Upload DataSet.py

DataSet.py

@@ -0,0 +1,103 @@
+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}
+                }
+
+
+
+