| 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 Licenses, Tasks |
|
|
| _CITATION = """\ |
| @inproceedings{nguyen2020vitext2sql, |
| title = {{A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese}}, |
| author = {Anh Tuan Nguyen and Mai Hoang Dao and Dat Quoc Nguyen}, |
| booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020}, |
| year = {2020}, |
| pages = {4079--4085} |
| } |
| """ |
|
|
| _DATASETNAME = "vitext2sql" |
|
|
| _DESCRIPTION = """\ |
| This is the first public large-scale Text-to-SQL semantic parsing dataset for Vietnamese. |
| The dataset is created by manually translating the Spider dataset into Vietnamese. |
| """ |
|
|
| _HOMEPAGE = "https://github.com/VinAIResearch/ViText2SQL" |
|
|
| _LICENSE = f"""{Licenses.OTHERS.value} | |
| By downloading the ViText2SQL dataset, USER agrees: |
| 1. to use ViText2SQL for research or educational purposes only. |
| 2. to not distribute ViText2SQL or part of ViText2SQL in any original or modified form. |
| 3. and to cite our EMNLP-2020 Findings paper above whenever ViText2SQL is employed to help produce published results. |
| Copyright (c) 2020 VinAI Research |
| THE DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| OUT OF OR IN CONNECTION WITH THE DATA OR THE USE OR OTHER DEALINGS IN THE |
| DATA. |
| """ |
|
|
| _SOURCE_VERSION = "1.0.0" |
|
|
| _URLS = { |
| "word-level": { |
| "train": "https://raw.githubusercontent.com/VinAIResearch/ViText2SQL/master/data/word-level/train.json", |
| "test": "https://raw.githubusercontent.com/VinAIResearch/ViText2SQL/master/data/word-level/test.json", |
| "validation": "https://raw.githubusercontent.com/VinAIResearch/ViText2SQL/master/data/word-level/dev.json", |
| }, |
| "syllable-level": { |
| "train": "https://raw.githubusercontent.com/VinAIResearch/ViText2SQL/master/data/syllable-level/train.json", |
| "test": "https://raw.githubusercontent.com/VinAIResearch/ViText2SQL/master/data/syllable-level/test.json", |
| "validation": "https://raw.githubusercontent.com/VinAIResearch/ViText2SQL/master/data/syllable-level/dev.json", |
| }, |
| } |
|
|
| _LOCAL = False |
| _LANGUAGES = ["vie"] |
| _SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] |
|
|
| _SEACROWD_VERSION = "2024.06.20" |
|
|
|
|
| class ViText2SQLDataset(datasets.GeneratorBasedBuilder): |
| """Vitext2sql dataset is a Text-to-SQL semantic parsing dataset for Vietnamese.""" |
|
|
| SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
| SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
|
|
| BUILDER_CONFIGS = [ |
| SEACrowdConfig( |
| name=f"{_DATASETNAME}_source", |
| version=SOURCE_VERSION, |
| description="Vitext2sql word level source schema", |
| schema="source", |
| subset_id="vitext2sql", |
| ), |
| SEACrowdConfig( |
| name=f"{_DATASETNAME}_source_syllable", |
| version=SOURCE_VERSION, |
| description="Vitext2sql syllable level source schema", |
| schema="source", |
| subset_id="vitext2sql", |
| ), |
| SEACrowdConfig( |
| name=f"{_DATASETNAME}_seacrowd_t2t", |
| version=SEACROWD_VERSION, |
| description="Vitext2sql SEACrowd schema for word-level", |
| schema="seacrowd_t2t", |
| subset_id="vitext2sql", |
| ), |
| SEACrowdConfig( |
| name=f"{_DATASETNAME}_seacrowd_syllable_t2t", |
| version=SEACROWD_VERSION, |
| description="Vitext2sql SEACrowd schema for syllable-level", |
| schema="seacrowd_t2t", |
| subset_id="vitext2sql", |
| ), |
| ] |
|
|
| DEFAULT_CONFIG_NAME = "vitext2sql_source" |
|
|
| def _info(self) -> datasets.DatasetInfo: |
| if self.config.schema == "source": |
| |
| |
| features = datasets.Features( |
| { |
| "db_id": datasets.Value("string"), |
| "query": datasets.Value("string"), |
| "query_toks": [datasets.Value("string")], |
| "query_toks_no_value": [datasets.Value("string")], |
| "question": datasets.Value("string"), |
| "question_toks": [datasets.Value("string")], |
| "sql": datasets.Value("large_string"), |
| } |
| ) |
| elif self.config.schema == "seacrowd_t2t": |
| 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]: |
| if "syllable" in self.config.name: |
| level_urls = _URLS["syllable-level"] |
| else: |
| level_urls = _URLS["word-level"] |
|
|
| data_files = dl_manager.download_and_extract(level_urls) |
| split_generators = [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepath": data_files["test"], |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": data_files["train"], |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "filepath": data_files["validation"], |
| }, |
| ), |
| ] |
|
|
| return split_generators |
|
|
| def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: |
| df = pd.read_json(filepath) |
| if self.config.schema == "source": |
| for i, row in df.iterrows(): |
| entry = {"db_id": row["db_id"], "query": row["query"], "query_toks": row["query_toks"], "query_toks_no_value": row["query_toks_no_value"], "question": row["question"], "question_toks": row["question_toks"], "sql": str(row["sql"])} |
| yield i, entry |
| elif self.config.schema == "seacrowd_t2t": |
| for i, row in df.iterrows(): |
| entry = { |
| "id": str(i), |
| "text_1": row["question"], |
| "text_2": row["query"], |
| "text_1_name": "question", |
| "text_2_name": "sql_query", |
| } |
| yield i, entry |
|
|
