allennlp.modules.text_field_embedders.text_field_embedder#

TextFieldEmbedder#

TextFieldEmbedder(self)

A TextFieldEmbedder is a Module that takes as input the :class:~allennlp.data.DataArray produced by a :class:~allennlp.data.fields.TextField and returns as output an embedded representation of the tokens in that field.

The DataArrays produced by TextFields are dictionaries with named representations, like "words" and "characters". When you create a TextField, you pass in a dictionary of :class:~allennlp.data.TokenIndexer objects, telling the field how exactly the tokens in the field should be represented. This class changes the type signature of Module.forward, restricting TextFieldEmbedders to take inputs corresponding to a single TextField, which is a dictionary of tensors with the same names as were passed to the TextField.

We also add a method to the basic Module API: :func:get_output_dim(). You might need this if you want to construct a Linear layer using the output of this embedder, for instance.

default_implementation#

str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.str() (if defined) or repr(object). encoding defaults to sys.getdefaultencoding(). errors defaults to 'strict'.

forward#

TextFieldEmbedder.forward(self, text_field_input:Dict[str, Dict[str, torch.Tensor]], num_wrapping_dims:int=0, **kwargs) -> torch.Tensor

Parameters

  • text_field_input : TextFieldTensors 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=0) 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 TimeDistribute the embedding of each named representation.

get_output_dim#

TextFieldEmbedder.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.