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

A MultiLabelField is an extension of the LabelField that allows for multiple labels. It is particularly useful in multi-label classification where more than one label can be correct. As with the LabelField, 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 a vector of length equal to the vocabulary size with one hot encoding for the labels (all zeros, and ones for the labels).


  • labels : Sequence[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.
  • num_labels : int, optional (default = None)
    If skip_indexing=True, the total number of possible labels should be provided, which is required to decide the size of the output tensor. num_labels should equal largest label id + 1. If skip_indexing=False, num_labels is not required.


class MultiLabelField(Field[torch.Tensor]):
 | ...
 | def count_vocab_items(self, counter: Dict[str, Dict[str, int]])


class MultiLabelField(Field[torch.Tensor]):
 | ...
 | def index(self, vocab: Vocabulary)


class MultiLabelField(Field[torch.Tensor]):
 | ...
 | def get_padding_lengths(self) -> Dict[str, int]


class MultiLabelField(Field[torch.Tensor]):
 | ...
 | def as_tensor(self, padding_lengths: Dict[str, int]) -> torch.Tensor


class MultiLabelField(Field[torch.Tensor]):
 | ...
 | def empty_field(self)


class MultiLabelField(Field[torch.Tensor]):
 | ...
 | def human_readable_repr(self) -> Sequence[Union[str, int]]