Upload css.py

css.py

@@ -0,0 +1,133 @@
+# 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.
+"""CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset"""
+
+
+import json
+import os
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+"""
+
+_DESCRIPTION = "CSS is a large-scale cross-schema Chinese text-to-SQL datasets"
+
+_URL = "https://huggingface.co/datasets/zhanghanchong/css/resolve/main/css.zip"
+
+
+class CSS(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="css",
+            version=VERSION,
+            description="CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset",
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "query": datasets.Value("string"),
+                "db_id": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "question_id": datasets.Value("string")
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_filepath = dl_manager.download_and_extract(_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit("example/train"),
+                gen_kwargs={
+                    "data_filepath": os.path.join(downloaded_filepath, "css/example/train.json"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit("example/dev"),
+                gen_kwargs={
+                    "data_filepath": os.path.join(downloaded_filepath, "css/example/dev.json"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit("example/test"),
+                gen_kwargs={
+                    "data_filepath": os.path.join(downloaded_filepath, "css/example/test.json"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit("template/train"),
+                gen_kwargs={
+                    "data_filepath": os.path.join(downloaded_filepath, "css/template/train.json"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit("template/dev"),
+                gen_kwargs={
+                    "data_filepath": os.path.join(downloaded_filepath, "css/template/dev.json"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit("template/test"),
+                gen_kwargs={
+                    "data_filepath": os.path.join(downloaded_filepath, "css/template/test.json"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit("schema/train"),
+                gen_kwargs={
+                    "data_filepath": os.path.join(downloaded_filepath, "css/schema/train.json"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit("schema/dev"),
+                gen_kwargs={
+                    "data_filepath": os.path.join(downloaded_filepath, "css/schema/dev.json"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit("schema/test"),
+                gen_kwargs={
+                    "data_filepath": os.path.join(downloaded_filepath, "css/schema/test.json"),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", data_filepath)
+        with open(data_filepath, encoding="utf-8") as f:
+            css = json.load(f)
+            for idx, sample in enumerate(css):
+                yield idx, {
+                    "query": sample["query"],
+                    "db_id": sample["db_id"],
+                    "question": sample["question"],
+                    "question_id": sample["question_id"],
+                }