Datasets:

GEM
/

cs_restaurants

	import csv
	import json
	import os
	import datasets

	_CITATION = """\
	@inproceedings{cs_restaurants,
	address = {Tokyo, Japan},
	title = {Neural {Generation} for {Czech}: {Data} and {Baselines}},
	shorttitle = {Neural {Generation} for {Czech}},
	url = {https://www.aclweb.org/anthology/W19-8670/},
	urldate = {2019-10-18},
	booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)},
	author = {Dušek, Ondřej and Jurčíček, Filip},
	month = oct,
	year = {2019},
	pages = {563--574},
	}
	"""

	_DESCRIPTION = """\
	The task is generating responses in the context of a (hypothetical) dialogue
	system that provides information about restaurants. The input is a basic
	intent/dialogue act type and a list of slots (attributes) and their values.
	The output is a natural language sentence.
	"""

	_URLs = {
	"train": "https://raw.githubusercontent.com/UFAL-DSG/cs_restaurant_dataset/master/train.json",
	"validation": "https://raw.githubusercontent.com/UFAL-DSG/cs_restaurant_dataset/master/devel.json",
	"test": "https://raw.githubusercontent.com/UFAL-DSG/cs_restaurant_dataset/master/test.json",
	"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/cs_restaurants.zip",
	}


	class CSRestaurants(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")
	DEFAULT_CONFIG_NAME = "cs_restaurants"

	def _info(self):
	features = datasets.Features(
	{
	"gem_id": datasets.Value("string"),
	"gem_parent_id": datasets.Value("string"),
	"dialog_act": datasets.Value("string"),
	"dialog_act_delexicalized": datasets.Value("string"),
	"target_delexicalized": datasets.Value("string"),
	"target": datasets.Value("string"),
	"references": [datasets.Value("string")],
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=datasets.info.SupervisedKeysData(
	input="dialog_act", output="target"
	),
	homepage="https://github.com/UFAL-DSG/cs_restaurant_dataset",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_dir = dl_manager.download_and_extract(_URLs)
	challenge_sets = [
	("challenge_train_sample", "train_cs_restaurants_RandomSample500.json"),
	(
	"challenge_validation_sample",
	"validation_cs_restaurants_RandomSample500.json",
	),
	(
	"challenge_test_scramble",
	"test_cs_restaurants_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"], "cs_restaurants", 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"cs_restaurants-{split}-{id_}"
	yield id_, exple
	else:
	with open(filepath, encoding="utf8") as f:
	data = json.load(f)
	for id_, instance in enumerate(data):
	yield id_, {
	"gem_id": f"cs_restaurants-{split}-{id_}",
	"gem_parent_id": f"cs_restaurants-{split}-{id_}",
	"dialog_act": instance["da"],
	"dialog_act_delexicalized": instance["delex_da"],
	"target": instance["text"],
	"target_delexicalized": instance["delex_text"],
	"references": [] if split == "train" else [instance["text"]],
	}