labels.py

labels.py

@@ -0,0 +1,98 @@
+# 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.
+"""Yahoo! Answers Topic Classification Dataset"""
+
+
+import csv
+import os
+
+import datasets
+
+
+_DESCRIPTION = """
+Yahoo! Answers Topic Classification is text classification dataset. \
+The dataset is the Yahoo! Answers corpus as of 10/25/2007. \
+The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. \
+From all the answers and other meta-information, this dataset only used the best answer content and the main category information.
+"""
+
+_URL = "https://drive.google.com/uc?export=download&id=19SSlf8ohOAxWVda-cFFPZA1e_FZ4TJl9"
+
+_TOPICS = [
+    "Society & Culture",
+    "Science & Mathematics",
+    "Health",
+    "Education & Reference",
+    "Computers & Internet",
+    "Sports",
+    "Business & Finance",
+    "Entertainment & Music",
+    "Family & Relationships",
+    "Politics & Government",
+]
+
+
+class YahooAnswersTopics(datasets.GeneratorBasedBuilder):
+    "Yahoo! Answers Topic Classification Dataset"
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="yahoo_answers_topics",
+            version=datasets.Version("1.0.0", ""),
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "topic": datasets.features.ClassLabel(names=_TOPICS),
+                    "question_title": datasets.Value("string"),
+                    "question_content": datasets.Value("string"),
+                    "best_answer": datasets.Value("string"),
+                },
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset",
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URL)
+
+        # Extracting (un-taring) the training data
+        data_dir = os.path.join(data_dir, "yahoo_answers_csv")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "train.csv")}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "test.csv")}
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            rows = csv.reader(f)
+            for i, row in enumerate(rows):
+                yield i, {
+                    "id": i,
+                    "topic": int(row[0]) - 1,
+                    "question_title": row[1],
+                    "question_content": row[2],
+                    "best_answer": row[3],
+                }