allennlp.data.dataset_readers.snli

class allennlp.data.dataset_readers.snli.SnliReader(tokenizer: allennlp.data.tokenizers.tokenizer.Tokenizer = None, token_indexers: Dict[str, allennlp.data.token_indexers.token_indexer.TokenIndexer] = None, lazy: bool = False)

Bases: allennlp.data.dataset_readers.dataset_reader.DatasetReader

Reads a file from the Stanford Natural Language Inference (SNLI) dataset. This data is formatted as jsonl, one json-formatted instance per line. The keys in the data are “gold_label”, “sentence1”, and “sentence2”. We convert these keys into fields named “label”, “premise” and “hypothesis”, along with a metadata field containing the tokenized strings of the premise and hypothesis.

Parameters

tokenizerTokenizer, optional (default=``WordTokenizer()``)

We use this Tokenizer for both the premise and the hypothesis. See Tokenizer.

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

We similarly use this for both the premise and the hypothesis. See TokenIndexer.

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 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.