Upload social_bias_frames.py

social_bias_frames.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
+"""Social Bias Frames"""
+
+
+import csv
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{sap2020socialbiasframes,
+   title={Social Bias Frames: Reasoning about Social and Power Implications of Language},
+   author={Sap, Maarten and Gabriel, Saadia and Qin, Lianhui and Jurafsky, Dan and Smith, Noah A and Choi, Yejin},
+   year={2020},
+   booktitle={ACL},
+}
+"""
+
+_DESCRIPTION = """\
+Social Bias Frames is a new way of representing the biases and offensiveness that are implied in language.
+For example, these frames are meant to distill the implication that "women (candidates) are less qualified"
+behind the statement "we shouldn’t lower our standards to hire more women."
+"""
+
+_DATA_URL = "https://homes.cs.washington.edu/~msap/social-bias-frames/SBIC.v2.tgz"
+
+
+class SocialBiasFrames(datasets.GeneratorBasedBuilder):
+    """TSocial Bias Frame"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "whoTarget": datasets.Value("string"),
+                    "intentYN": datasets.Value("string"),
+                    "sexYN": datasets.Value("string"),
+                    "sexReason": datasets.Value("string"),
+                    "offensiveYN": datasets.Value("string"),
+                    "annotatorGender": datasets.Value("string"),
+                    "annotatorMinority": datasets.Value("string"),
+                    "sexPhrase": datasets.Value("string"),
+                    "speakerMinorityYN": datasets.Value("string"),
+                    "WorkerId": datasets.Value("string"),
+                    "HITId": datasets.Value("string"),
+                    "annotatorPolitics": datasets.Value("string"),
+                    "annotatorRace": datasets.Value("string"),
+                    "annotatorAge": datasets.Value("string"),
+                    "post": datasets.Value("string"),
+                    "targetMinority": datasets.Value("string"),
+                    "targetCategory": datasets.Value("string"),
+                    "targetStereotype": datasets.Value("string"),
+                    "dataSource": datasets.Value("string"),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both premise
+            # and hypothesis as input).
+            supervised_keys=None,
+            homepage="https://homes.cs.washington.edu/~msap/social-bias-frames/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive = dl_manager.download(_DATA_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": "SBIC.v2.tst.csv", "files": dl_manager.iter_archive(archive)},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": "SBIC.v2.dev.csv", "files": dl_manager.iter_archive(archive)},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": "SBIC.v2.trn.csv", "files": dl_manager.iter_archive(archive)},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, files):
+        """This function returns the examples in the raw (text) form."""
+        for path, f in files:
+            if path == filepath:
+                lines = (line.decode("utf-8") for line in f)
+                reader = csv.DictReader(lines)
+                for idx, row in enumerate(reader):
+                    yield idx, row
+                break