Delete loading script

openbookqa.py

@@ -1,159 +0,0 @@
-"""OpenBookQA dataset."""
-
-
-import json
-import os
-import textwrap
-
-import datasets
-
-
-_HOMEPAGE = "https://allenai.org/data/open-book-qa"
-
-_DESCRIPTION = """\
-OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic
-(with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In
-particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge,
-and rich text comprehension.
-OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding
-of a subject.
-"""
-
-_CITATION = """\
-@inproceedings{OpenBookQA2018,
- title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},
- author={Todor Mihaylov and Peter Clark and Tushar Khot and Ashish Sabharwal},
- booktitle={EMNLP},
- year={2018}
-}
-"""
-
-_URL = "https://s3-us-west-2.amazonaws.com/ai2-website/data/OpenBookQA-V1-Sep2018.zip"
-
-
-class OpenbookqaConfig(datasets.BuilderConfig):
-    def __init__(self, data_dir=None, filenames=None, version=datasets.Version("1.0.1", ""), **kwargs):
-        """BuilderConfig for openBookQA dataset
-
-        Args:
-          data_dir: directory for the given dataset name
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super().__init__(version=version, **kwargs)
-        self.data_dir = data_dir
-        self.filenames = filenames
-
-
-class Openbookqa(datasets.GeneratorBasedBuilder):
-    """OpenBookQA dataset."""
-
-    BUILDER_CONFIGS = [
-        OpenbookqaConfig(
-            name="main",
-            description=textwrap.dedent(
-                """\
-                It consists of 5,957 multiple-choice elementary-level science questions (4,957 train, 500 dev, 500 test),
-                which probe the understanding of a small “book” of 1,326 core science facts and the application of these facts to novel
-                situations. For training, the dataset includes a mapping from each question to the core science fact it was designed to
-                probe. Answering OpenBookQA questions requires additional broad common knowledge, not contained in the book. The questions,
-                by design, are answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. Strong neural
-                baselines achieve around 50% on OpenBookQA, leaving a large gap to the 92% accuracy of crowd-workers.
-                """
-            ),
-            data_dir="Main",
-            filenames={
-                "train": "train.jsonl",
-                "validation": "dev.jsonl",
-                "test": "test.jsonl",
-            },
-        ),
-        OpenbookqaConfig(
-            name="additional",
-            description=textwrap.dedent(
-                """\
-                Additionally, we provide 5,167 crowd-sourced common knowledge facts, and an expanded version of the train/dev/test questions where
-                each question is associated with its originating core fact, a human accuracy score, a clarity score, and an anonymized crowd-worker
-                ID (in the 'Additional' folder).
-                """
-            ),
-            data_dir="Additional",
-            filenames={
-                "train": "train_complete.jsonl",
-                "validation": "dev_complete.jsonl",
-                "test": "test_complete.jsonl",
-            },
-        ),
-    ]
-    DEFAULT_CONFIG_NAME = "main"
-
-    def _info(self):
-        if self.config.name == "main":
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "question_stem": datasets.Value("string"),
-                    "choices": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "label": datasets.Value("string"),
-                        }
-                    ),
-                    "answerKey": datasets.Value("string"),
-                }
-            )
-        else:
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "question_stem": datasets.Value("string"),
-                    "choices": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "label": datasets.Value("string"),
-                        }
-                    ),
-                    "answerKey": datasets.Value("string"),
-                    "fact1": datasets.Value("string"),
-                    "humanScore": datasets.Value("float"),
-                    "clarity": datasets.Value("float"),
-                    "turkIdAnonymized": datasets.Value("string"),
-                }
-            )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        dl_dir = dl_manager.download_and_extract(_URL)
-        data_dir = os.path.join(dl_dir, "OpenBookQA-V1-Sep2018", "Data", self.config.data_dir)
-        splits = [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]
-        return [
-            datasets.SplitGenerator(
-                name=split,
-                gen_kwargs={"filepath": os.path.join(data_dir, self.config.filenames[split])},
-            )
-            for split in splits
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            for uid, row in enumerate(f):
-                data = json.loads(row)
-                example = {
-                    "id": data["id"],
-                    "question_stem": data["question"]["stem"],
-                    "choices": {
-                        "text": [choice["text"] for choice in data["question"]["choices"]],
-                        "label": [choice["label"] for choice in data["question"]["choices"]],
-                    },
-                    "answerKey": data["answerKey"],
-                }
-                if self.config.name == "additional":
-                    for key in ["fact1", "humanScore", "clarity", "turkIdAnonymized"]:
-                        example[key] = data[key]
-                yield uid, example