Delete sts-ca.py

sts-ca.py

@@ -1,95 +0,0 @@
-# Loading script for the Semantic Textual Similarity Ca dataset.
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_CITATION = """
-               Rodriguez-Penagos, Carlos Gerardo, Armentano-Oller, Carme, Gonzalez-Agirre, Aitor, & Gibert Bonet, Ona. (2021). 
-               Semantic Textual Similarity in Catalan (Version 1.0.1) [Data set]. 
-               Zenodo. http://doi.org/10.5281/zenodo.4761434
-            """
-
-_DESCRIPTION = """
-                  Semantic Textual Similarity in Catalan.
-                  STS corpus is a benchmark for evaluating Semantic Text Similarity in Catalan.
-                  It consists of more than 3000 sentence pairs, annotated with the semantic similarity between them, 
-                  using a scale from 0 (no similarity at all) to 5 (semantic equivalence). 
-                  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).
-                  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.
-                  This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB).
-                  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.
-                  This dataset can be used to build and score semantic similiarity models.
-               """
-
-_HOMEPAGE = """https://zenodo.org/record/4761434"""
-
-# TODO: upload datasets to github
-_URL = "https://huggingface.co/datasets/projecte-aina/sts-ca/resolve/main/"
-_TRAINING_FILE = "train.tsv"
-_DEV_FILE = "dev.tsv"
-_TEST_FILE = "test.tsv"
-
-
-class StsCaConfig(datasets.BuilderConfig):
-    """ Builder config for the Semantic Textual Similarity Ca dataset """
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for StsCa.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(StsCaConfig, self).__init__(**kwargs)
-
-
-class StsCa(datasets.GeneratorBasedBuilder):
-    """Semantic Textual Similarity Ca dataset."""
-
-    BUILDER_CONFIGS = [
-        StsCaConfig(
-            name="StsCa",
-            version=datasets.Version("1.0.2"),
-            description="Semantic Textual Similarity in catalan dataset"
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sentence1": datasets.Value("string"),
-                    "sentence2": datasets.Value("string"),
-                    "label": datasets.Value("float"),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = {
-            "train": f"{_URL}{_TRAINING_FILE}",
-            "dev": f"{_URL}{_DEV_FILE}",
-            "test": f"{_URL}{_TEST_FILE}",
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """ Returns the examples in the raw text form """
-        logger.info("⏳ Generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
-                ref, sentence1, sentence2, score = row.split('\t')
-                yield id_, {
-                    "sentence1": sentence1,
-                    "sentence2": sentence2,
-                    "label": score,
-                }