Datasets documentation

Dataset features

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# Dataset features

Features defines the internal structure of a dataset. It is used to specify the underlying serialization format. What’s more interesting to you though is that Features contains high-level information about everything from the column names and types, to the ClassLabel. You can think of Features as the backbone of a dataset.

The Features format is simple: dict[column_name, column_type]. It is a dictionary of column name and column type pairs. The column type provides a wide range of options for describing the type of data you have.

Let’s have a look at the features of the MRPC dataset from the GLUE benchmark:

>>> from datasets import load_dataset
>>> dataset = load_dataset('glue', 'mrpc', split='train')
>>> dataset.features
{'idx': Value(dtype='int32', id=None),
'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),
'sentence1': Value(dtype='string', id=None),
'sentence2': Value(dtype='string', id=None),
}

The Value feature tells 🤗 Datasets:

• The idx data type is int32.
• The sentence1 and sentence2 data types are string.

🤗 Datasets supports many other data types such as bool, float32 and binary to name just a few.

Refer to Value for a full list of supported data types.

The ClassLabel feature informs 🤗 Datasets the label column contains two classes. The classes are labeled not_equivalent and equivalent. Labels are stored as integers in the dataset. When you retrieve the labels, ClassLabel.int2str() and ClassLabel.str2int() carries out the conversion from integer value to label name, and vice versa.

If your data type contains a list of objects, then you want to use the Sequence feature. Remember the SQuAD dataset?

>>> from datasets import load_dataset
>>> dataset.features
'context': Value(dtype='string', id=None),
'id': Value(dtype='string', id=None),
'question': Value(dtype='string', id=None),
'title': Value(dtype='string', id=None)}

The answers field is constructed using the Sequence feature because it contains two subfields, text and answer_start, which are lists of string and int32, respectively.

See the flatten section to learn how you can extract the nested subfields as their own independent columns.

The array feature type is useful for creating arrays of various sizes. You can create arrays with two dimensions using Array2D, and even arrays with five dimensions using Array5D.

>>> features = Features({'a': Array2D(shape=(1, 3), dtype='int32'))

The array type also allows the first dimension of the array to be dynamic. This is useful for handling sequences with variable lengths such as sentences, without having to pad or truncate the input to a uniform shape.

>>> features = Features({'a': Array3D(shape=(None, 5, 2), dtype='int32')})