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# 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 dataset"
_LICENSE = "CC BY-SA 4.0"
_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,
license=_LICENSE,
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"],
}
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