allennlp.models.simple_tagger

class allennlp.models.simple_tagger.SimpleTagger(vocab: allennlp.data.vocabulary.Vocabulary, text_field_embedder: allennlp.modules.text_field_embedders.text_field_embedder.TextFieldEmbedder, encoder: allennlp.modules.seq2seq_encoders.seq2seq_encoder.Seq2SeqEncoder, initializer: allennlp.nn.initializers.InitializerApplicator = <allennlp.nn.initializers.InitializerApplicator object>, regularizer: typing.Union[allennlp.nn.regularizers.regularizer_applicator.RegularizerApplicator, NoneType] = None) → None

Bases: allennlp.models.model.Model

This SimpleTagger simply encodes a sequence of text with a stacked Seq2SeqEncoder, then predicts a tag for each token in the sequence.

Parameters:

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 in between embedding tokens and predicting output tags.

initializer : InitializerApplicator, optional (default=``InitializerApplicator()``)

Used to initialize the model parameters.

regularizer : RegularizerApplicator, optional (default=``None``)

If provided, will be used to calculate the regularization penalty during training.

decode(output_dict: typing.Dict[str, torch.FloatTensor]) → typing.Dict[str, torch.FloatTensor]

Does a simple position-wise argmax over each token, converts indices to string labels, and adds a "tags" key to the dictionary with the result.

forward(tokens: typing.Dict[str, torch.LongTensor], tags: torch.LongTensor = None) → typing.Dict[str, torch.FloatTensor]

Parameters:

tokens : Dict[str, torch.LongTensor], required

The output of TextField.as_array(), which should typically be passed directly to a TextFieldEmbedder. This output is a dictionary mapping keys to TokenIndexer tensors. At its most basic, using a SingleIdTokenIndexer this is: {"tokens": Tensor(batch_size, num_tokens)}. This dictionary will have the same keys as were used for the TokenIndexers when you created the TextField representing your sequence. The dictionary is designed to be passed directly to a TextFieldEmbedder, which knows how to combine different word representations into a single vector per token in your input.

tags : torch.LongTensor, optional (default = None)

A torch tensor representing the sequence of integer gold class labels of shape (batch_size, num_tokens).

Returns:

An output dictionary consisting of:

logits : torch.FloatTensor

A tensor of shape (batch_size, num_tokens, tag_vocab_size) representing unnormalised log probabilities of the tag classes.

class_probabilities : torch.FloatTensor

A tensor of shape (batch_size, num_tokens, tag_vocab_size) representing a distribution of the tag classes per word.

loss : torch.FloatTensor, optional

A scalar loss to be optimised.

classmethod from_params(vocab: allennlp.data.vocabulary.Vocabulary, params: allennlp.common.params.Params) → allennlp.models.simple_tagger.SimpleTagger

get_metrics(reset: bool = False) → typing.Dict[str, float]

Returns a dictionary of metrics. This method will be called by allennlp.training.Trainer in order to compute and use model metrics for early stopping and model serialisation. 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 should be populated during the call to ``forward`, with the Metric handling the accumulation of the metric until this method is called.