Datasets:

persiannlp
/

parsinlu_sentiment

	# 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.
	"""ParsiNLU Persian reading comprehension task"""

	from __future__ import absolute_import, division, print_function

	import csv
	import json

	import datasets
	from datasets import NamedSplit

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """\
	@article{huggingface:dataset,
	title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
	authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
	year={2020}
	journal = {arXiv e-prints},
	eprint = {2012.06154},
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	A Persian sentiment analysis task (deciding whether a given sentence contains a particular sentiment).
	"""

	_HOMEPAGE = "https://github.com/persiannlp/parsinlu/"

	_LICENSE = "CC BY-NC-SA 4.0"

	_URL = "https://raw.githubusercontent.com/persiannlp/parsinlu/master/data/sentiment-analysis/"
	_URLs = {
	"train": _URL + "ABSA_Dataset_train.jsonl",
	"dev_food": _URL + "food_dev.jsonl",
	"dev_movies": _URL + "movie_dev.jsonl",
	"test_food": _URL + "food_test.jsonl",
	"test_movies": _URL + "movie_test.jsonl",
	}

	TRAIN_ALL = NamedSplit("train")
	TEST_FOOD = NamedSplit("test_food")
	TEST_MOVIES = NamedSplit("test_movies")
	VALIDATION_FOOD = NamedSplit("validation_food")
	VALIDATION_MOVIES = NamedSplit("validation_movies")


	class ParsinluReadingComprehension(datasets.GeneratorBasedBuilder):
	"""ParsiNLU Persian reading comprehension task."""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="parsinlu-repo", version=VERSION, description="ParsiNLU repository: sentiment-analysis"
	),
	]

	def _info(self):
	features = datasets.Features(
	{
	"review": datasets.Value("string"),
	"review_id": datasets.Value("string"),
	"example_id": datasets.Value("string"),
	"excel_id": datasets.Value("string"),
	"question": datasets.Value("string"),
	"category": datasets.Value("string"),
	"aspect": datasets.Value("string"),
	"label": datasets.Value("string"),
	"guid": 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,
	# 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=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)



	def _split_generators(self, dl_manager):
	data_dir = dl_manager.download_and_extract(_URLs)
	return [
	datasets.SplitGenerator(
	name=TRAIN_ALL,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir["train"],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=TEST_FOOD,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": data_dir["test_food"], "split": "test_food"},
	),
	datasets.SplitGenerator(
	name=TEST_MOVIES,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": data_dir["test_movies"], "split": "test_movies"},
	),
	datasets.SplitGenerator(
	name=VALIDATION_FOOD,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir["dev_food"],
	"split": "dev_food",
	},
	),
	datasets.SplitGenerator(
	name=VALIDATION_MOVIES,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir["dev_movies"],
	"split": "dev_movies",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	logger.info("generating examples from = %s", filepath)

	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f.readlines()):
	row = json.loads(row)
	yield id_, row