Datasets:

xlangai
/

spider

	# 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.
	"""Spider: A Large-Scale Human-Labeled Dataset for Text-to-SQL Tasks"""


	import json
	import os

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@article{yu2018spider,
	title={Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task},
	author={Yu, Tao and Zhang, Rui and Yang, Kai and Yasunaga, Michihiro and Wang, Dongxu and Li, Zifan and Ma, James and Li, Irene and Yao, Qingning and Roman, Shanelle and others},
	journal={arXiv preprint arXiv:1809.08887},
	year={2018}
	}
	"""

	_DESCRIPTION = """\
	Spider is a large-scale complex and cross-domain semantic parsing and text-toSQL dataset annotated by 11 college students
	"""

	_HOMEPAGE = "https://yale-lily.github.io/spider"

	_LICENSE = "CC BY-SA 4.0"

	_URL = "https://huggingface.co/datasets/spider/resolve/main/data/spider.zip"


	class Spider(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="spider",
	version=VERSION,
	description="Spider: A Large-Scale Human-Labeled Dataset for Text-to-SQL Tasks",
	),
	]

	def _info(self):
	features = datasets.Features(
	{
	"db_id": datasets.Value("string"),
	"query": datasets.Value("string"),
	"question": datasets.Value("string"),
	"query_toks": datasets.features.Sequence(datasets.Value("string")),
	"query_toks_no_value": datasets.features.Sequence(datasets.Value("string")),
	"question_toks": datasets.features.Sequence(datasets.Value("string")),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	downloaded_filepath = dl_manager.download_and_extract(_URL)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"data_filepath": os.path.join(downloaded_filepath, "spider/train_spider.json"),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"data_filepath": os.path.join(downloaded_filepath, "spider/dev.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:
	spider = json.load(f)
	for idx, sample in enumerate(spider):
	yield idx, {
	"db_id": sample["db_id"],
	"query": sample["query"],
	"question": sample["question"],
	"query_toks": sample["query_toks"],
	"query_toks_no_value": sample["query_toks_no_value"],
	"question_toks": sample["question_toks"],
	}