Skip to content

span_extractor

allennlp.modules.span_extractors.span_extractor

[SOURCE]

SpanExtractor#

class SpanExtractor(torch.nn.Module,  Registrable)

Many NLP models deal with representations of spans inside a sentence. SpanExtractors define methods for extracting and representing spans from a sentence.

SpanExtractors take a sequence tensor of shape (batch_size, timesteps, embedding_dim) and indices of shape (batch_size, num_spans, 2) and return a tensor of shape (batch_size, num_spans, ...), forming some representation of the spans.

forward#

class SpanExtractor(torch.nn.Module,  Registrable):
 | ...
 | @overrides
 | def forward(
 |     self,
 |     sequence_tensor: torch.FloatTensor,
 |     span_indices: torch.LongTensor,
 |     sequence_mask: torch.BoolTensor = None,
 |     span_indices_mask: torch.BoolTensor = None
 | )

Given a sequence tensor, extract spans and return representations of them. Span representation can be computed in many different ways, such as concatenation of the start and end spans, attention over the vectors contained inside the span, etc.

Parameters

  • sequence_tensor : torch.FloatTensor
    A tensor of shape (batch_size, sequence_length, embedding_size) representing an embedded sequence of words.
  • span_indices : torch.LongTensor
    A tensor of shape (batch_size, num_spans, 2), where the last dimension represents the inclusive start and end indices of the span to be extracted from the sequence_tensor.
  • sequence_mask : torch.BoolTensor, optional (default = None)
    A tensor of shape (batch_size, sequence_length) representing padded elements of the sequence.
  • span_indices_mask : torch.BoolTensor, optional (default = None)
    A tensor of shape (batch_size, num_spans) representing the valid spans in the indices tensor. This mask is optional because sometimes it's easier to worry about masking after calling this function, rather than passing a mask directly.

Returns

  • A tensor of shape (batch_size, num_spans, embedded_span_size),
  • where embedded_span_size depends on the way spans are represented.

get_input_dim#

class SpanExtractor(torch.nn.Module,  Registrable):
 | ...
 | def get_input_dim(self) -> int

Returns the expected final dimension of the sequence_tensor.

get_output_dim#

class SpanExtractor(torch.nn.Module,  Registrable):
 | ...
 | def get_output_dim(self) -> int

Returns the expected final dimension of the returned span representation.