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