Datasets:

GEM
/

web_nlg

	import json
	import os
	import datasets

	_CITATION = """\
	@inproceedings{castro-ferreira20:bilin-bi-direc-webnl-shared,
	title={The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task Overview and Evaluation Results (WebNLG+ 2020)},
	author={Castro Ferreira, Thiago and
	Gardent, Claire and
	Ilinykh, Nikolai and
	van der Lee, Chris and
	Mille, Simon and
	Moussallem, Diego and
	Shimorina, Anastasia},
	booktitle = {Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+ 2020)},
	pages = "55--76",
	year = 2020,
	address = {Dublin, Ireland (Virtual)},
	publisher = {Association for Computational Linguistics}}
	"""

	_DESCRIPTION = """\
	WebNLG is a bi-lingual dataset (English, Russian) of parallel DBpedia triple sets
	and short texts that cover about 450 different DBpedia properties. The WebNLG data
	was originally created to promote the development of RDF verbalisers able to
	generate short text and to handle micro-planning (i.e., sentence segmentation and
	ordering, referring expression generation, aggregation); the goal of the task is
	to generate texts starting from 1 to 7 input triples which have entities in common
	(so the input is actually a connected Knowledge Graph). The dataset contains about
	17,000 triple sets and 45,000 crowdsourced texts in English, and 7,000 triples sets
	and 19,000 crowdsourced texts in Russian. A challenging test set section with
	entities and/or properties that have not been seen at training time is available.
	"""

	_LANG = ["en", "ru"]
	_URLs = {
	"en": {
	"train": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_train.json",
	"validation": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_val.json",
	"test": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_en_test.json",
	"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/web_nlg_en.zip",
	},
	"ru": {
	"train": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_train.json",
	"validation": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_val.json",
	"test": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_web_nlg/webnlg_ru_test.json",
	"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/web_nlg_ru.zip",
	},
	}


	class WebNLG(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name=lang,
	version=datasets.Version("1.0.0"),
	description="",
	)
	for lang in _LANG
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"gem_id": datasets.Value("string"),
	"gem_parent_id": datasets.Value("string"),
	"input": [datasets.Value("string")],
	"target": datasets.Value("string"), # single target for train
	"references": [datasets.Value("string")],
	"category": datasets.Value("string"),
	"webnlg_id": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage="https://webnlg-challenge.loria.fr/challenge_2020/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_dir = dl_manager.download_and_extract(_URLs[self.config.name])
	lang = str(self.config.name)
	challenge_sets = [
	("challenge_train_sample", f"train_web_nlg_{lang}_RandomSample500.json"),
	(
	"challenge_validation_sample",
	f"validation_web_nlg_{lang}_RandomSample500.json",
	),
	(
	"challenge_test_scramble",
	f"test_web_nlg_{lang}_ScrambleInputStructure500.json",
	),
	]
	if lang == "en":
	challenge_sets += [
	(
	"challenge_test_numbers",
	f"test_web_nlg_{lang}_replace_numbers_500.json",
	)
	]
	return [
	datasets.SplitGenerator(
	name=spl, gen_kwargs={"filepath": dl_dir[spl], "split": spl}
	)
	for spl in ["train", "validation", "test"]
	] + [
	datasets.SplitGenerator(
	name=challenge_split,
	gen_kwargs={
	"filepath": os.path.join(
	dl_dir["challenge_set"], f"web_nlg_{self.config.name}", filename
	),
	"split": challenge_split,
	},
	)
	for challenge_split, filename in challenge_sets
	]

	def _generate_examples(self, filepath, split, filepaths=None, lang=None):
	"""Yields examples."""
	if "challenge" in split:
	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"web_nlg_{self.config.name}-{split}-{id_}"
	yield id_, exple
	else:
	with open(filepath, encoding="utf-8") as f:
	examples = json.load(f)
	id_ = -1
	for example in examples["values"]:
	if split == "train":
	for target in example["target"]:
	id_ += 1
	yield id_, {
	"gem_id": f"web_nlg_{self.config.name}-{split}-{id_}",
	"gem_parent_id": f"web_nlg_{self.config.name}-{split}-{id_}",
	"input": example["input"],
	"target": target,
	"references": []
	if split == "train"
	else example["target"],
	"category": example["category"],
	"webnlg_id": example["webnlg-id"],
	}
	else:
	id_ += 1
	yield id_, {
	"gem_id": f"web_nlg_{self.config.name}-{split}-{id_}",
	"gem_parent_id": f"web_nlg_{self.config.name}-{split}-{id_}",
	"input": example["input"],
	"target": example["target"][0]
	if len(example["target"]) > 0
	else "",
	"references": example["target"],
	"category": example["category"],
	"webnlg_id": example["webnlg-id"],
	}