Datasets:

McGill-NLP
/

FaithDial

	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor (Nouha Dziri).
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""FaithDial: A Faithful Benchmark for Information-Seeking Dialogue"""


	import json

	import datasets


	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@article{dziri2022faithdial,
	title={FaithDial: A Faithful Benchmark for Information-Seeking Dialogue},
	author={Dziri, Nouha and Kamalloo, Ehsan and Milton, Sivan and Zaiane, Osmar and Yu, Mo and Ponti, Edoardo and Reddy, Siva},
	journal={arXiv preprint, arXiv:2204.10757},
	year={2022},
	url={https://arxiv.org/abs/2204.10757}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	FaithDial is a new benchmark for hallucination-free dialogues, created by manually editing hallucinated and uncooperative responses in Wizard of Wikipedia.
	"""

	_HOMEPAGE = "https://mcgill-nlp.github.io/FaithDial/"

	_LICENSE = "MIT"

	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLS = {
	"train": "data/train.json",
	"valid": "data/valid.json",
	"valid_random_split": "data/valid_random_split.json",
	"valid_topic_split": "data/valid_topic_split.json",
	"test": "data/test.json",
	"test_random_split": "data/test_random_split.json",
	"test_topic_split": "data/test_topic_split.json",
	}


	class FaithDialDataset(datasets.GeneratorBasedBuilder):
	"""FaithDial is a new benchmark for hallucination-free dialogues."""

	VERSION = datasets.Version("1.0.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="plain_text", version=VERSION, description="Plain text"),
	]

	DEFAULT_CONFIG_NAME = (
	"plain_text" # It's not mandatory to have a default configuration. Just use one if it make sense.
	)

	def _info(self):
	features = datasets.Features(
	{
	"dialog_idx": datasets.Value("int32"),
	"response": datasets.Value("string"),
	"original_response": datasets.Value("string"),
	"history": datasets.features.Sequence(datasets.Value("string")),
	"knowledge": datasets.Value("string"),
	"BEGIN": datasets.features.Sequence(datasets.Value("string")),
	"VRM": datasets.features.Sequence(datasets.Value("string")),
	}
	)

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	downloaded_files = dl_manager.download_and_extract(_URLS)

	split_dict = {
	"train": datasets.Split.TRAIN,
	"valid": datasets.Split.VALIDATION,
	"test": datasets.Split.TEST,
	}

	return [
	datasets.SplitGenerator(
	name=split_dict.get(split, split),
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_file,
	"split": split,
	},
	)
	for split, downloaded_file in sorted(downloaded_files.items(), key=lambda x: x[0])
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)
	key = 0
	for dialogue in data:
	for utterance in dialogue["utterances"]:
	yield key, {
	"dialog_idx": dialogue["dialog_idx"],
	"response": utterance["response"],
	"original_response": utterance["original_response"],
	"history": utterance["history"],
	"knowledge": utterance["knowledge"],
	"BEGIN": utterance["BEGIN"],
	"VRM": utterance["VRM"],
	}
	key += 1