Delete Parafraseja.py

Parafraseja.py

@@ -1,100 +0,0 @@
-# Loading script for the ReviewsFinder dataset.
-
-
-import json
-import csv
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_CITATION = """ """
-
-
-_DESCRIPTION = """ Parafraseja is a dataset of 16,584 pairs of sentences with a label that indicates if they are paraphrases or not. The original sentences were collected from TE-ca and STS-ca. For each sentence, an annotator wrote a sentence that was a paraphrase and another that was not. The guidelines of this annotation are available.  """
-
-
-_HOMEPAGE = """ https://huggingface.co/datasets/projecte-aina/Parafraseja/ """
-
-
-
-_URL = "https://huggingface.co/datasets/projecte-aina/Parafraseja/resolve/main/"
-_TRAINING_FILE = "train.jsonl"
-_DEV_FILE = "dev.jsonl"
-_TEST_FILE = "test.jsonl"
-
-
-class ParafrasejaConfig(datasets.BuilderConfig):
-    """ Builder config for the Parafraseja dataset """
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for parafrasis.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(ParafrasejaConfig, self).__init__(**kwargs)
-
-
-class Parafraseja(datasets.GeneratorBasedBuilder):
-    """ Parafrasis Dataset """
-
-
-    BUILDER_CONFIGS = [
-        ParafrasejaConfig(
-            name="Parafraseja",
-            version=datasets.Version("1.0.0"),
-            description="Parafraseja dataset",
-        ),
-    ]
-
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sentence1": datasets.Value("string"),
-                    "sentence2": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel
-                    (names=
-                        [
-                            "No Parafrasis",
-                            "Parafrasis",
-                        ]
-                    ),
-                }
-            ),
-            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):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            data = [json.loads(line) for line in f]
-            for id_, article in enumerate(data):
-                    yield id_, {
-                        "sentence1": article['original'],
-                        "sentence2": article['new'],
-                        "label": article['label'],
-                    }