Datasets:

GEM
/

common_gen

	import json
	import os
	import datasets

	_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",
	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.
	"""

	_URLs = {
	"data": "https://storage.googleapis.com/huggingface-nlp/datasets/common_gen/commongen_data.zip",
	"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/common_gen.zip",
	}


	class CommonGen(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")
	DEFAULT_CONFIG_NAME = "common_gen"

	def _info(self):
	features = datasets.Features(
	{
	"gem_id": datasets.Value("string"),
	"gem_parent_id": datasets.Value("string"),
	"concept_set_id": datasets.Value("int32"),
	"concepts": [datasets.Value("string")],
	"target": datasets.Value("string"), # single target for train
	"references": [
	datasets.Value("string")
	], # multiple references for validation
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=datasets.info.SupervisedKeysData(
	input="concepts", output="target"
	),
	homepage="https://inklab.usc.edu/CommonGen/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_dir = dl_manager.download_and_extract(_URLs)
	challenge_sets = [
	("challenge_train_sample", "train_common_gen_RandomSample500.json"),
	(
	"challenge_validation_sample",
	"validation_common_gen_RandomSample500.json",
	),
	(
	"challenge_test_scramble",
	"test_common_gen_ScrambleInputStructure500.json",
	),
	]
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": os.path.join(dl_dir["data"], "commongen.train.jsonl"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(dl_dir["data"], "commongen.dev.jsonl"),
	"split": "validation",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(
	dl_dir["data"], "commongen.test_noref.jsonl"
	),
	"split": "test",
	},
	),
	] + [
	datasets.SplitGenerator(
	name=challenge_split,
	gen_kwargs={
	"filepath": os.path.join(
	dl_dir["challenge_set"], "common_gen", filename
	),
	"split": challenge_split,
	},
	)
	for challenge_split, filename in challenge_sets
	]

	def _generate_examples(self, filepath, split, filepaths=None, lang=None):
	"""Yields examples."""
	if split.startswith("challenge"):
	exples = json.load(open(filepath, encoding="utf-8"))
	if isinstance(exples, dict):
	assert len(exples) == 1, "multiple entries found"
	exples = list(exples.values())[0]
	for id_, exple in enumerate(exples):
	if len(exple) == 0:
	continue
	exple["gem_parent_id"] = exple["gem_id"]
	exple["gem_id"] = f"common_gen-{split}-{id_}"
	yield id_, exple
	else:
	with open(filepath, encoding="utf-8") as f:
	id_ = -1
	i = -1
	for row in f:
	row = row.replace(", }", "}") # Fix possible JSON format error
	data = json.loads(row)
	concepts = [word for word in data["concept_set"].split("#")]
	if split == "train":
	i += 1
	for scene in data["scene"]:
	id_ += 1
	yield id_, {
	"gem_id": f"common_gen-{split}-{id_}",
	"gem_parent_id": f"common_gen-{split}-{id_}",
	"concept_set_id": i,
	"concepts": concepts,
	"target": scene,
	"references": [],
	}
	else:
	id_ += 1
	yield id_, {
	"gem_id": f"common_gen-{split}-{id_}",
	"gem_parent_id": f"common_gen-{split}-{id_}",
	"concept_set_id": id_,
	"concepts": concepts,
	"target": "" if split == "test" else data["scene"][0],
	"references": [] if split == "test" else data["scene"],
	}