Delete loading script

math_qa.py

@@ -1,84 +0,0 @@
-"""TODO(math_qa): Add a description here."""
-
-
-import json
-import os
-
-import datasets
-
-
-# TODO(math_qa): BibTeX citation
-_CITATION = """
-"""
-
-# TODO(math_qa):
-_DESCRIPTION = """
-Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset. AQuA-RAT has provided the questions, options, rationale, and the correct options.
-"""
-_URL = "https://math-qa.github.io/math-QA/data/MathQA.zip"
-
-
-class MathQa(datasets.GeneratorBasedBuilder):
-    """TODO(math_qa): Short description of my dataset."""
-
-    # TODO(math_qa): Set up version.
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        # TODO(math_qa): Specifies the datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    # These are the features of your dataset like images, labels ...
-                    "Problem": datasets.Value("string"),
-                    "Rationale": datasets.Value("string"),
-                    "options": datasets.Value("string"),
-                    "correct": datasets.Value("string"),
-                    "annotated_formula": datasets.Value("string"),
-                    "linear_formula": datasets.Value("string"),
-                    "category": datasets.Value("string"),
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://math-qa.github.io/math-QA/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(math_qa): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        dl_path = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(dl_path, "train.json")},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(dl_path, "test.json")},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(dl_path, "dev.json")},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        # TODO(math_qa): Yields (key, example) tuples from the dataset
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for id_, row in enumerate(data):
-                yield id_, row