Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
Romanian
Size:
10K<n<100K
ArXiv:
License:
File size: 4,841 Bytes
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# coding=utf-8
# Copyright 2021 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.
"""LaRoSeDa: A Large Romanian Sentiment Data Set"""
import json
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{
tache2101clustering,
title={Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set},
author={Anca Maria Tache and Mihaela Gaman and Radu Tudor Ionescu},
journal={ArXiv},
year = {2021}
}
"""
# You can copy an official description
_DESCRIPTION = """\
LaRoSeDa (A Large Romanian Sentiment Data Set) contains 15,000 reviews written in Romanian, of which 7,500 are positive and 7,500 negative.
Star ratings of 1 and 2 and of 4 and 5 are provided for negative and positive reviews respectively.
The current dataset uses star rating as the label for multi-class classification.
"""
_HOMEPAGE = "https://github.com/ancatache/LaRoSeDa"
_LICENSE = "CC BY-SA 4.0 License"
# 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)
_URL = "https://raw.githubusercontent.com/ancatache/LaRoSeDa/main/data_splitted/"
_TRAIN_FILE = "laroseda_train.json"
_TEST_FILE = "laroseda_test.json"
class LarosedaConfig(datasets.BuilderConfig):
"""BuilderConfig for the LaRoSeDa dataset"""
def __init__(self, **kwargs):
super(LarosedaConfig, self).__init__(**kwargs)
class Laroseda(datasets.GeneratorBasedBuilder):
"""LaRoSeDa dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
LarosedaConfig(name="laroseda", version=VERSION, description="LaRoSeDa dataset"),
]
def _info(self):
features = datasets.Features(
{
"index": datasets.Value("string"),
"title": datasets.Value("string"),
"content": datasets.Value("string"),
"starRating": datasets.Value("int64"),
}
)
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."""
urls_to_download = {
"train": _URL + _TRAIN_FILE,
"test": _URL + _TEST_FILE,
}
downloaded_files = dl_manager.download(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["train"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": downloaded_files["test"],
},
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
with open(filepath, "r", encoding="utf-8") as f:
data_list = json.load(f)["reviews"]
for i, d in enumerate(data_list):
yield i, {
"index": d["index"],
"title": d["title"],
"content": d["content"],
"starRating": int(d["starRating"]),
}
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