Delete loading script

qasc.py

@@ -1,123 +0,0 @@
-"""TODO(qasc): Add a description here."""
-
-
-import json
-
-import datasets
-
-
-# TODO(qasc): BibTeX citation
-_CITATION = """\
-@article{allenai:qasc,
-      author    = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},
-      title     = {QASC: A Dataset for Question Answering via Sentence Composition},
-      journal   = {arXiv:1910.11473v2},
-      year      = {2020},
-}
-"""
-
-# TODO(qasc):
-_DESCRIPTION = """
-QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice
-questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.
-"""
-_URl = "http://data.allenai.org/downloads/qasc/qasc_dataset.tar.gz"
-
-
-class Qasc(datasets.GeneratorBasedBuilder):
-    """TODO(qasc): Short description of my dataset."""
-
-    # TODO(qasc): Set up version.
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        # TODO(qasc): 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(
-                {
-                    "id": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "choices": datasets.features.Sequence(
-                        {"text": datasets.Value("string"), "label": datasets.Value("string")}
-                    ),
-                    "answerKey": datasets.Value("string"),
-                    "fact1": datasets.Value("string"),
-                    "fact2": datasets.Value("string"),
-                    "combinedfact": datasets.Value("string"),
-                    "formatted_question": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
-                }
-            ),
-            # 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://allenai.org/data/qasc",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO(qasc): Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        archive = dl_manager.download(_URl)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "/".join(["QASC_Dataset", "train.jsonl"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "/".join(["QASC_Dataset", "test.jsonl"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": "/".join(["QASC_Dataset", "dev.jsonl"]),
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, files):
-        """Yields examples."""
-        # TODO(qasc): Yields (key, example) tuples from the dataset
-        for path, f in files:
-            if path == filepath:
-                for row in f:
-                    data = json.loads(row.decode("utf-8"))
-                    answerkey = data.get("answerKey", "")
-                    id_ = data["id"]
-                    question = data["question"]["stem"]
-                    choices = data["question"]["choices"]
-                    text_choices = [choice["text"] for choice in choices]
-                    label_choices = [choice["label"] for choice in choices]
-                    fact1 = data.get("fact1", "")
-                    fact2 = data.get("fact2", "")
-                    combined_fact = data.get("combinedfact", "")
-                    formatted_question = data.get("formatted_question", "")
-                    yield id_, {
-                        "id": id_,
-                        "answerKey": answerkey,
-                        "question": question,
-                        "choices": {"text": text_choices, "label": label_choices},
-                        "fact1": fact1,
-                        "fact2": fact2,
-                        "combinedfact": combined_fact,
-                        "formatted_question": formatted_question,
-                    }
-                break