BasicClassifier(self,, text_field_embedder:allennlp.modules.text_field_embedders.text_field_embedder.TextFieldEmbedder, seq2vec_encoder:allennlp.modules.seq2vec_encoders.seq2vec_encoder.Seq2VecEncoder, seq2seq_encoder:allennlp.modules.seq2seq_encoders.seq2seq_encoder.Seq2SeqEncoder=None, feedforward:Union[allennlp.modules.feedforward.FeedForward, NoneType]=None, dropout:float=None, num_labels:int=None, label_namespace:str='labels', initializer:allennlp.nn.initializers.InitializerApplicator=<allennlp.nn.initializers.InitializerApplicator object at 0x7f546cb03c50>, regularizer:Union[allennlp.nn.regularizers.regularizer_applicator.RegularizerApplicator, NoneType]=None) -> None

This Model implements a basic text classifier. After embedding the text into a text field, we will optionally encode the embeddings with a Seq2SeqEncoder. The resulting sequence is pooled using a Seq2VecEncoder and then passed to a linear classification layer, which projects into the label space. If a Seq2SeqEncoder is not provided, we will pass the embedded text directly to the Seq2VecEncoder.


  • vocab : Vocabulary
  • text_field_embedder : TextFieldEmbedder Used to embed the input text into a TextField
  • seq2seq_encoder : Seq2SeqEncoder, optional (default=None) Optional Seq2Seq encoder layer for the input text.
  • seq2vec_encoder : Seq2VecEncoder Required Seq2Vec encoder layer. If seq2seq_encoder is provided, this encoder will pool its output. Otherwise, this encoder will operate directly on the output of the text_field_embedder.
  • feedforward : FeedForward, optional, (default = None). An optional feedforward layer to apply after the seq2vec_encoder.
  • dropout : float, optional (default = None) Dropout percentage to use.
  • num_labels : int, optional (default = None) Number of labels to project to in classification layer. By default, the classification layer will project to the size of the vocabulary namespace corresponding to labels.
  • label_namespace : str, optional (default = "labels") Vocabulary namespace corresponding to labels. By default, we use the "labels" namespace.
  • initializer : InitializerApplicator, optional (default=InitializerApplicator()) If provided, will be used to initialize the model parameters.
  • regularizer : RegularizerApplicator, optional (default=None) If provided, will be used to calculate the regularization penalty during training.


BasicClassifier.forward(self, tokens:Dict[str, Dict[str, torch.Tensor]], label:torch.IntTensor=None) -> Dict[str, torch.Tensor]


  • tokens : TextFieldTensors From a TextField
  • label : torch.IntTensor, optional (default = None) From a LabelField


An output dictionary consisting of:

logits: torch.FloatTensor A tensor of shape (batch_size, num_labels) representing unnormalized log probabilities of the label. probs: torch.FloatTensor A tensor of shape (batch_size, num_labels) representing probabilities of the label. loss: torch.FloatTensor, optional A scalar loss to be optimised.


BasicClassifier.decode(self, output_dict:Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]

Does a simple argmax over the probabilities, converts index to string label, and add "label" key to the dictionary with the result.