Datasets:
Tasks:
Text2Text Generation
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
File size: 3,952 Bytes
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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.
"""Spider: A Large-Scale Human-Labeled Dataset for Text-to-SQL Tasks"""
from __future__ import absolute_import, division, print_function
import json
import logging
import datasets
_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://drive.google.com/uc?export=download&id=1_AckYkinAnhqmRQtGsQgUKAnTHxxX5J0"
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": downloaded_filepath + "/spider/train_spider.json",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_filepath": downloaded_filepath + "/spider/dev.json",
},
),
]
def _generate_examples(self, data_filepath):
"""This function returns the examples in the raw (text) form."""
logging.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"],
}
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