import json import os import random import datasets random.seed(42) # This is important, to ensure the same order for concept sets as the official script. _CITATION = """\ @inproceedings{lin-etal-2020-commongen, title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning", author = "Lin, Bill Yuchen and Zhou, Wangchunshu and Shen, Ming and Zhou, Pei and Bhagavatula, Chandra and Choi, Yejin and Ren, Xiang", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.165", doi = "10.18653/v1/2020.findings-emnlp.165", pages = "1823--1840" } """ _DESCRIPTION = """\ CommonGen is a constrained text generation task, associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts; the task is to generate a coherent sentence describing an everyday scenario using these concepts. CommonGen is challenging because it inherently requires 1) relational reasoning using background commonsense knowledge, and 2) compositional generalization ability to work on unseen concept combinations. Our dataset, constructed through a combination of crowd-sourcing from AMT and existing caption corpora, consists of 30k concept-sets and 50k sentences in total. """ _URL = "https://storage.googleapis.com/huggingface-nlp/datasets/common_gen/commongen_data.zip" class CommonGen(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("2020.5.30") def _info(self): features = datasets.Features( { "concept_set_idx": datasets.Value("int32"), "concepts": datasets.Sequence(datasets.Value("string")), "target": datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=datasets.info.SupervisedKeysData(input="concepts", output="target"), homepage="https://inklab.usc.edu/CommonGen/index.html", citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" dl_dir = dl_manager.download_and_extract(_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dl_dir, "commongen.train.jsonl"), "split": "train"}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(dl_dir, "commongen.dev.jsonl"), "split": "dev"}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dl_dir, "commongen.test_noref.jsonl"), "split": "test"}, ), ] def _generate_examples(self, filepath, split): """Yields examples.""" with open(filepath, encoding="utf-8") as f: id_ = 0 for idx, row in enumerate(f): row = row.replace(", }", "}") # Fix possible JSON format error data = json.loads(row) rand_order = [word for word in data["concept_set"].split("#")] random.shuffle(rand_order) if split == "test": yield idx, { "concept_set_idx": idx, "concepts": rand_order, "target": "", } else: for scene in data["scene"]: yield id_, { "concept_set_idx": idx, "concepts": rand_order, "target": scene, } id_ += 1