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token_embedder

allennlp.modules.token_embedders.token_embedder

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TokenEmbedder#

class TokenEmbedder(torch.nn.Module,  Registrable)

A TokenEmbedder is a Module that takes as input a tensor with integer ids that have been output from a TokenIndexer and outputs a vector per token in the input. The input typically has shape (batch_size, num_tokens) or (batch_size, num_tokens, num_characters), and the output is of shape (batch_size, num_tokens, output_dim). The simplest TokenEmbedder is just an embedding layer, but for character-level input, it could also be some kind of character encoder.

We add a single method to the basic Module API: get_output_dim(). This lets us more easily compute output dimensions for the TextFieldEmbedder, which we might need when defining model parameters such as LSTMs or linear layers, which need to know their input dimension before the layers are called.

default_implementation#

class TokenEmbedder(torch.nn.Module,  Registrable):
 | ...
 | default_implementation = "embedding"

get_output_dim#

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

Returns the final output dimension that this TokenEmbedder uses to represent each token. This is not the shape of the returned tensor, but the last element of that shape.