Create test_data.py

test_data.py

@@ -0,0 +1,87 @@
+# 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.
+"""test_data dataset."""
+
+
+import csv
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+_DESCRIPTION = """"""
+
+_HOMEPAGE = "https://github.com/freeziyou/test_data"
+
+_LICENSE = "Creative Commons Attribution 4.0 International"
+
+_TRAIN_DOWNLOAD_URL = (
+    "https://raw.githubusercontent.com/freeziyou/test_data/main/train.csv"
+)
+_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/freeziyou/test_data/main/test.csv"
+
+
+class test_data(datasets.GeneratorBasedBuilder):
+    """test_data dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+                "label": datasets.features.ClassLabel(
+                    names=[
+					"none",
+					"like",
+					"unlike",
+					"hope",
+					"questioning",
+					"express_surprise",
+					"normal_interaction",
+					"express_sad",
+					"tease",
+					"meme",
+					"express_abashed"
+				]),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            task_templates=[TextClassification(text_column="text", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
+        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples as (key, example) tuples."""
+        with open(filepath, encoding="utf-8") as f:
+            csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True)
+            # call next to skip header
+            next(csv_reader)
+            for id_, row in enumerate(csv_reader):
+                text, label = row
+                yield id_, {"text": text, "label": label}