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gap_raw.py

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+# 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
+"""GAP is a gender-balanced text data set."""
+
+
+import csv
+
+import datasets
+
+
+_CITATION = """
+@article{DBLP:journals/corr/abs-1810-05201,
+  author    = {Kellie Webster and
+               Marta Recasens and
+               Vera Axelrod and
+               Jason Baldridge},
+  title     = {Mind the {GAP:} {A} Balanced Corpus of Gendered Ambiguous Pronouns},
+  journal   = {CoRR},
+  volume    = {abs/1810.05201},
+  year      = {2018},
+  url       = {http://arxiv.org/abs/1810.05201},
+  archivePrefix = {arXiv},
+  eprint    = {1810.05201},
+  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
+  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1810-05201},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DESCRIPTION = """
+GAP is a gender-balanced dataset containing 8,908 coreference-labeled pairs of
+(ambiguous pronoun, antecedent name), sampled from Wikipedia and released by
+Google AI Language for the evaluation of coreference resolution in practical
+applications.
+"""
+
+_TRAINURL = "https://raw.githubusercontent.com/google-research-datasets/gap-coreference/master/gap-development.tsv"
+_VALIDATIONURL = "https://raw.githubusercontent.com/google-research-datasets/gap-coreference/master/gap-validation.tsv"
+_TESTURL = "https://raw.githubusercontent.com/google-research-datasets/gap-coreference/master/gap-test.tsv"
+
+
+class Gap(datasets.GeneratorBasedBuilder):
+    """GAP is a gender-balanced dataset.
+
+    It contains 8,908 coreference-labeled pairs
+    of (ambiguous pronoun, antecedent name), sampled from Wikipedia.
+    """
+
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "ID": datasets.Value("string"),
+                    "Text": datasets.Value("string"),
+                    "Pronoun": datasets.Value("string"),
+                    "Pronoun-offset": datasets.Value("int32"),
+                    "A": datasets.Value("string"),
+                    "A-offset": datasets.Value("int32"),
+                    "A-coref": datasets.Value("bool"),
+                    "B": datasets.Value("string"),
+                    "B-offset": datasets.Value("int32"),
+                    "B-coref": datasets.Value("bool"),
+                    "URL": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/google-research-datasets/gap-coreference",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        directory = dl_manager.download_and_extract(
+            {"train": _TRAINURL, "validation": _VALIDATIONURL, "test": _TESTURL}
+        )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": directory["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": directory["validation"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": directory["test"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as tsvfile:
+            reader = csv.DictReader(tsvfile, dialect="excel-tab")
+            for i, row in enumerate(reader):
+                row["A-coref"] = row["A-coref"] == "TRUE"
+                row["B-coref"] = row["B-coref"] == "TRUE"
+                row["A-offset"] = int(row["A-offset"])
+                row["B-offset"] = int(row["B-offset"])
+                row["Pronoun-offset"] = int(row["Pronoun-offset"])
+                yield i, row