QuoraParaphraseDatasetReader(lazy: bool = False, tokenizer: allennlp.data.tokenizers.tokenizer.Tokenizer = None, token_indexers: Dict[str, allennlp.data.token_indexers.token_indexer.TokenIndexer] = None)¶
Reads a file from the Quora Paraphrase dataset. The train/validation/test split of the data comes from the paper Bilateral Multi-Perspective Matching for Natural Language Sentences by Zhiguo Wang et al., 2017. Each file of the data is a tsv file without header. The columns are is_duplicate, question1, question2, and id. All questions are pre-tokenized and tokens are space separated. We convert these keys into fields named “label”, “premise” and “hypothesis”, so that it is compatible to some existing natural language inference algorithms.
DatasetReader. If this is
True, training will start sooner, but will take longer per batch. This also allows training with datasets that are too large to fit in memory.
Tokenizer to use to split the premise and hypothesis into words or other kinds of tokens. Defaults to
Dict[str, TokenIndexer], optional
Indexers used to define input token representations. Defaults to
text_to_instance(self, premise:str, hypothesis:str, label:str=None) → 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
DatasetReaderto process new text lets us accomplish this, as we can just call
DatasetReader.text_to_instancewhen serving predictions.
The input type here is rather vaguely specified, unfortunately. The
Predictorwill have to make some assumptions about the kind of
DatasetReaderthat it’s using, in order to pass it the right information.