Dataset features

datasets.Features define the internal structure and typings for each example in the dataset. Features are used to specify the underlying serailization format but also contain high-level informations regarding the fields, e.g. conversion methods from names to integer values for a class label field.

Here is a brief presentation of the various types of features which can be used to define the dataset fields (aka columns):

• datasets.Features is the base class and should be only called once and instantiated with a dictionary of field names and field sub-features as detailed in the rest of this list,

• a python dict specifies that the field is a nested field containing a mapping of sub-fields to sub-fields features. It’s possible to have nested fields of nested fields in an arbitrary manner.

• a python list or a datasets.Sequence specifies that the field contains a list of objects. The python list or datasets.Sequence should be provided with a single sub-feature as an example of the feature type hosted in this list. Python list are simplest to define and write while datasets.Sequence provide a few more specific behaviors like the possibility to specify a fixed length for the list (slightly more efficient).

Note

A datasets.Sequence with a internal dictionary feature will be automatically converted into a dictionary of lists. This behavior is implemented to have a compatilbity layer with the TensorFlow Datasets library but may be un-wanted in some cases. If you don’t want this behavior, you can use a python list instead of the datasets.Sequence.

• a datasets.ClassLabel feature specifies a field with a predefined set of classes which can have labels associated to them and will be stored as integers in the dataset. This field will be stored and retrieved as an integer value and two conversion methods, datasets.ClassLabel.str2int() and datasets.ClassLabel.int2str() can be used to convert from the label names to the associate integer value and vice-versa.

• a datasets.Value feature specifies a single typed value, e.g. int64 or string. The types supported are all the non-nested types of Apache Arrow among which the most commonly used ones are int64, float32 and string.

• finally, two features are specific to Machine Translation: datasets.Translation and datasets.TranslationVariableLanguages. We refer to the package reference for more details on these features.