label_field
allennlp.data.fields.label_field
LabelField¶
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.
Parameters¶
- 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 theVocabulary
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 isFalse
and your labels are not strings, this throws aConfigurationError
.
count_vocab_items¶
class LabelField(Field[torch.Tensor]):
| ...
| def count_vocab_items(self, counter: Dict[str, Dict[str, int]])
index¶
class LabelField(Field[torch.Tensor]):
| ...
| def index(self, vocab: Vocabulary)
get_padding_lengths¶
class LabelField(Field[torch.Tensor]):
| ...
| def get_padding_lengths(self) -> Dict[str, int]
as_tensor¶
class LabelField(Field[torch.Tensor]):
| ...
| def as_tensor(self, padding_lengths: Dict[str, int]) -> torch.Tensor
empty_field¶
class LabelField(Field[torch.Tensor]):
| ...
| def empty_field(self)
human_readable_repr¶
class LabelField(Field[torch.Tensor]):
| ...
| def human_readable_repr(self) -> Union[str, int]