%2Fconvert%2Fparquet/amazon_polarity/train/0002.parquet",
      "filename": "0002.parquet",
      "size": 320587882
    },
    {
      "dataset": "amazon_polarity",
      "config": "amazon_polarity",
      "split": "train",
      "url": "https://huggingface.co/datasets/amazon_polarity/resolve/refs%2Fconvert%2Fparquet/amazon_polarity/train/0003.parquet",
      "filename": "0003.parquet",
      "size": 66515954
    }
  ],
 "pending": [],
 "failed": []}

To read and query the Parquet files, take a look at the Query datasets from Datasets Server guide.

Partially converted datasets

The Parquet version can be partial if the dataset is not already in Parquet format or if it is bigger than 5GB.

In that case the Parquet files are generated up to 5GB and placed in a split directory prefixed with “partial”, e.g. “partial-train” instead of “train”.

Using the Model Database Hub API

For convenience, you can directly use the Model Database Hub /api/parquet endpoint which returns the list of Parquet URLs:

Python
JavaScript
cURL 
import requests
headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = "https://huggingface.co/api/datasets/duorc/parquet"
def query():
    response = requests.get(API_URL, headers=headers)
    return response.json()
urls = query()

The endpoint response is a JSON containing a list of the dataset’s files URLs in the Parquet format for each split and configuration. For example, the duorc dataset has one Parquet file for the train split of the “ParaphraseRC” configuration (see the List splits and configurations guide for more details about splits and configurations).

{
  "ParaphraseRC": {
    "test": [
      "https://huggingface.co/api/datasets/duorc/parquet/ParaphraseRC/test/0.parquet"
    ],
    "train": [
      "https://huggingface.co/api/datasets/duorc/parquet/ParaphraseRC/train/0.parquet"
    ],
    "validation": [
      "https://huggingface.co/api/datasets/duorc/parquet/ParaphraseRC/validation/0.parquet"
    ]
  },
  "SelfRC": {
    "test": [
      "https://huggingface.co/api/datasets/duorc/parquet/SelfRC/test/0.parquet"
    ],
    "train": [
      "https://huggingface.co/api/datasets/duorc/parquet/SelfRC/train/0.parquet"
    ],
    "validation": [
      "https://huggingface.co/api/datasets/duorc/parquet/SelfRC/validation/0.parquet"
    ]
  }
}

Optionally you can specify which configuration name to return, as well as which split:

Python
JavaScript
cURL 
import requests
headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = "https://huggingface.co/api/datasets/duorc/parquet/ParaphraseRC/train"
def query():
    response = requests.get(API_URL, headers=headers)
    return response.json()
urls = query()
[
  "https://huggingface.co/api/datasets/duorc/parquet/ParaphraseRC/train/0.parquet"
]

Each parquet file can also be accessed using its shard index: https://huggingface.co/api/datasets/duorc/parquet/ParaphraseRC/train/0.parquet redirects to https://huggingface.co/datasets/duorc/resolve/refs%2Fconvert%2Fparquet/ParaphraseRC/train/0000.parquet for example.

←Search text in a dataset Get the number of rows and the bytes size→