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""" |
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MULTISPIDER, the largest multilingual text-to-SQL dataset which covers \ |
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seven languages (English, German, French, Spanish, Japanese, \ |
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Chinese, and Vietnamese). Upon MULTISPIDER, we further identify \ |
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the lexical and structural challenges of text-to-SQL (caused by \ |
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specific language properties and dialect sayings) and their \ |
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intensity across different languages. |
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""" |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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import pandas as pd |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Tasks, Licenses |
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_CITATION = """\ |
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@inproceedings{Dou2022MultiSpiderTB, |
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title={MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing}, |
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author={Longxu Dou and Yan Gao and Mingyang Pan and Dingzirui Wang and Wanxiang Che and Dechen Zhan and Jian-Guang Lou}, |
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booktitle={AAAI Conference on Artificial Intelligence}, |
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year={2023}, |
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url={https://ojs.aaai.org/index.php/AAAI/article/view/26499/26271} |
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} |
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""" |
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_DATASETNAME = "multispider" |
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_DESCRIPTION = """\ |
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MULTISPIDER, the largest multilingual text-to-SQL dataset which covers \ |
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seven languages (English, German, French, Spanish, Japanese, \ |
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Chinese, and Vietnamese). Upon MULTISPIDER, we further identify \ |
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the lexical and structural challenges of text-to-SQL (caused by \ |
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specific language properties and dialect sayings) and their \ |
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intensity across different languages. |
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""" |
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_HOMEPAGE = "https://github.com/longxudou/multispider" |
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_LANGUAGES = ["vie"] |
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_LICENSE = Licenses.CC_BY_4_0.value |
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_LOCAL = False |
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_URLS = { |
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"train": "https://huggingface.co/datasets/dreamerdeo/multispider/resolve/main/dataset/multispider/with_original_value/train_vi.json?download=true", |
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"dev": "https://huggingface.co/datasets/dreamerdeo/multispider/raw/main/dataset/multispider/with_original_value/dev_vi.json", |
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} |
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_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class MultispiderDataset(datasets.GeneratorBasedBuilder): |
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""" |
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MULTISPIDER, the largest multilingual text-to-SQL dataset which covers \ |
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seven languages (English, German, French, Spanish, Japanese, \ |
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Chinese, and Vietnamese). Upon MULTISPIDER, we further identify \ |
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the lexical and structural challenges of text-to-SQL (caused by \ |
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specific language properties and dialect sayings) and their \ |
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intensity across different languages. |
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""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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SEACROWD_SCHEMA_NAME = "t2t" |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_source", |
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version=SOURCE_VERSION, |
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description=f"{_DATASETNAME} source schema", |
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schema="source", |
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subset_id=f"{_DATASETNAME}", |
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), |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}", |
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version=SEACROWD_VERSION, |
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description=f"{_DATASETNAME} SEACrowd schema", |
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schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}", |
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subset_id=f"{_DATASETNAME}", |
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), |
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] |
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"db_id": datasets.Value("string"), |
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"query": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"query_toks": datasets.Sequence(feature=datasets.Value("string")), |
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"query_toks_no_value": datasets.Sequence(feature=datasets.Value("string")), |
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"question_toks": datasets.Sequence(feature=datasets.Value("string")), |
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"sql": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": |
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features = schemas.text2text_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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data_path_train = Path(dl_manager.download_and_extract(_URLS["train"])) |
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data_path_dev = Path(dl_manager.download_and_extract(_URLS["dev"])) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": data_path_train, |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": data_path_dev, |
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"split": "dev", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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df = pd.read_json(filepath) |
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for index, row in df.iterrows(): |
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if self.config.schema == "source": |
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example = row.to_dict() |
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elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": |
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example = { |
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"id": str(index), |
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"text_1": str(row["question"]), |
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"text_2": str(row["query"]), |
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"text_1_name": "question", |
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"text_2_name": "query", |
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} |
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yield index, example |
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