Delete loading script

squadshifts.py

@@ -1,180 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-"""SQUAD: The Stanford Question Answering Dataset."""
-
-
-import json
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_DESCRIPTION = r"""\
-SquadShifts consists of four new test sets for the Stanford Question Answering \
-Dataset (SQuAD) from four different domains: Wikipedia articles, New York \
-Times articles, Reddit comments, and Amazon product reviews. Each dataset \
-was generated using the same data generating pipeline, Amazon Mechanical \
-Turk interface, and data cleaning code as the original SQuAD v1.1 dataset. \
-The "new-wikipedia" dataset measures overfitting on the original SQuAD v1.1 \
-dataset.  The "new-york-times", "reddit", and "amazon" datasets measure \
-robustness to natural distribution shifts. We encourage SQuAD model developers \
-to also evaluate their methods on these new datasets! \
-"""
-
-_LICENSE = "CC-BY-4.0"
-
-_CITATION = """\
-@InProceedings{pmlr-v119-miller20a,
-  title = {The Effect of Natural Distribution Shift on Question Answering Models},
-  author = {Miller, John and Krauth, Karl and Recht, Benjamin and Schmidt, Ludwig},
-  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
-  pages = {6905--6916},
-  year = {2020},
-  editor = {III, Hal Daumé and Singh, Aarti},
-  volume = {119},
-  series = {Proceedings of Machine Learning Research},
-  month = {13--18 Jul},
-  publisher = {PMLR},
-  pdf = {http://proceedings.mlr.press/v119/miller20a/miller20a.pdf},
-  url = {https://proceedings.mlr.press/v119/miller20a.html},
-}
-"""
-
-_URL = "https://raw.githubusercontent.com/modestyachts/squadshifts-website/master/datasets/"
-_URLS = {
-    "new_wiki": _URL + "new_wiki_v1.0.json",
-    "nyt": _URL + "nyt_v1.0.json",
-    "reddit": _URL + "reddit_v1.0.json",
-    "amazon": _URL + "amazon_reviews_v1.0.json",
-}
-
-
-class SquadShiftsConfig(datasets.BuilderConfig):
-    """BuilderConfig for SquadShifts."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for SQUAD.
-
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(SquadShiftsConfig, self).__init__(**kwargs)
-
-
-class SquadShifts(datasets.GeneratorBasedBuilder):
-    """SquadShifts consists of four new test sets for the SQUAD dataset."""
-
-    BUILDER_CONFIGS = [
-        SquadShiftsConfig(
-            name="new_wiki",
-            version=datasets.Version("1.0.0", ""),
-            description="SQuADShifts New Wikipedia article dataset",
-        ),
-        SquadShiftsConfig(
-            name="nyt",
-            version=datasets.Version("1.0.0", ""),
-            description="SQuADShifts New York Times article dataset.",
-        ),
-        SquadShiftsConfig(
-            name="reddit",
-            version=datasets.Version("1.0.0", ""),
-            description="SQuADShifts Reddit comment dataset.",
-        ),
-        SquadShiftsConfig(
-            name="amazon",
-            version=datasets.Version("1.0.0", ""),
-            description="SQuADShifts Amazon product review dataset.",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                        }
-                    ),
-                }
-            ),
-            homepage="https://modestyachts.github.io/squadshifts-website/index.html",
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        urls_to_download = _URLS
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        if self.config.name == "new_wiki" or self.config.name == "default":
-            return [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["new_wiki"]}
-                ),
-            ]
-        elif self.config.name == "nyt":
-            return [
-                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["nyt"]}),
-            ]
-        elif self.config.name == "reddit":
-            return [
-                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["reddit"]}),
-            ]
-        elif self.config.name == "amazon":
-            return [
-                datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["amazon"]}),
-            ]
-        else:
-            raise ValueError(f"SQuADShifts dataset name {self.config.name} not found!")
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepath)
-        with open(filepath, encoding="utf-8") as f:
-            squad = json.load(f)
-            for article in squad["data"]:
-                title = article.get("title", "").strip()
-                for paragraph in article["paragraphs"]:
-                    context = paragraph["context"].strip()
-                    for qa in paragraph["qas"]:
-                        question = qa["question"].strip()
-                        id_ = qa["id"]
-
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"].strip() for answer in qa["answers"]]
-
-                        # Features currently used are "context", "question", and "answers".
-                        # Others are extracted here for the ease of future expansions.
-                        yield id_, {
-                            "title": title,
-                            "context": context,
-                            "question": question,
-                            "id": id_,
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }