class = None, target_tokenizer: = None, source_token_indexers: Dict[str,] = None, target_token_indexers: Dict[str,] = None, source_add_start_token: bool = True, dummy_instances_for_vocab_generation: bool = False, lazy: bool = False)[source]


Reads instances from the Event2Mind dataset.

This dataset is CSV and has the columns: Source,Event,Xintent,Xemotion,Otheremotion,Xsent,Osent

Source is the provenance of the given instance. Event is free-form English text. The Xintent, Xemotion, and Otheremotion columns are JSON arrays containing the intention of “person x”, the reaction to the event by “person x” and the reaction to the event by others. The remaining columns are not used.

For instance: rocstory,PersonX talks to PersonX’s mother,”[“”to keep in touch””]”,”[“”accomplished””]”,”[“”loved””]”,5.0,5.0

Currently we only consume the event, intent and emotions, not the sentiments.

START_SYMBOL and END_SYMBOL tokens are added to the source and target sequences.

source_tokenizerTokenizer, optional

Tokenizer to use to split the input sequences into words or other kinds of tokens. Defaults to WordTokenizer().

target_tokenizerTokenizer, optional

Tokenizer to use to split the output sequences (during training) into words or other kinds of tokens. Defaults to source_tokenizer.

source_token_indexersDict[str, TokenIndexer], optional

Indexers used to define input (source side) token representations. Defaults to {"tokens": SingleIdTokenIndexer()}.

target_token_indexersDict[str, TokenIndexer], optional

Indexers used to define output (target side) token representations. Defaults to source_token_indexers.

source_add_start_tokenbool, (optional, default=True)

Whether or not to add START_SYMBOL to the beginning of the source sequence.

dummy_instances_for_vocab_generationbool (optional, default=False)

Whether to generate instances that use each token of input precisely once. Normally we instead generate all combinations of Source, Xintent, Xemotion and Otheremotion columns which distorts the underlying token counts. This flag should be used exclusively with the dry-run command as the instances generated will be nonsensical outside the context of vocabulary generation.

text_to_instance(self, source_string: str, xintent_string: str = None, xreact_string: str = None, oreact_string: str = None) →[source]

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.