Upload dpr_raw.py with huggingface_hub

dpr_raw.py

@@ -0,0 +1,105 @@
+# 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),
+                }