Datasets:
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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
import csv
import os
import datasets
_DOWNLOAD_URL = "https://raw.githubusercontent.com/timpal0l/sts-benchmark-swedish/master/data/stsb-mt-sv.zip"
_TRAIN_FILE = "train-sv.tsv"
_VAL_FILE = "dev-sv.tsv"
_TEST_FILE = "test-sv.tsv"
_CITATION = """\
@article{isbister2020not,
title={Why Not Simply Translate? A First Swedish Evaluation Benchmark for Semantic Similarity},
author={Isbister, Tim and Sahlgren, Magnus},
journal={arXiv preprint arXiv:2009.03116},
year={2020}
}
"""
_DESCRIPTION = "Machine translated Swedish version of the original STS-B (http://ixa2.si.ehu.eus/stswiki)"
class StsbMtSv(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text import of the Swedish Machine Translated STS-B",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"sentence1": datasets.Value("string"),
"sentence2": datasets.Value("string"),
"score": datasets.Value("float"),
}
),
supervised_keys=None,
homepage="https://github.com/timpal0l/sts-benchmark-swedish",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(dl_dir, _TEST_FILE)},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": os.path.join(dl_dir, _VAL_FILE)},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": os.path.join(dl_dir, _TRAIN_FILE)},
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for idx, row in enumerate(reader):
yield idx, {
"sentence1": row["sentence1"],
"sentence2": row["sentence2"],
"score": row["score"],
}
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