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