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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """ |
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Rodriguez-Penagos, Carlos Gerardo, Armentano-Oller, Carme, Gonzalez-Agirre, Aitor, & Gibert Bonet, Ona. (2021). |
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Semantic Textual Similarity in Catalan (Version 1.0.1) [Data set]. |
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Zenodo. http://doi.org/10.5281/zenodo.4761434 |
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""" |
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_DESCRIPTION = """ |
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Semantic Textual Similarity in Catalan. |
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STS corpus is a benchmark for evaluating Semantic Text Similarity in Catalan. |
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It consists of more than 3000 sentence pairs, annotated with the semantic similarity between them, |
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using a scale from 0 (no similarity at all) to 5 (semantic equivalence). |
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It is done manually by 4 different annotators following our guidelines based on previous work from the SemEval challenges (https://www.aclweb.org/anthology/S13-1004.pdf). |
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The source data are scraped sentences from the Catalan Textual Corpus (https://doi.org/10.5281/zenodo.4519349), used under CC-by-SA-4.0 licence (https://creativecommons.org/licenses/by-sa/4.0/). The dataset is released under the same licence. |
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This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB). |
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This is the version 1.0.2 of the dataset with the complete human and automatic annotations and the analysis scripts. It also has a more accurate license. |
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This dataset can be used to build and score semantic similiarity models. |
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""" |
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_HOMEPAGE = """https://zenodo.org/record/4761434""" |
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_URL = "https://huggingface.co/datasets/BSC-TeMU/sts-ca/resolve/main/" |
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_TRAINING_FILE = "train.tsv" |
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_DEV_FILE = "dev.tsv" |
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_TEST_FILE = "test.tsv" |
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class StsCaConfig(datasets.BuilderConfig): |
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""" Builder config for the Semantic Textual Similarity Ca dataset """ |
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def __init__(self, **kwargs): |
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"""BuilderConfig for StsCa. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(StsCaConfig, self).__init__(**kwargs) |
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class StsCa(datasets.GeneratorBasedBuilder): |
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"""Semantic Textual Similarity Ca dataset.""" |
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BUILDER_CONFIGS = [ |
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StsCaConfig( |
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name="StsCa", |
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version=datasets.Version("1.0.2"), |
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description="Semantic Textual Similarity in catalan dataset" |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"sentence1": datasets.Value("string"), |
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"sentence2": datasets.Value("string"), |
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"label": datasets.Value("float"), |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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urls_to_download = { |
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"train": f"{_URL}{_TRAINING_FILE}", |
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"dev": f"{_URL}{_DEV_FILE}", |
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"test": f"{_URL}{_TEST_FILE}", |
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} |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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] |
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def _generate_examples(self, filepath): |
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""" Returns the examples in the raw text form """ |
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logger.info("⏳ Generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as f: |
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for id_, row in enumerate(f): |
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ref, sentence1, sentence2, score = row.split('\t') |
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yield id_, { |
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"sentence1": sentence1, |
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"sentence2": sentence2, |
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"label": score, |
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} |
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