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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"""
import json
import os
import textwrap
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/SALT-NLP/spider_VALUE/resolve/main/data.zip"
class SpiderConfig(datasets.BuilderConfig):
"""BuilderConfig for Spider."""
def __init__(
self,
name,
description,
train_path,
dev_path,
**kwargs
):
super(SpiderConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.name = name
self.description = description
self.train_path = train_path
self.dev_path = dev_path
class Spider(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
SpiderConfig(
name="AppE",
description=textwrap.dedent(
"""\
An Appalachian English variant of a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students"""
),
train_path="train_spider_AppE.json",
dev_path="dev_AppE.json",
),
SpiderConfig(
name="ChcE",
description=textwrap.dedent(
"""\
A Chicano English variant of a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students"""
),
train_path="train_spider_ChcE.json",
dev_path="dev_ChcE.json",
),
SpiderConfig(
name="CollSgE",
description=textwrap.dedent(
"""\
A Singapore English (Singlish) variant of a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students"""
),
train_path="train_spider_CollSgE.json",
dev_path="dev_CollSgE.json",
),
SpiderConfig(
name="IndE",
description=textwrap.dedent(
"""\
An Indian English variant of a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students"""
),
train_path="train_spider_IndE.json",
dev_path="dev_IndE.json",
),
SpiderConfig(
name="UAAVE",
description=textwrap.dedent(
"""\
An Urban African American English variant of a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students"""
),
train_path="train_spider_UAAVE.json",
dev_path="dev_UAAVE.json",
),
SpiderConfig(
name="MULTI",
description=textwrap.dedent(
"""\
A mixed-dialectal variant of a large-scale complex and cross-domain semantic parsing and text-to-SQL dataset annotated by 11 college students"""
),
train_path="train_spider_MULTI.json",
dev_path="dev_MULTI.json",
),
]
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)
downloaded_filepath = os.path.join(downloaded_filepath, "data")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_filepath": os.path.join(downloaded_filepath, self.config.train_path),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_filepath": os.path.join(downloaded_filepath, self.config.dev_path),
},
)
]
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"],
} |