Remove deprecated tasks

fdRE.py

@@ -1,106 +1,104 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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 os
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_CITATION = """\
-        @author tianjie
-        fdRE
-        Chinese
-}
-"""
-
-_DESCRIPTION = """\
-     fdRE是一个中文的轴承故障诊断领域的关系抽取数据集
-     该数据集主要包含正向从属、反向从属以及无关三类标签
-"""
-
-_URL = "https://huggingface.co/datasets/leonadase/fdRE/resolve/main/fdRE.zip"
-
-
-class SemEval2010Task8(datasets.GeneratorBasedBuilder):
-    """The SemEval-2010 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals.
-    The task was designed to compare different approaches to semantic relation classification
-    and to provide a standard testbed for future research."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "relation": datasets.ClassLabel(
-                        names=[
-                            "Part_Of(E1,E2)",
-                            "Part_Of(E2,E1)",
-                            "Other",
-                        ]
-                    ),
-                }
-            ),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=datasets.info.SupervisedKeysData(input="sentence", output="relation"),
-            # Homepage of the dataset for documentation
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="sentence", label_column="relation")],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # dl_manager is a datasets.download.DownloadManager that can be used to
-        # download and extract URLs
-        dl_dir = dl_manager.download_and_extract(_URL)
-        # data_dir = os.path.join(dl_dir, "fdRE")
-        data_dir = dl_dir
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "train.txt"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "test.txt"),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as file:
-            lines = file.readlines()
-            num_lines_per_sample = 4
-
-            for i in range(0, len(lines), num_lines_per_sample):
-                idx = int(lines[i].split("\t")[0])
-                sentence = lines[i].split("\t")[1][1:-2]  # remove " at the start and "\n at the end
-                relation = lines[i + 1][:-1]  # remove \n at the end
-                yield idx, {
-                    "sentence": sentence,
-                    "relation": relation,
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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 os
+
+import datasets
+
+
+_CITATION = """\
+        @author tianjie
+        fdRE
+        Chinese
+}
+"""
+
+_DESCRIPTION = """\
+     fdRE是一个中文的轴承故障诊断领域的关系抽取数据集
+     该数据集主要包含正向从属、反向从属以及无关三类标签
+"""
+
+_URL = "https://huggingface.co/datasets/leonadase/fdRE/resolve/main/fdRE.zip"
+
+
+class SemEval2010Task8(datasets.GeneratorBasedBuilder):
+    """The SemEval-2010 Task 8 focuses on Multi-way classification of semantic relations between pairs of nominals.
+    The task was designed to compare different approaches to semantic relation classification
+    and to provide a standard testbed for future research."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "relation": datasets.ClassLabel(
+                        names=[
+                            "Part_Of(E1,E2)",
+                            "Part_Of(E2,E1)",
+                            "Other",
+                        ]
+                    ),
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=datasets.info.SupervisedKeysData(input="sentence", output="relation"),
+            # Homepage of the dataset for documentation
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        dl_dir = dl_manager.download_and_extract(_URL)
+        # data_dir = os.path.join(dl_dir, "fdRE")
+        data_dir = dl_dir
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.txt"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "test.txt"),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as file:
+            lines = file.readlines()
+            num_lines_per_sample = 4
+
+            for i in range(0, len(lines), num_lines_per_sample):
+                idx = int(lines[i].split("\t")[0])
+                sentence = lines[i].split("\t")[1][1:-2]  # remove " at the start and "\n at the end
+                relation = lines[i + 1][:-1]  # remove \n at the end
+                yield idx, {
+                    "sentence": sentence,
+                    "relation": relation,
                 }