Datasets:
hindi_discourse

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""TODO: Add a description here."""


	import json

	import datasets


	_CITATION = """\
	@inproceedings{swapnil2020,
	title={An Annotated Dataset of Discourse Modes in Hindi Stories},
	author={Swapnil Dhanwal, Hritwik Dutta, Hitesh Nankani, Nilay Shrivastava, Yaman Kumar, Junyi Jessy Li, Debanjan Mahata, Rakesh Gosangi, Haimin Zhang, Rajiv Ratn Shah, Amanda Stent},
	booktitle={Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
	volume={12},
	pages={1191–1196},
	year={2020}
	"""

	_DESCRIPTION = """\
	The Hindi Discourse Analysis dataset is a corpus for analyzing discourse modes present in its sentences.
	It contains sentences from stories written by 11 famous authors from the 20th Century.
	4-5 stories by each author have been selected which were available in the public domain resulting
	in a collection of 53 stories. Most of these short stories were originally written in Hindi
	but some of them were written in other Indian languages and later translated to Hindi.
	"""


	_DOWNLOAD_URL = "https://raw.githubusercontent.com/midas-research/hindi-discourse/master/discourse_dataset.json"


	class HindiDiscourse(datasets.GeneratorBasedBuilder):
	"""Hindi Discourse Dataset - dataset of discourse modes in Hindi stories."""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	# This method pecifies the datasets.DatasetInfo object which contains informations and typings for the dataset

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"Story_no": datasets.Value("int32"),
	"Sentence": datasets.Value("string"),
	"Discourse Mode": datasets.ClassLabel(
	names=["Argumentative", "Descriptive", "Dialogue", "Informative", "Narrative", "Other"]
	),
	}
	),
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage="https://github.com/midas-research/hindi-discourse",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	dataset_path = dl_manager.download_and_extract(_DOWNLOAD_URL)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dataset_path}),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	hindiDiscourse = json.load(f)

	for sentence, rowData in hindiDiscourse.items():
	yield sentence, {
	"Story_no": rowData["Story_no"],
	"Sentence": rowData["Sentence"],
	"Discourse Mode": rowData["Discourse Mode"],
	}