allennlp.data.dataset_readers.babi

class allennlp.data.dataset_readers.babi.BabiReader(keep_sentences: bool = False, token_indexers: Dict[str, allennlp.data.token_indexers.token_indexer.TokenIndexer] = None, lazy: bool = False)

Bases: allennlp.data.dataset_readers.dataset_reader.DatasetReader

Reads one single task in the bAbI tasks format as formulated in Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks (https://arxiv.org/abs/1502.05698). Since this class handle a single file, if one wants to load multiple tasks together it has to merge them into a single file and use this reader.

Parameters

keep_sentences: ``bool``, optional, (default = ``False``)

Whether to keep each sentence in the context or to concatenate them. Default is False that corresponds to concatenation.

token_indexersDict[str, TokenIndexer], optional (default=``{“tokens”: SingleIdTokenIndexer()}``)

We use this to define the input representation for the text. See TokenIndexer.

lazybool, optional, (default = False)

Whether or not instances can be consumed lazily.

text_to_instance(self, context: List[List[str]], question: List[str], answer: str, supports: List[int]) → allennlp.data.instance.Instance

Does whatever tokenization or processing is necessary to go from textual input to an Instance. The primary intended use for this is with a Predictor, which gets text input as a JSON object and needs to process it to be input to a model.

The intent here is to share code between _read() and what happens at model serving time, or any other time you want to make a prediction from new data. We need to process the data in the same way it was done at training time. Allowing the DatasetReader to process new text lets us accomplish this, as we can just call DatasetReader.text_to_instance when serving predictions.

The input type here is rather vaguely specified, unfortunately. The Predictor will have to make some assumptions about the kind of DatasetReader that it’s using, in order to pass it the right information.