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import json |
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import datasets |
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from datasets.features import Sequence |
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_URLS = { |
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"full": { |
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"train": "./personachat_truecased_full_train.json", |
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"valid": "./personachat_truecased_full_valid.json" |
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}, |
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"sample": { |
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"train": "./personachat_truecased_sample_train.json", |
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"valid": "./personachat_truecased_sample_valid.json" |
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} |
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} |
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_DESCRIPTION = """\ |
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A version of the PersonaChat dataset that has been true-cased, and also has been given more normalized punctuation. |
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The original PersonaChat dataset is in all lower case, and has extra space around each clause/sentence separating |
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punctuation mark. This version of the dataset has more of a natural language look, with sentence capitalization, |
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proper noun capitalization, and normalized whitespace. Also, each dialogue turn includes a pool of distractor |
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candidate responses, which can be used by a multiple choice regularization loss during training. |
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""" |
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_CITATION = """\ |
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@article{zhang2018personalizing, |
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title={Personalizing dialogue agents: I have a dog, do you have pets too?}, |
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author={Zhang, Saizheng and Dinan, Emily and Urbanek, Jack and Szlam, Arthur and Kiela, Douwe and Weston, Jason}, |
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journal={arXiv preprint arXiv:1801.07243}, |
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year={2018} |
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} |
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""" |
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_LICENSE = "Like the original PersonaChat dataset, this dataset is released under the CC BY 4.0 license." |
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class PersonachatTruecased(datasets.GeneratorBasedBuilder): |
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""" |
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Version of the PersonaChat dataset that includes true-casing, normalized punctuation, and candidate distractor |
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responses for each dialogue turn, for including a multiple choice regularzation loss while training. |
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""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="full", version=VERSION, description="The full dataset."), |
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datasets.BuilderConfig( |
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name="sample", version=VERSION, description="A sample sample of the dataset, useful for testing." |
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) |
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] |
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DEFAULT_CONFIG_NAME = "full" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features({ |
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"personality": Sequence(datasets.Value("string")), |
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"candidates": Sequence(datasets.Value("string")), |
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"history": Sequence(datasets.Value("string")), |
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"conv_id": datasets.Value("int32"), |
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"utterance_idx": datasets.Value("int32") |
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}), |
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citation=_CITATION, |
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license=_LICENSE |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager): |
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split_paths = dl_manager.download(_URLS[self.config.name]) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"data_path": split_paths["train"]} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"data_path": split_paths["valid"]} |
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) |
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] |
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def _generate_examples(self, data_path: str): |
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with open(data_path) as f: |
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data = json.load(f) |
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for conv_id, conv in enumerate(data): |
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personality = conv["personality"] |
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for utterance_idx, utterance in enumerate(conv["utterances"]): |
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id_ = f"{conv_id}-{utterance_idx}" |
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yield id_, { |
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"personality": personality, |
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"candidates": utterance["candidates"], |
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"history": utterance["history"], |
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"conv_id": conv_id, |
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"utterance_idx": utterance_idx |
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} |
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