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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.
"""PAUQ: Text-to-SQL in Russian"""
import json
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{bakshandaeva-etal-2022-pauq,
title = "{PAUQ}: Text-to-{SQL} in {R}ussian",
author = "Bakshandaeva, Daria and
Somov, Oleg and
Dmitrieva, Ekaterina and
Davydova, Vera and
Tutubalina, Elena",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.175",
"""
_DESCRIPTION = """\
Pauq is a first Russian text-to-SQL dataset translated from original Spider dataset
with corrections and refinements of question, queries and databases.
"""
_LICENSE = "CC BY-SA 4.0"
_HOMEPAGE = "https://github.com/ai-spiderweb/pauq"
_URL = "https://huggingface.co/datasets/composite/pauq/resolve/main/formatted_pauq.zip"
class Pauq(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="ru_pauq_tl",
version=VERSION,
description=_DESCRIPTION,
),
datasets.BuilderConfig(
name="en_pauq_tl",
version=VERSION,
description=_DESCRIPTION,
),
datasets.BuilderConfig(
name="ru_pauq_iid",
version=VERSION,
description=_DESCRIPTION,
),
datasets.BuilderConfig(
name="en_pauq_iid",
version=VERSION,
description=_DESCRIPTION,
),
]
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"db_id": datasets.Value("string"),
"source": datasets.Value("string"),
"type": datasets.Value("string"),
"question": datasets.Value("string"),
"query": datasets.Value("string"),
"sql": datasets.features.Sequence(datasets.Value("string")),
"question_toks": datasets.features.Sequence(datasets.Value("string")),
"query_toks": datasets.features.Sequence(datasets.Value("string")),
"query_toks_no_value": datasets.features.Sequence(datasets.Value("string")),
"masked_query": 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, "splits/ru_iid_train.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_filepath": os.path.join(downloaded_filepath, "splits/ru_iid_test.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_filepath": os.path.join(downloaded_filepath, "splits/ru_tl_train.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_filepath": os.path.join(downloaded_filepath, "splits/ru_tl_est.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_filepath": os.path.join(downloaded_filepath, "splits/en_iid_train.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_filepath": os.path.join(downloaded_filepath, "splits/en_iid_test.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_filepath": os.path.join(downloaded_filepath, "splits/en_tl_train.json"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_filepath": os.path.join(downloaded_filepath, "splits/en_tl_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:
pauq = json.load(f)
for idx, sample in enumerate(pauq):
yield idx, {
"id": sample["id"],
"db_id": sample["db_id"],
"source": sample["source"],
"type": sample["type"],
"query": sample["query"],
"sql": datasets.Value("string"),
"question": sample["question"],
"question_toks": sample["question_toks"],
"query_toks": sample["query_toks"],
"query_toks_no_value": sample["query_toks_no_value"],
"masked_query": sample["masked_query"]
} |