Datasets:

GEM
/

e2e_nlg

	import csv
	import json
	import os
	import datasets

	_CITATION = """\
	@inproceedings{e2e_cleaned,
	address = {Tokyo, Japan},
	title = {Semantic {Noise} {Matters} for {Neural} {Natural} {Language} {Generation}},
	url = {https://www.aclweb.org/anthology/W19-8652/},
	booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)},
	author = {Dušek, Ondřej and Howcroft, David M and Rieser, Verena},
	year = {2019},
	pages = {421--426},
	}
	"""

	_DESCRIPTION = """\
	The E2E dataset is designed for a limited-domain data-to-text task --
	generation of restaurant descriptions/recommendations based on up to 8 different
	attributes (name, area, price range etc.).
	"""

	_URLs = {
	"train": "https://github.com/tuetschek/e2e-cleaning/raw/master/cleaned-data/train-fixed.no-ol.csv",
	"validation": "https://raw.githubusercontent.com/jordiclive/GEM_datasets/main/e2e/validation.json",
	"test": "https://raw.githubusercontent.com/jordiclive/GEM_datasets/main/e2e/test.json",
	"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/e2e_nlg.zip",
	}


	class E2ENlg(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.1")
	DEFAULT_CONFIG_NAME = "e2e_nlg"

	def _info(self):
	features = datasets.Features(
	{
	"gem_id": datasets.Value("string"),
	"gem_parent_id": datasets.Value("string"),
	"meaning_representation": datasets.Value("string"),
	"target": datasets.Value("string"),
	"references": [datasets.Value("string")],
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=datasets.info.SupervisedKeysData(
	input="meaning_representation", output="target"
	),
	homepage="http://www.macs.hw.ac.uk/InteractionLab/E2E/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_dir = dl_manager.download_and_extract(_URLs)
	challenge_sets = [
	("challenge_train_sample", "train_e2e_nlg_RandomSample500.json"),
	("challenge_validation_sample", "validation_e2e_nlg_RandomSample500.json"),
	("challenge_test_scramble", "test_e2e_nlg_ScrambleInputStructure500.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"], "e2e_nlg", 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"e2e_nlg-{split}-{id_}"
	yield id_, exple
	if split.startswith("test") or split.startswith("validation"):
	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
	yield id_, {
	"gem_id": f"e2e_nlg-{split}-{id_}",
	"gem_parent_id": f"e2e_nlg-{split}-{id_}",
	"meaning_representation": exple["meaning_representation"],
	"target": exple["references"][0],
	"references": exple["references"],
	}
	else:
	with open(filepath, encoding="utf-8") as f:
	reader = csv.DictReader(f)
	for id_, example in enumerate(reader):
	yield id_, {
	"gem_id": f"e2e_nlg-{split}-{id_}",
	"gem_parent_id": f"e2e_nlg-{split}-{id_}",
	"meaning_representation": example["mr"],
	"target": example["ref"],
	"references": []
	}