text_field_embedder: allennlp.modules.text_field_embedders.text_field_embedder.TextFieldEmbedder,
    encoder: allennlp.modules.seq2seq_encoders.seq2seq_encoder.Seq2SeqEncoder,
    tag_representation_dim: int,
    arc_representation_dim: int,
    tag_feedforward: allennlp.modules.feedforward.FeedForward = None,
    arc_feedforward: allennlp.modules.feedforward.FeedForward = None,
    pos_tag_embedding: allennlp.modules.token_embedders.embedding.Embedding = None,
    dropout: float = 0.0,
    input_dropout: float = 0.0,
    edge_prediction_threshold: float = 0.5,
    initializer: allennlp.nn.initializers.InitializerApplicator = <allennlp.nn.initializers.InitializerApplicator object at 0x7f28a44d3f60>,
) -> None

A Parser for arbitrary graph structures.

Registered as a Model with name "graph_parser".


  • vocab : Vocabulary, required A Vocabulary, required in order to compute sizes for input/output projections.
  • text_field_embedder : TextFieldEmbedder, required Used to embed the tokens TextField we get as input to the model.
  • encoder : Seq2SeqEncoder The encoder (with its own internal stacking) that we will use to generate representations of tokens.
  • tag_representation_dim : int, required. The dimension of the MLPs used for arc tag prediction.
  • arc_representation_dim : int, required. The dimension of the MLPs used for arc prediction.
  • tag_feedforward : FeedForward, optional, (default = None). The feedforward network used to produce tag representations. By default, a 1 layer feedforward network with an elu activation is used.
  • arc_feedforward : FeedForward, optional, (default = None). The feedforward network used to produce arc representations. By default, a 1 layer feedforward network with an elu activation is used.
  • pos_tag_embedding : Embedding, optional. Used to embed the pos_tags SequenceLabelField we get as input to the model.
  • dropout : float, optional, (default = 0.0) The variational dropout applied to the output of the encoder and MLP layers.
  • input_dropout : float, optional, (default = 0.0) The dropout applied to the embedded text input.
  • edge_prediction_threshold : int, optional (default = 0.5) The probability at which to consider a scored edge to be 'present' in the decoded graph. Must be between 0 and 1.
  • initializer : InitializerApplicator, optional (default=InitializerApplicator()) Used to initialize the model parameters.


    tokens: Dict[str, Dict[str, torch.Tensor]],
    pos_tags: torch.LongTensor = None,
    metadata: List[Dict[str, Any]] = None,
    arc_tags: torch.LongTensor = None,
) -> Dict[str, torch.Tensor]


  • tokens : TextFieldTensors, required The output of TextField.as_array().
  • pos_tags : torch.LongTensor, optional (default = None) The output of a SequenceLabelField containing POS tags.
  • metadata : List[Dict[str, Any]], optional (default = None)
  • A dictionary of metadata for each batch element which has keys:
  • tokens : List[str], required. The original string tokens in the sentence.
  • arc_tags : torch.LongTensor, optional (default = None) A torch tensor representing the sequence of integer indices denoting the parent of every word in the dependency parse. Has shape (batch_size, sequence_length, sequence_length).


An output dictionary.


GraphParser.get_metrics(self, reset:bool=False) -> Dict[str, float]

Returns a dictionary of metrics. This method will be called by in order to compute and use model metrics for early stopping and model serialization. We return an empty dictionary here rather than raising as it is not required to implement metrics for a new model. A boolean reset parameter is passed, as frequently a metric accumulator will have some state which should be reset between epochs. This is also compatible with Metrics. Metrics should be populated during the call to forward, with the Metric handling the accumulation of the metric until this method is called.


    output_dict: Dict[str, torch.Tensor],
) -> Dict[str, torch.Tensor]

Takes the result of forward and makes it human readable. Most of the time, the only thing this method does is convert tokens / predicted labels from tensors to strings that humans might actually understand. Somtimes you'll also do an argmax or something in here, too, but that most often happens in Model.forward, before you compute your metrics.

This method modifies the input dictionary, and also returns the same dictionary.

By default in the base class we do nothing.