# 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. """GLUCOSE: GeneraLized and COntextualized Story Explanations, is a novel conceptual framework and dataset for commonsense reasoning. Given a short story and a sentence X in the story, GLUCOSE captures ten dimensions of causal explanation related to X. These dimensions, inspired by human cognitive psychology, cover often-implicit causes and effects of X, including events, location, possession, and other attributes.""" import csv import os import datasets # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @inproceedings{mostafazadeh2020glucose, title={GLUCOSE: GeneraLized and COntextualized Story Explanations}, author={Nasrin Mostafazadeh and Aditya Kalyanpur and Lori Moon and David Buchanan and Lauren Berkowitz and Or Biran and Jennifer Chu-Carroll}, year={2020}, booktitle={The Conference on Empirical Methods in Natural Language Processing}, publisher={Association for Computational Linguistics} } """ # You can copy an official description _DESCRIPTION = """\ When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context. """ _HOMEPAGE = "https://github.com/ElementalCognition/glucose" _LICENSE = "Creative Commons Attribution-NonCommercial 4.0 International Public License" _URLs = { "glucose": { "test": "https://raw.githubusercontent.com/ElementalCognition/glucose/master/test/test_set_no_answers.csv", "train": "https://github.com/TevenLeScao/glucose/blob/master/GLUCOSE_training_data.zip?raw=true", } } class Glucose(datasets.GeneratorBasedBuilder): """GLUCOSE: GeneraLized and COntextualized Story Explanations, is a novel conceptual framework and dataset for commonsense reasoning.""" VERSION = datasets.Version("1.1.0") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="glucose", description="Main dataset"), ] def _info(self): feature_dict = { "experiment_id": datasets.Value("string"), "story_id": datasets.Value("string"), # The train set contains only one ID in numeric form "worker_id": datasets.Value("int64"), # The test set contains several IDs in string form "worker_ids": datasets.Value("string"), "submission_time_normalized": datasets.Value("string"), "worker_quality_assessment": datasets.Value("int64"), "selected_sentence_index": datasets.Value("int64"), "story": datasets.Value("string"), "selected_sentence": datasets.Value("string"), "number_filled_in": datasets.Value("int64"), } for i in range(1, 11): feature_dict[f"{i}_specificNL"] = datasets.Value("string") feature_dict[f"{i}_specificStructured"] = datasets.Value("string") feature_dict[f"{i}_generalNL"] = datasets.Value("string") feature_dict[f"{i}_generalStructured"] = datasets.Value("string") features = datasets.Features(feature_dict) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" train_url = _URLs[self.config.name]["train"] test_url = _URLs[self.config.name]["test"] train_data = dl_manager.download_and_extract(train_url) test_data = dl_manager.download_and_extract(test_url) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(train_data, "GLUCOSE_training_data_final.csv"), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": test_data, "split": "test"}, ), ] def _generate_examples(self, filepath, split): with open(filepath, encoding="utf8") as f: data = csv.reader(f) next(data) for id_, row in enumerate(data): if split == "train": yield id_, train_dict_from_row(row) else: yield id_, test_dict_from_row(row) def train_dict_from_row(row): return_dict = { "experiment_id": row[0], "story_id": row[1], "worker_id": row[2], "worker_ids": "", "submission_time_normalized": row[3], "worker_quality_assessment": row[4], "selected_sentence_index": row[5], "story": row[6], "selected_sentence": row[7], "number_filled_in": row[48], } for i in range(1, 11): return_dict[f"{i}_specificNL"] = row[4 * i + 4] return_dict[f"{i}_specificStructured"] = row[4 * i + 5] return_dict[f"{i}_generalNL"] = row[4 * i + 6] return_dict[f"{i}_generalStructured"] = row[4 * i + 7] return return_dict def test_dict_from_row(row): return_dict = { "experiment_id": "", "story_id": row[0], "worker_id": -1, "worker_ids": row[3], "submission_time_normalized": "", "worker_quality_assessment": -1, "selected_sentence_index": -1, "story": row[1], "selected_sentence": row[2], "number_filled_in": -1, } for i in range(1, 11): return_dict[f"{i}_specificNL"] = row[2 * i + 2] return_dict[f"{i}_generalNL"] = row[2 * i + 3] return_dict[f"{i}_specificStructured"] = "" return_dict[f"{i}_generalStructured"] = "" return return_dict