Datasets:

sagteam
/

cedr_v1

@@ -1,188 +0,0 @@
-# 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"],
-                    }