# 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"], }