Release: 1.18.1

Commit from https://github.com/huggingface/datasets/commit/218e496519ff14b4bc69ea559616af6f2ef89e57

journalists_questions.py

@@ -1,76 +1,76 @@
-# 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.
-
-
-import csv
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{hasanain2016questions,
-  title={What Questions Do Journalists Ask on Twitter?},
-  author={Hasanain, Maram and Bagdouri, Mossaab and Elsayed, Tamer and Oard, Douglas W},
-  booktitle={Tenth International AAAI Conference on Web and Social Media},
-  year={2016}
-}
-"""
-
-_DESCRIPTION = """\
-The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic
-tweets manually labeled for question identification over Arabic tweets posted by journalists.
-"""
-_DATA_URL = "https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U"
-
-
-class JournalistsQuestions(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Journalists tweet IDs and annotation by whether the tweet has a question",
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "tweet_id": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=["no", "yes"]),
-                    "label_confidence": datasets.Value("float"),
-                }
-            ),
-            homepage="http://qufaculty.qu.edu.qa/telsayed/datasets/",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(_DATA_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_dir}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        with open(filepath, encoding="utf-8") as f:
-            reader = csv.DictReader(f, delimiter="\t", fieldnames=["tweet_id", "label", "label_confidence"])
-            for idx, row in enumerate(reader):
-                yield idx, {
-                    "tweet_id": row["tweet_id"],
-                    "label": row["label"],
-                    "label_confidence": float(row["label_confidence"]),
-                }
+# 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.
+
+
+import csv
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{hasanain2016questions,
+  title={What Questions Do Journalists Ask on Twitter?},
+  author={Hasanain, Maram and Bagdouri, Mossaab and Elsayed, Tamer and Oard, Douglas W},
+  booktitle={Tenth International AAAI Conference on Web and Social Media},
+  year={2016}
+}
+"""
+
+_DESCRIPTION = """\
+The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic
+tweets manually labeled for question identification over Arabic tweets posted by journalists.
+"""
+_DATA_URL = "https://drive.google.com/uc?export=download&id=1CBrh-9OrSpKmPQBxTK_ji6mq6WTN_U9U"
+
+
+class JournalistsQuestions(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Journalists tweet IDs and annotation by whether the tweet has a question",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "tweet_id": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["no", "yes"]),
+                    "label_confidence": datasets.Value("float"),
+                }
+            ),
+            homepage="http://qufaculty.qu.edu.qa/telsayed/datasets/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_dir = dl_manager.download_and_extract(_DATA_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_dir}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t", fieldnames=["tweet_id", "label", "label_confidence"])
+            for idx, row in enumerate(reader):
+                yield idx, {
+                    "tweet_id": row["tweet_id"],
+                    "label": row["label"],
+                    "label_confidence": float(row["label_confidence"]),
+                }