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composed_seq2seq

allennlp_models.generation.models.composed_seq2seq

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ComposedSeq2Seq#

@Model.register("composed_seq2seq")
class ComposedSeq2Seq(Model):
 | def __init__(
 |     self,
 |     vocab: Vocabulary,
 |     source_text_embedder: TextFieldEmbedder,
 |     encoder: Seq2SeqEncoder,
 |     decoder: SeqDecoder,
 |     tied_source_embedder_key: Optional[str] = None,
 |     initializer: InitializerApplicator = InitializerApplicator(),
 |     **kwargs
 | ) -> None

This ComposedSeq2Seq class is a Model which takes a sequence, encodes it, and then uses the encoded representations to decode another sequence. You can use this as the basis for a neural machine translation system, an abstractive summarization system, or any other common seq2seq problem. The model here is simple, but should be a decent starting place for implementing recent models for these tasks.

The ComposedSeq2Seq class composes separate Seq2SeqEncoder and SeqDecoder classes. These parts are customizable and are independent from each other.

Parameters

  • vocab : Vocabulary
    Vocabulary containing source and target vocabularies. They may be under the same namespace (tokens) or the target tokens can have a different namespace, in which case it needs to be specified as target_namespace.
  • source_text_embedders : TextFieldEmbedder
    Embedders for source side sequences
  • encoder : Seq2SeqEncoder
    The encoder of the "encoder/decoder" model
  • decoder : SeqDecoder
    The decoder of the "encoder/decoder" model
  • tied_source_embedder_key : str, optional (default = None)
    If specified, this key is used to obtain token_embedder in source_text_embedder and the weights are shared/tied with the decoder's target embedding weights.
  • initializer : InitializerApplicator, optional (default = InitializerApplicator())
    Used to initialize the model parameters.

forward#

class ComposedSeq2Seq(Model):
 | ...
 | def forward(
 |     self,
 |     source_tokens: TextFieldTensors,
 |     target_tokens: TextFieldTensors = None
 | ) -> Dict[str, torch.Tensor]

Make forward pass on the encoder and decoder for producing the entire target sequence.

Parameters

  • source_tokens : TextFieldTensors
    The output of TextField.as_array() applied on the source TextField. This will be passed through a TextFieldEmbedder and then through an encoder.
  • target_tokens : TextFieldTensors, optional (default = None)
    Output of Textfield.as_array() applied on target TextField. We assume that the target tokens are also represented as a TextField.

Returns

  • Dict[str, torch.Tensor]
    The output tensors from the decoder.

make_output_human_readable#

class ComposedSeq2Seq(Model):
 | ...
 | def make_output_human_readable(
 |     self,
 |     output_dict: Dict[str, torch.Tensor]
 | ) -> Dict[str, torch.Tensor]

Finalize predictions.

get_metrics#

class ComposedSeq2Seq(Model):
 | ...
 | def get_metrics(self, reset: bool = False) -> Dict[str, float]

default_predictor#

class ComposedSeq2Seq(Model):
 | ...
 | default_predictor = "seq2seq"