spider_VALUE.py

# 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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""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"],
                }