Create medical_qa_ru_data.py

medical_qa_ru_data.py

@@ -0,0 +1,69 @@
+# 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.
+"""Russian Q&A posts from a medical related forum"""
+
+
+import csv
+
+import datasets
+
+_DESCRIPTION = """\
+This dataset contains 190,335 Russian Q&A posts from a medical related forum.
+"""
+
+#_HOMEPAGE = "??"
+
+_LICENSE = ""
+
+
+_URL = "??"
+
+
+class MedicalQARuData(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        features = datasets.Features(
+            {
+                "date": datasets.Value("string"),
+                "categ": datasets.Value("string"),
+                "theme": datasets.Value("string"),
+                "desc": datasets.Value("string"),
+                "ans": datasets.Value("string"),
+                "spec10": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_file = dl_manager.download_and_extract(_URL)
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file})]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            data = csv.reader(f)
+            for id_, row in enumerate(data):
+                yield id_, {
+                    "date": row[0],
+                    "categ": row[1],
+                    "theme": row[2],
+                    "desc": row[3],
+                    "ans": row[4],
+                    "spec10": row[5],
+                }