Delete loading script

codah.py

@@ -1,141 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The COmmonsense Dataset Adversarially-authored by Humans (CODAH)"""
-
-
-import csv
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{chen2019codah,
-  title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},
-  author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},
-  booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},
-  pages={63--69},
-  year={2019}
-}
-"""
-
-_DESCRIPTION = """\
-The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense \
-question-answering in the sentence completion style of SWAG. As opposed to other automatically \
-generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback \
-from a pre-trained model and use this information to design challenging commonsense questions. \
-Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.
-"""
-
-_URL = "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/"
-_FULL_DATA_URL = _URL + "full_data.tsv"
-
-QUESTION_CATEGORIES_MAPPING = {
-    "i": "Idioms",
-    "r": "Reference",
-    "p": "Polysemy",
-    "n": "Negation",
-    "q": "Quantitative",
-    "o": "Others",
-}
-
-
-class CodahConfig(datasets.BuilderConfig):
-    """BuilderConfig for CODAH."""
-
-    def __init__(self, fold=None, **kwargs):
-        """BuilderConfig for CODAH.
-
-        Args:
-          fold: `string`, official cross validation fold.
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(CodahConfig, self).__init__(**kwargs)
-        self.fold = fold
-
-
-class Codah(datasets.GeneratorBasedBuilder):
-    """The COmmonsense Dataset Adversarially-authored by Humans (CODAH)"""
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        CodahConfig(name="codah", version=datasets.Version("1.0.0"), description="Full CODAH dataset", fold=None),
-        CodahConfig(
-            name="fold_0", version=datasets.Version("1.0.0"), description="Official CV split (fold_0)", fold="fold_0"
-        ),
-        CodahConfig(
-            name="fold_1", version=datasets.Version("1.0.0"), description="Official CV split (fold_1)", fold="fold_1"
-        ),
-        CodahConfig(
-            name="fold_2", version=datasets.Version("1.0.0"), description="Official CV split (fold_2)", fold="fold_2"
-        ),
-        CodahConfig(
-            name="fold_3", version=datasets.Version("1.0.0"), description="Official CV split (fold_3)", fold="fold_3"
-        ),
-        CodahConfig(
-            name="fold_4", version=datasets.Version("1.0.0"), description="Official CV split (fold_4)", fold="fold_4"
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("int32"),
-                    "question_category": datasets.features.ClassLabel(
-                        names=["Idioms", "Reference", "Polysemy", "Negation", "Quantitative", "Others"]
-                    ),
-                    "question_propmt": datasets.Value("string"),
-                    "candidate_answers": datasets.features.Sequence(datasets.Value("string")),
-                    "correct_answer_idx": datasets.Value("int32"),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/Websail-NU/CODAH",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        if self.config.name == "codah":
-            data_file = dl_manager.download(_FULL_DATA_URL)
-            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_file": data_file})]
-
-        base_url = f"{_URL}cv_split/{self.config.fold}/"
-        _urls = {
-            "train": base_url + "train.tsv",
-            "dev": base_url + "dev.tsv",
-            "test": base_url + "test.tsv",
-        }
-        downloaded_files = dl_manager.download_and_extract(_urls)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_file": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"data_file": downloaded_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"data_file": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, data_file):
-        with open(data_file, encoding="utf-8") as f:
-            rows = csv.reader(f, delimiter="\t")
-            for i, row in enumerate(rows):
-                question_category = QUESTION_CATEGORIES_MAPPING[row[0]] if row[0] != "" else -1
-                example = {
-                    "id": i,
-                    "question_category": question_category,
-                    "question_propmt": row[1],
-                    "candidate_answers": row[2:-1],
-                    "correct_answer_idx": int(row[-1]),
-                }
-                yield i, example