Datasets:
empathetic_dialogues

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


	import csv

	import datasets


	_CITATION = """\
	@inproceedings{rashkin2019towards,
	title = {Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset},
	author = {Hannah Rashkin and Eric Michael Smith and Margaret Li and Y-Lan Boureau},
	booktitle = {ACL},
	year = {2019},
	}
	"""

	_DESCRIPTION = """\
	PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
	"""
	_URL = "https://dl.fbaipublicfiles.com/parlai/empatheticdialogues/empatheticdialogues.tar.gz"


	class EmpatheticDialogues(datasets.GeneratorBasedBuilder):
	"""TODO(empathetic_dialogues): Short description of my dataset."""

	# TODO(empathetic_dialogues): Set up version.
	VERSION = datasets.Version("0.1.0")

	def _info(self):
	# TODO(empathetic_dialogues): Specifies the datasets.DatasetInfo object
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# datasets.features.FeatureConnectors
	features=datasets.Features(
	{
	"conv_id": datasets.Value("string"),
	"utterance_idx": datasets.Value("int32"),
	"context": datasets.Value("string"),
	"prompt": datasets.Value("string"),
	"speaker_idx": datasets.Value("int32"),
	"utterance": datasets.Value("string"),
	"selfeval": datasets.Value("string"),
	"tags": datasets.Value("string")
	# These are the features of your dataset like images, labels ...
	}
	),
	# 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/facebookresearch/EmpatheticDialogues",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO(empathetic_dialogues): Downloads the data and defines the splits
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs
	archive = dl_manager.download(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"files": dl_manager.iter_archive(archive), "split_file": "empatheticdialogues/train.csv"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"files": dl_manager.iter_archive(archive), "split_file": "empatheticdialogues/valid.csv"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"files": dl_manager.iter_archive(archive), "split_file": "empatheticdialogues/test.csv"},
	),
	]

	def _generate_examples(self, files, split_file):
	"""Yields examples."""
	for path, f in files:
	if split_file == path:
	data = csv.DictReader(line.decode("utf-8") for line in f)
	for id_, row in enumerate(data):
	utterance = row["utterance"]
	speaker_id = int(row["speaker_idx"])
	context = row["context"]
	conv_id = row["conv_id"]
	tags = row["tags"] if row["tags"] else ""
	selfeval = row["selfeval"] if row["selfeval"] else ""
	utterance_id = int(row["utterance_idx"])
	prompt = row["prompt"]
	yield id_, {
	"utterance": utterance,
	"utterance_idx": utterance_id,
	"context": context,
	"speaker_idx": speaker_id,
	"conv_id": conv_id,
	"selfeval": selfeval,
	"prompt": prompt,
	"tags": tags,
	}
	break