ro_sts.py

# 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
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""RO-STS: The Romanian Semantic Textual Similarity Dataset"""


import datasets


_CITATION = """\
@inproceedings{dumitrescu2021liro,
  title={Liro: Benchmark and leaderboard for romanian language tasks},
  author={Dumitrescu, Stefan Daniel and Rebeja, Petru and Lorincz, Beata and Gaman, Mihaela and Avram, Andrei and Ilie, Mihai and Pruteanu, Andrei and Stan, Adriana and Rosia, Lorena and Iacobescu, Cristina and others},
  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},
  year={2021}
}
"""

_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(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            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"],
                }