Delete loading script

definite_pronoun_resolution.py

@@ -1,105 +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
-"""The Definite Pronoun Resolution Dataset."""
-
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{rahman2012resolving,
-  title={Resolving complex cases of definite pronouns: the winograd schema challenge},
-  author={Rahman, Altaf and Ng, Vincent},
-  booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
-  pages={777--789},
-  year={2012},
-  organization={Association for Computational Linguistics}
-}"""
-
-_DESCRIPTION = """\
-Composed by 30 students from one of the author's undergraduate classes. These
-sentence pairs cover topics ranging from real events (e.g., Iran's plan to
-attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g.,
-Batman) and purely imaginary situations, largely reflecting the pop culture as
-perceived by the American kids born in the early 90s. Each annotated example
-spans four lines: the first line contains the sentence, the second line contains
-the target pronoun, the third line contains the two candidate antecedents, and
-the fourth line contains the correct antecedent. If the target pronoun appears
-more than once in the sentence, its first occurrence is the one to be resolved.
-"""
-
-
-_DATA_URL_PATTERN = "https://s3.amazonaws.com/datasets.huggingface.co/definite_pronoun_resolution/{}.c.txt"
-
-
-class DefinitePronounResolution(datasets.GeneratorBasedBuilder):
-    """The Definite Pronoun Resolution Dataset."""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text import of the Definite Pronoun Resolution Dataset.",  # pylint: disable=line-too-long
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "pronoun": datasets.Value("string"),
-                    "candidates": datasets.features.Sequence(datasets.Value("string"), length=2),
-                    "label": datasets.features.ClassLabel(num_classes=2),
-                }
-            ),
-            supervised_keys=("sentence", "label"),
-            homepage="http://www.hlt.utdallas.edu/~vince/data/emnlp12/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        files = dl_manager.download_and_extract(
-            {
-                "train": _DATA_URL_PATTERN.format("train"),
-                "test": _DATA_URL_PATTERN.format("test"),
-            }
-        )
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": files["test"]}),
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": files["train"]}),
-        ]
-
-    def _generate_examples(self, filepath):
-        with open(filepath, encoding="utf-8") as f:
-            line_num = -1
-            while True:
-                line_num += 1
-                sentence = f.readline().strip()
-                pronoun = f.readline().strip()
-                candidates = [c.strip() for c in f.readline().strip().split(",")]
-                correct = f.readline().strip()
-                f.readline()
-                if not sentence:
-                    break
-                yield line_num, {
-                    "sentence": sentence,
-                    "pronoun": pronoun,
-                    "candidates": candidates,
-                    "label": candidates.index(correct),
-                }