Datasets:
ubuntu_dialogs_corpus

	"""TODO(ubuntu_dialogs_corpus): Add a description here."""


	import csv
	import os

	import datasets


	# TODO(ubuntu_dialogs_corpus): BibTeX citation
	_CITATION = """\
	@article{DBLP:journals/corr/LowePSP15,
	author = {Ryan Lowe and
	Nissan Pow and
	Iulian Serban and
	Joelle Pineau},
	title = {The Ubuntu Dialogue Corpus: {A} Large Dataset for Research in Unstructured
	Multi-Turn Dialogue Systems},
	journal = {CoRR},
	volume = {abs/1506.08909},
	year = {2015},
	url = {http://arxiv.org/abs/1506.08909},
	archivePrefix = {arXiv},
	eprint = {1506.08909},
	timestamp = {Mon, 13 Aug 2018 16:48:23 +0200},
	biburl = {https://dblp.org/rec/journals/corr/LowePSP15.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	# TODO(ubuntu_dialogs_corpus):
	_DESCRIPTION = """\
	Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter.
	"""


	class UbuntuDialogsCorpusConfig(datasets.BuilderConfig):
	"""BuilderConfig for UbuntuDialogsCorpus."""

	def __init__(self, features, **kwargs):
	"""BuilderConfig for UbuntuDialogsCorpus.

	Args:

	**kwargs: keyword arguments forwarded to super.
	"""

	super(UbuntuDialogsCorpusConfig, self).__init__(version=datasets.Version("2.0.0"), **kwargs)
	self.features = features


	class UbuntuDialogsCorpus(datasets.GeneratorBasedBuilder):
	"""TODO(ubuntu_dialogs_corpus): Short description of my dataset."""

	# TODO(ubuntu_dialogs_corpus): Set up version.
	VERSION = datasets.Version("2.0.0")
	BUILDER_CONFIGS = [
	UbuntuDialogsCorpusConfig(
	name="train", features=["Context", "Utterance", "Label"], description="training features"
	),
	UbuntuDialogsCorpusConfig(
	name="dev_test",
	features=["Context", "Ground Truth Utterance"] + ["Distractor_" + str(i) for i in range(9)],
	description="test and dev features",
	),
	]

	@property
	def manual_download_instructions(self):
	return """\
	Please download the Ubuntu Dialog Corpus from https://github.com/rkadlec/ubuntu-ranking-dataset-creator. Run ./generate.sh -t -s -l to download the
	data. Others arguments are left to their default values here. Please save train.csv, test.csv and valid.csv in the same path"""

	def _info(self):
	# TODO(ubuntu_dialogs_corpus): Specifies the datasets.DatasetInfo object
	features = {feature: datasets.Value("string") for feature in self.config.features}
	if self.config.name == "train":
	features["Label"] = datasets.Value("int32")
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# datasets.features.FeatureConnectors
	features=datasets.Features(
	# These are the features of your dataset like images, labels ...
	features
	),
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage="https://github.com/rkadlec/ubuntu-ranking-dataset-creator",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(ubuntu_dialogs_corpus): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	manual_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))

	if self.config.name == "train":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(manual_dir, "train.csv")},
	),
	]
	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(manual_dir, "test.csv")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(manual_dir, "valid.csv")},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	# TODO(ubuntu_dialogs_corpus): Yields (key, example) tuples from the dataset
	with open(filepath, encoding="utf-8") as f:
	data = csv.DictReader(f)
	for id_, row in enumerate(data):
	yield id_, row