Datasets:
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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.
"""RO-STS: The Romanian Semantic Textual Similarity Dataset"""
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
Article under review
"""
# You can copy an official description
_DESCRIPTION = """\
The RO-STS (Romanian Semantic Textual Similarity) dataset contains 8628 pairs of sentences with their similarity score. It is a high-quality translation of the STS benchmark dataset.
"""
_HOMEPAGE = "https://github.com/dumitrescustefan/RO-STS/"
_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/dumitrescustefan/RO-STS/master/dataset/text-similarity/"
_TRAINING_FILE = "RO-STS.train.tsv"
_TEST_FILE = "RO-STS.test.tsv"
_DEV_FILE = "RO-STS.dev.tsv"
class ROSTSConfig(datasets.BuilderConfig):
"""BuilderConfig for RO-STS dataset"""
def __init__(self, **kwargs):
super(ROSTSConfig, self).__init__(**kwargs)
class RoSts(datasets.GeneratorBasedBuilder):
"""RO-STS dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
ROSTSConfig(name="ro_sts", version=VERSION, description="RO-STS dataset"),
]
def _info(self):
features = datasets.Features(
{
"score": datasets.Value("float"),
"sentence1": datasets.Value("string"),
"sentence2": 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):
"""Returns SplitGenerators."""
urls_to_download = {"train": _URL + _TRAINING_FILE, "dev": _URL + _DEV_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"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={"filepath": downloaded_files["dev"]},
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
with open(filepath, encoding="utf-8") as f:
reader = f.readlines()
for idx, row in enumerate(reader):
splits = row.strip().split("\t")
yield idx, {
"score": splits[0], # row["score"],
"sentence1": splits[1], # row["sentence1"],
"sentence2": splits[2], # row["sentence2"],
}
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