SpacyTokenIndexer(self, hidden_dim:int=96, token_min_padding_length:int=0) -> None

This :class:SpacyTokenIndexer represents tokens as word vectors from a spacy model. You might want to do this for two main reasons; easier integration with a spacy pipeline and no out of vocabulary tokens.


  • hidden_dim : int, optional (default=96) The dimension of the vectors that spacy generates for representing words.
  • token_min_padding_length : int, optional (default=0)
  • See :class:TokenIndexer.


SpacyTokenIndexer.count_vocab_items(self,, counter:Dict[str, Dict[str, int]])

The :class:Vocabulary needs to assign indices to whatever strings we see in the training data (possibly doing some frequency filtering and using an OOV, or out of vocabulary, token). This method takes a token and a dictionary of counts and increments counts for whatever vocabulary items are present in the token. If this is a single token ID representation, the vocabulary item is likely the token itself. If this is a token characters representation, the vocabulary items are all of the characters in the token.


SpacyTokenIndexer.tokens_to_indices(self, tokens:List[spacy.tokens.token.Token], -> Dict[str, List[numpy.ndarray]]

Takes a list of tokens and converts them to an IndexedTokenList. This could be just an ID for each token from the vocabulary. Or it could split each token into characters and return one ID per character. Or (for instance, in the case of byte-pair encoding) there might not be a clean mapping from individual tokens to indices, and the IndexedTokenList could be a complex data structure.


SpacyTokenIndexer.as_padded_tensor_dict(self, tokens:Dict[str, List[Any]], padding_lengths:Dict[str, int]) -> Dict[str, torch.Tensor]

This method pads a list of tokens given the input padding lengths (which could actually truncate things, depending on settings) and returns that padded list of input tokens as a Dict[str, torch.Tensor]. This is a dictionary because there should be one key per argument that the TokenEmbedder corresponding to this class expects in its forward() method (where the argument name in the TokenEmbedder needs to make the key in this dictionary).

The base class implements the case when all you want to do is create a padded LongTensor for every list in the tokens dictionary. If your TokenIndexer needs more complex logic than that, you need to override this method.