Datasets:

zhanghanchong
/

css

Tasks:

Text2Text Generation

Languages:

Chinese

Size Categories:

1K<n<10K

ArXiv:

License:

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css / css.py

zhanghanchong

Update css.py

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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"],
	}