Datasets:

sagteam
/

cedr_v1

	# 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.

	# Lint as: python3
	"""CEDR dataset"""

	import json
	import os

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@article{sboev2021data,
	title={Data-Driven Model for Emotion Detection in Russian Texts},
	author={Sboev, Alexander and Naumov, Aleksandr and Rybka, Roman},
	journal={Procedia Computer Science},
	volume={190},
	pages={637--642},
	year={2021},
	publisher={Elsevier}
	}
	"""

	_LICENSE = """http://www.apache.org/licenses/LICENSE-2.0"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	This new dataset is designed to solve emotion recognition task for text data in Russian. The Corpus for Emotions Detecting in
	Russian-language text sentences of different social sources (CEDR) contains 9410 sentences in Russian labeled for 5 emotion
	categories. The data collected from different sources: posts of the LiveJournal social network, texts of the online news
	agency Lenta.ru, and Twitter microblog posts. There are two variants of the corpus: main and enriched. The enriched variant
	is include tokenization and lemmatization. Dataset with predefined train/test splits.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://github.com/sag111/CEDR"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLs = {
	"main": "https://sagteam.ru/cedr/main.zip",
	"enriched": "https://sagteam.ru/cedr/enriched.zip",
	}


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class Cedr(datasets.GeneratorBasedBuilder):
	"""This dataset is designed to solve emotion recognition task for text data in Russian."""

	VERSION = datasets.Version("0.1.1")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="main", version=VERSION, description="This part of CEDR dataset covers a main version"
	),
	datasets.BuilderConfig(
	name="enriched", version=VERSION, description="This part of CEDR dataset covers a enriched version"
	),
	]

	DEFAULT_CONFIG_NAME = "main" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	if self.config.name == "main": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"text": datasets.Value("string"),
	"labels": datasets.features.Sequence(
	datasets.ClassLabel(names=["joy", "sadness", "surprise", "fear", "anger"])
	),
	"source": datasets.Value("string"),
	# These are the features of your dataset like images, labels ...
	}
	)
	else: # This is an example to show how to have different features for "first_domain" and "second_domain"
	features = datasets.Features(
	{
	"text": datasets.Value("string"),
	"labels": datasets.features.Sequence(
	datasets.ClassLabel(names=["joy", "sadness", "surprise", "fear", "anger"])
	),
	"source": datasets.Value("string"),
	"sentences": [
	[
	{
	"forma": datasets.Value("string"),
	"lemma": datasets.Value("string"),
	}
	]
	]
	# These are the features of your dataset like images, labels ...
	}
	)
	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):
	"""Returns SplitGenerators."""
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	my_urls = _URLs[self.config.name]
	data_dir = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, self.config.name, "train.jsonl"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, self.config.name, "test.jsonl"), "split": "test"},
	),
	]

	def _generate_examples(
	self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	):
	"""Yields examples as (key, example) tuples."""
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.

	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	if self.config.name == "main":
	yield id_, {
	"text": data["text"],
	"source": data["source"],
	"labels": data["labels"],
	}
	else:
	yield id_, {
	"text": data["text"],
	"source": data["source"],
	"sentences": data["sentences"],
	"labels": data["labels"],
	}