Datasets:

McGill-NLP
/

TopiOCQA

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# 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.

	# Lint as: python3
	"""TopiOCQA: Open-domain Conversational Question Answering with Topic Switching"""


	import json

	import datasets
	# from datasets.tasks import QuestionAnsweringExtractive


	logger = datasets.logging.get_logger(__name__)


	_DESCRIPTION = """\
	TopiOCQA is an information-seeking conversational dataset with challenging topic switching phenomena.
	"""

	_URLS = {
	"train": "data/topiocqa_train.jsonl",
	"valid": "data/topiocqa_valid.jsonl",
	}


	class TopiOCQAConfig(datasets.BuilderConfig):
	"""BuilderConfig for TopiOCQA."""

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

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(TopiOCQAConfig, self).__init__(**kwargs)


	class TopiOCQA(datasets.GeneratorBasedBuilder):
	"""TopiOCQA: Open-domain Conversational Question Answering with Topic Switching"""

	BUILDER_CONFIGS = [
	TopiOCQAConfig(
	name="plain_text",
	version=datasets.Version("1.0.1", ""),
	description="Plain text",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"Conversation_no": datasets.Value("int32"),
	"Turn_no": datasets.Value("int32"),
	"Question": datasets.Value("string"),
	"Answer": datasets.Value("string"),
	"Topic": datasets.Value("string"),
	"Topic_section": datasets.Value("string"),
	"Rationale": datasets.Value("string"),
	"is_nq": datasets.Value("bool"),
	"Context": datasets.features.Sequence(datasets.Value("string")),
	"Additional_answers": datasets.features.Sequence(
	{
	"Answer": datasets.Value("string"),
	"Topic": datasets.Value("string"),
	"Topic_section": datasets.Value("string"),
	"Rationale": datasets.Value("string"),
	}
	),
	"Gold_passage": {
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"text": datasets.Value("string"),
	}
	}
	),
	supervised_keys=None,
	homepage="https://mcgill-nlp.github.io/topiocqa/",
	)

	def _split_generators(self, dl_manager):
	downloaded_files = dl_manager.download_and_extract(_URLS)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	logger.info("generating examples from = %s", filepath)
	key = 0
	with open(filepath, encoding="utf-8") as f:
	for line in f:
	data = json.loads(line)
	yield key, {
	"Conversation_no": data["Conversation_no"],
	"Turn_no": data["Turn_no"],
	"Question": data["Question"],
	"Answer": data["Answer"],
	"Topic": data["Topic"],
	"Topic_section": data["Topic_section"],
	"Rationale": data["Rationale"],
	"is_nq": data["is_nq"],
	"Context": data["Context"],
	"Additional_answers": data["Additional_answers"],
	"Gold_passage": data["Gold_passage"],
	}
	key += 1