Data types

Datasets supported by the dataset viewer have a tabular format, meaning a data point is represented in a row and its features are contained in columns. Using the /first-rows endpoint allows you to preview the first 100 rows of a dataset and information about each feature. Within the features key, you’ll notice it returns a _type field. This value describes the data type of the column, and it is also known as a dataset’s Features.

There are several different data Features for representing different data formats such as Audio and Image for speech and image data respectively. Knowing a dataset feature gives you a better understanding of the data type you’re working with, and how you can preprocess it.

For example, the /first-rows endpoint for the Rotten Tomatoes dataset returns the following:

{"dataset": "cornell-movie-review-data/rotten_tomatoes",
 "config": "default",
 "split": "train",
 "features": [{"feature_idx": 0,
   "name": "text",
   "type": {"dtype": "string", 
   "id": null,
   "_type": "Value"}},
  {"feature_idx": 1,
   "name": "label",
   "type": {"num_classes": 2,
    "names": ["neg", "pos"],
    "id": null,
    "_type": "ClassLabel"}}],
  ...
 }

This dataset has two columns, text and label:

• The text column has a type of Value. The Value type is extremely versatile and represents scalar values such as strings, integers, dates, and even timestamp values.

• The label column has a type of ClassLabel. The ClassLabel type represents the number of classes in a dataset and their label names. Naturally, this means you’ll frequently see ClassLabel used in classification datasets.

For a complete list of available data types, take a look at the Features documentation.