[ allennlp.modules.text_field_embedders.text_field_embedder ]
class TextFieldEmbedder(torch.nn.Module, Registrable)
DataArrays produced by
TextFields are dictionaries with named representations, like
"words" and "characters". When you create a
TextField, you pass in a dictionary of
TokenIndexer objects, telling the field how exactly the
tokens in the field should be represented. This class changes the type signature of
TextFieldEmbedders to take inputs corresponding to a single
TextField, which is
a dictionary of tensors with the same names as were passed to the
We also add a method to the basic
get_output_dim(). You might need this
if you want to construct a
Linear layer using the output of this embedder, for instance.
class TextFieldEmbedder(torch.nn.Module, Registrable): | ... | default_implementation = "basic"
class TextFieldEmbedder(torch.nn.Module, Registrable): | ... | def forward( | self, | text_field_input: TextFieldTensors, | num_wrapping_dims: int = 0, | **kwargs | ) -> torch.Tensor
- text_field_input :
A dictionary that was the output of a call to
TextField.as_tensor. Each tensor in here is assumed to have a shape roughly similar to
(batch_size, sequence_length)(perhaps with an extra trailing dimension for the characters in each token).
- num_wrapping_dims :
int, optional (default =
If you have a
ListField[TextField]that created the
text_field_input, you'll end up with tensors of shape
(batch_size, wrapping_dim1, wrapping_dim2, ..., sequence_length). This parameter tells us how many wrapping dimensions there are, so that we can correctly
TimeDistributethe embedding of each named representation.
class TextFieldEmbedder(torch.nn.Module, Registrable): | ... | def get_output_dim(self) -> int
Returns the dimension of the vector representing each token in the output of this
TextFieldEmbedder. This is not the shape of the returned tensor, but the last element
of that shape.