Delete loading script

davis_pdp.py

@@ -1,129 +0,0 @@
-# coding=utf-8
-# Copyright 2020 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.
-"""The Winograd Schema Challenge Dataset"""
-
-import xml.etree.ElementTree as ET
-
-import datasets
-
-
-_DESCRIPTION = """\
-A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is
-resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its
-resolution. The schema takes its name from a well-known example by Terry Winograd:
-
-> The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.
-
-If the word is ``feared'', then ``they'' presumably refers to the city council; if it is ``advocated'' then ``they''
-presumably refers to the demonstrators.
-"""
-
-_CITATION = """\
-@inproceedings{levesque2012winograd,
-  title={The winograd schema challenge},
-  author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},
-  booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},
-  year={2012},
-  organization={Citeseer}
-}
-"""
-
-_HOMPAGE = "https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html"
-_DOWNLOAD_URL = "https://cs.nyu.edu/~davise/papers/WinogradSchemas/PDPChallenge2016.xml"
-
-
-class WinogradWSCConfig(datasets.BuilderConfig):
-    """BuilderConfig for WinogradWSC."""
-
-    def __init__(self, *args, language=None, inds=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.inds = set(inds) if inds is not None else None
-
-    def is_in_range(self, id):
-        """Takes an index and tells you if it belongs to the configuration's subset"""
-        return id in self.inds if self.inds is not None else True
-
-
-class WinogradWSC(datasets.GeneratorBasedBuilder):
-    """The Winograd Schema Challenge Dataset"""
-
-    BUILDER_CONFIG_CLASS = WinogradWSCConfig
-    BUILDER_CONFIGS = [
-        WinogradWSCConfig(
-            name="davis_pdp",
-            description="Full set of winograd examples",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                    "pronoun": datasets.Value("string"),
-                    "pronoun_loc": datasets.Value("int32"),
-                    "quote": datasets.Value("string"),
-                    "quote_loc": datasets.Value("int32"),
-                    "options": datasets.Sequence(datasets.Value("string")),
-                    "label": datasets.ClassLabel(num_classes=2),
-                    "source": datasets.Value("string"),
-                }
-            ),
-            homepage=_HOMPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        path = dl_manager.download_and_extract(_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": path}),
-        ]
-
-    def _cleanup_whitespace(self, text):
-        return " ".join(text.split())
-
-    def _generate_examples(self, filepath):
-        tree = ET.parse(filepath)
-        for id, schema in enumerate(tree.getroot()):
-            if not self.config.is_in_range(id):
-                continue
-
-            text_root = schema.find("text")
-            quote_root = schema.find("quote")
-
-            text_left = self._cleanup_whitespace(text_root.findtext("txt1", ""))
-            text_right = self._cleanup_whitespace(text_root.findtext("txt2", ""))
-            quote_left = self._cleanup_whitespace(quote_root.findtext("quote1", ""))
-            quote_right = self._cleanup_whitespace(quote_root.findtext("quote2", ""))
-            pronoun = self._cleanup_whitespace(text_root.findtext("pron"))
-
-            features = {}
-            features["text"] = " ".join([text_left, pronoun, text_right]).strip()
-            features["quote"] = " ".join([quote_left, pronoun, quote_right]).strip()
-
-            features["pronoun"] = pronoun
-            features["options"] = [
-                self._cleanup_whitespace(option.text) for option in schema.find("answers").findall("answer")
-            ]
-
-            answer_txt = self._cleanup_whitespace(schema.findtext("correctAnswer"))
-            features["label"] = int("B" in answer_txt)  # convert "  A. " or " B " strings to a 0/1 index
-
-            features["pronoun_loc"] = len(text_left) + 1 if len(text_left) > 0 else 0
-            features["quote_loc"] = features["pronoun_loc"] - (len(quote_left) + 1 if len(quote_left) > 0 else 0)
-            features["source"] = self._cleanup_whitespace(schema.findtext("source"))
-
-            yield id, features