Upload DataSet.py

DataSet.py

@@ -1,103 +1,2 @@
 from datasets import load_dataset
-import json
-
-_DESCRIPTION = "This is a testing dataset"
-
-_CITATION = """\
-@inproceedings{clark2019boolq,
-  title={DataSet: Understand the working of Huggig Face},
-  author={DataMetica}
-}"""
-
-_URLS = {
-    "train": "train/" + "*.jsonl",
-    "test": "test/" + "*.json",
-}
-
-def _info(self):
-    return datasets.DatasetInfo(
-        description = _description,
-        features = datasets.Features(
-            {
-                "HIVE": datasets.Value("string"),
-                "BQ": datasets.Value("string"),
-            }
-        ),
-    )
-
-load_dataset("dataSet")
-
-class DataSetConfig(datasets.BuilderConfig):
-    """BuilderConfig for SuperGLUE."""
-
-    def __init__(self, features, citation, url, label_classes=("False", "True"), **kwargs):
-        """BuilderConfig for DataSet.
-        Args:
-          features: `list[string]`, list of the features that will appear in the
-            feature dict. Should not include "label".
-          data_url: `string`, url to download the zip file from.
-          citation: `string`, citation for the data set.
-          url: `string`, url for information about the data set.
-          label_classes: `list[string]`, the list of classes for the label if the
-            label is present as a string. Non-string labels will be cast to either
-            'False' or 'True'.
-          **kwargs: keyword arguments forwarded to super.
-        """
-        # Version history:
-        # 1.0.0: Initial version.
-        super(DataSetConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
-        self.features = features
-        self.label_classes = label_classes
-        self.citation = citation
-        self.url = url
-
-
-class DataSet(datasets.GeneratorBasedBuilder):
-    """The SuperGLUE benchmark."""
-
-    BUILDER_CONFIGS = [
-        DataSetConfig(
-            name="dataSet",
-            description=_DESCRIPTION,
-            features=["HIVE", "BQ"],
-            supervised_keys=None,
-            citation=_BOOLQ_CITATION,
-            url=""
-        )
-
-    def _info(self):
-        features = {feature: datasets.Value("string") for feature in self.config.features}
-        return datasets.DatasetInfo(
-            description=_GLUE_DESCRIPTION + self.config.description,
-            features=datasets.Features(features),
-            homepage=self.config.url,
-            citation=self.config.citation + "\n" + _SUPER_GLUE_CITATION,
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        urls_to_download = self._URLS
-        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.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) as f:
-            squad = json.load(f)
-            for element in squad["data"]:
-                inputQuery = article.get("HIVE", "").strip()
-                output = article.get("BQ", "").strip()
-            # Features currently used are "context", "question", and "answers".
-                    # Others are extracted here for the ease of future expansions.
-                yield id_, {
-                    "HIVE": {inputQuery},
-                    "BQ": {output}
-                }
-
-
-
-
+load_dataset("sg19296/DataSet")