Delete loading script

race.py

@@ -1,111 +0,0 @@
-"""TODO(race): Add a description here."""
-
-
-import json
-
-import datasets
-
-
-_CITATION = """\
-@article{lai2017large,
-    title={RACE: Large-scale ReAding Comprehension Dataset From Examinations},
-    author={Lai, Guokun and Xie, Qizhe and Liu, Hanxiao and Yang, Yiming and Hovy, Eduard},
-    journal={arXiv preprint arXiv:1704.04683},
-    year={2017}
-}
-"""
-
-_DESCRIPTION = """\
-Race is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The
- dataset is collected from English examinations in China, which are designed for middle school and high school students.
-The dataset can be served as the training and test sets for machine comprehension.
-
-"""
-
-_URL = "http://www.cs.cmu.edu/~glai1/data/race/RACE.tar.gz"
-
-
-class Race(datasets.GeneratorBasedBuilder):
-    """ReAding Comprehension Dataset From Examination dataset from CMU"""
-
-    VERSION = datasets.Version("0.1.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="high", description="Exams designed for high school students", version=VERSION),
-        datasets.BuilderConfig(
-            name="middle", description="Exams designed for middle school students", version=VERSION
-        ),
-        datasets.BuilderConfig(
-            name="all", description="Exams designed for both high school and middle school students", version=VERSION
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=datasets.Features(
-                {
-                    "example_id": datasets.Value("string"),
-                    "article": datasets.Value("string"),
-                    "answer": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "options": datasets.features.Sequence(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="http://www.cs.cmu.edu/~glai1/data/race/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # Downloads the data and defines the splits
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        archive = dl_manager.download(_URL)
-        case = str(self.config.name)
-        if case == "all":
-            case = ""
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"train_test_or_eval": f"RACE/test/{case}", "files": dl_manager.iter_archive(archive)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"train_test_or_eval": f"RACE/train/{case}", "files": dl_manager.iter_archive(archive)},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"train_test_or_eval": f"RACE/dev/{case}", "files": dl_manager.iter_archive(archive)},
-            ),
-        ]
-
-    def _generate_examples(self, train_test_or_eval, files):
-        """Yields examples."""
-        for file_idx, (path, f) in enumerate(files):
-            if path.startswith(train_test_or_eval) and path.endswith(".txt"):
-                data = json.loads(f.read().decode("utf-8"))
-                questions = data["questions"]
-                answers = data["answers"]
-                options = data["options"]
-                for i in range(len(questions)):
-                    question = questions[i]
-                    answer = answers[i]
-                    option = options[i]
-                    yield f"{file_idx}_{i}", {
-                        "example_id": data["id"],
-                        "article": data["article"],
-                        "question": question,
-                        "answer": answer,
-                        "options": option,
-                    }