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LabelField Objects#

class LabelField(Field[torch.Tensor]):
 | def __init__(
 |     self,
 |     label: Union[str, int],
 |     label_namespace: str = "labels",
 |     skip_indexing: bool = False
 | ) -> None

A LabelField is a categorical label of some kind, where the labels are either strings of text or 0-indexed integers (if you wish to skip indexing by passing skip_indexing=True). If the labels need indexing, we will use a Vocabulary to convert the string labels into integers.

This field will get converted into an integer index representing the class label.


  • label : Union[str, int]
  • label_namespace : str, optional (default = "labels")
    The namespace to use for converting label strings into integers. We map label strings to integers for you (e.g., "entailment" and "contradiction" get converted to 0, 1, ...), and this namespace tells the Vocabulary object which mapping from strings to integers to use (so "entailment" as a label doesn't get the same integer id as "entailment" as a word). If you have multiple different label fields in your data, you should make sure you use different namespaces for each one, always using the suffix "labels" (e.g., "passage_labels" and "question_labels").
  • skip_indexing : bool, optional (default = False)
    If your labels are 0-indexed integers, you can pass in this flag, and we'll skip the indexing step. If this is False and your labels are not strings, this throws a ConfigurationError.


 | @overrides
 | def count_vocab_items(self, counter: Dict[str, Dict[str, int]])


 | @overrides
 | def index(self, vocab: Vocabulary)


 | @overrides
 | def get_padding_lengths(self) -> Dict[str, int]


 | @overrides
 | def as_tensor(self, padding_lengths: Dict[str, int]) -> torch.Tensor


 | @overrides
 | def empty_field(self)