metadata_field

allennlp.data.fields.metadata_field

MetadataField¶

class MetadataField(Field[DataArray],  Mapping[str, Any]):
 | def __init__(self, metadata: Any) -> None

A MetadataField is a Field that does not get converted into tensors. It just carries side information that might be needed later on, for computing some third-party metric, or outputting debugging information, or whatever else you need. We use this in the BiDAF model, for instance, to keep track of question IDs and passage token offsets, so we can more easily use the official evaluation script to compute metrics.

We don't try to do any kind of smart combination of this field for batched input - when you use this Field in a model, you'll get a list of metadata objects, one for each instance in the batch.

Parameters¶

metadata : Any
Some object containing the metadata that you want to store. It's likely that you'll want this to be a dictionary, but it could be anything you want.

iter¶

class MetadataField(Field[DataArray],  Mapping[str, Any]):
 | ...
 | def __iter__(self)

get_padding_lengths¶

class MetadataField(Field[DataArray],  Mapping[str, Any]):
 | ...
 | def get_padding_lengths(self) -> Dict[str, int]

as_tensor¶

class MetadataField(Field[DataArray],  Mapping[str, Any]):
 | ...
 | def as_tensor(self, padding_lengths: Dict[str, int]) -> DataArray

empty_field¶

class MetadataField(Field[DataArray],  Mapping[str, Any]):
 | ...
 | def empty_field(self) -> "MetadataField"

batch_tensors¶

class MetadataField(Field[DataArray],  Mapping[str, Any]):
 | ...
 | def batch_tensors(
 |     self,
 |     tensor_list: List[DataArray]
 | ) -> List[DataArray]

human_readable_repr¶

class MetadataField(Field[DataArray],  Mapping[str, Any]):
 | ...
 | def human_readable_repr(self)