Datasets:

allenai
/

common_gen

	from __future__ import absolute_import, division, print_function

	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