# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity""" import json import os import datasets _CITATION = """\ @inproceedings{rodriguez2020curiosity, title = {Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity}, author = {Pedro Rodriguez and Paul Crook and Seungwhan Moon and Zhiguang Wang}, year = 2020, booktitle = {Empirical Methods in Natural Language Processing} } """ _DESCRIPTION = """\ This dataset contains 14K dialogs (181K utterances) where users and assistants converse about geographic topics like geopolitical entities and locations. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages. """ _HOMEPAGE = "https://www.pedro.ai/curiosity" _LICENSE = "https://github.com/facebookresearch/curiosity/blob/master/LICENSE" _URL = "https://obj.umiacs.umd.edu/curiosity/" _URLs = { "train": _URL + "curiosity_dialogs.train.json", "val": _URL + "curiosity_dialogs.val.json", "test": _URL + "curiosity_dialogs.test.json", "test_zero": _URL + "curiosity_dialogs.test_zero.json", } class CuriosityDialogsConfig(datasets.BuilderConfig): """BuilderConfig for Curiosity Dialogs dataset""" def __init__(self, **kwargs): """BuilderConfig for Curiosity Dialogs dataset. Args: **kwargs: keyword arguments forwarded to super. """ super(CuriosityDialogsConfig, self).__init__(**kwargs) class CuriosityDialogs(datasets.GeneratorBasedBuilder): """Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity""" VERSION = datasets.Version("1.1.0") BUILDER_CONFIGS = [ CuriosityDialogsConfig( name="curiosity_dialogs", version=datasets.Version("1.1.0"), description="Curiosity Dialog: A Dataset for Conversational Curiosity", ), ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "messages": datasets.Sequence( { "message": datasets.Value("string"), "liked": datasets.ClassLabel(names=["False", "True"]), "sender": datasets.ClassLabel(names=["user", "assistant"]), "facts": datasets.Sequence( { "fid": datasets.Value("int32"), "used": datasets.ClassLabel(names=["False", "True"]), "source": datasets.ClassLabel(names=["section", "known", "random"]), } ), "message_id": datasets.Value("string"), "dialog_acts": datasets.Sequence(datasets.Value("string")), } ), "known_entities": datasets.Sequence(datasets.Value("string")), "focus_entity": datasets.Value("string"), "dialog_id": datasets.Value("int32"), "inferred_steps": datasets.ClassLabel(names=["False", "True"]), "created_time": datasets.Value("int64"), "aspects": datasets.Sequence(datasets.Value("string")), "first_aspect": datasets.Value("string"), "second_aspect": datasets.Value("string"), "shuffle_facts": datasets.ClassLabel(names=["False", "True"]), "related_entities": datasets.Sequence(datasets.Value("string")), "tag": datasets.Value("string"), "user_id": datasets.Value("int32"), "assistant_id": datasets.Value("int32"), "is_annotated": datasets.ClassLabel(names=["False", "True"]), "user_dialog_rating": datasets.Value("int32"), "user_other_agent_rating": datasets.Value("int32"), "assistant_dialog_rating": datasets.Value("int32"), "assistant_other_agent_rating": datasets.Value("int32"), "reported": datasets.ClassLabel(names=["False", "True"]), "annotated": datasets.ClassLabel(names=["False", "True"]), } ), supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" data_dir = dl_manager.download_and_extract(_URLs) return [ datasets.SplitGenerator( name="train", gen_kwargs={ "filepath": os.path.join(data_dir["train"]), "split": "train", }, ), datasets.SplitGenerator( name="val", gen_kwargs={"filepath": os.path.join(data_dir["val"]), "split": "val"}, ), datasets.SplitGenerator( name="test", gen_kwargs={ "filepath": os.path.join(data_dir["test"]), "split": "test_zero", }, ), datasets.SplitGenerator( name="test_zero", gen_kwargs={ "filepath": os.path.join(data_dir["test_zero"]), "split": "test_zero", }, ), ] def _generate_examples(self, filepath, split): """Yields examples.""" # Bool entries are converted to string entries because of PyArrow error with open(filepath, encoding="utf-8") as f: dataset = json.load(f) dialogs = dataset["dialogs"] for id_, data in enumerate(dialogs): messages = data["messages"] for message in messages: message["liked"] = str(message["liked"]) facts = message["facts"] for fact in facts: fact["used"] = str(fact["used"]) known_entities = data["known_entities"] focus_entity = data["focus_entity"] dialog_id = data["dialog_id"] inferred_steps = str(data["inferred_steps"]) created_time = data["created_time"] aspects = data["aspects"] first_aspect = data["first_aspect"] second_aspect = data["second_aspect"] shuffle_facts = str(data["shuffle_facts"]) related_entities = data["related_entities"] tag = data["tag"] user_id = data["user_id"] assistant_id = data["assistant_id"] is_annotated = str(data["is_annotated"]) user_dialog_rating = data["user_dialog_rating"] user_other_agent_rating = data["user_other_agent_rating"] assistant_dialog_rating = data["assistant_dialog_rating"] assistant_other_agent_rating = data["assistant_other_agent_rating"] reported = str(data["reported"]) annotated = str(data["annotated"]) yield id_, { "messages": messages, "known_entities": known_entities, "focus_entity": focus_entity, "dialog_id": dialog_id, "inferred_steps": inferred_steps, "created_time": created_time, "aspects": aspects, "first_aspect": first_aspect, "second_aspect": second_aspect, "shuffle_facts": shuffle_facts, "related_entities": related_entities, "tag": tag, "user_id": user_id, "assistant_id": assistant_id, "is_annotated": is_annotated, "user_dialog_rating": user_dialog_rating, "user_other_agent_rating": user_other_agent_rating, "assistant_dialog_rating": assistant_dialog_rating, "assistant_other_agent_rating": assistant_other_agent_rating, "reported": reported, "annotated": annotated, }