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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 Licenses, Tasks |
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_CITATION = """\ |
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@inproceedings{nguyen2020vitext2sql, |
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title = {{A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese}}, |
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author = {Anh Tuan Nguyen and Mai Hoang Dao and Dat Quoc Nguyen}, |
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booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020}, |
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year = {2020}, |
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pages = {4079--4085} |
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} |
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""" |
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_DATASETNAME = "vitext2sql" |
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_DESCRIPTION = """\ |
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This is the first public large-scale Text-to-SQL semantic parsing dataset for Vietnamese. |
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The dataset is created by manually translating the Spider dataset into Vietnamese. |
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""" |
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_HOMEPAGE = "https://github.com/VinAIResearch/ViText2SQL" |
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_LICENSE = f"""{Licenses.OTHERS.value} | |
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By downloading the ViText2SQL dataset, USER agrees: |
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1. to use ViText2SQL for research or educational purposes only. |
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2. to not distribute ViText2SQL or part of ViText2SQL in any original or modified form. |
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3. and to cite our EMNLP-2020 Findings paper above whenever ViText2SQL is employed to help produce published results. |
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Copyright (c) 2020 VinAI Research |
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THE DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
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OUT OF OR IN CONNECTION WITH THE DATA OR THE USE OR OTHER DEALINGS IN THE |
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DATA. |
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""" |
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_SOURCE_VERSION = "1.0.0" |
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_URLS = { |
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"word-level": { |
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"train": "https://raw.githubusercontent.com/VinAIResearch/ViText2SQL/master/data/word-level/train.json", |
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"test": "https://raw.githubusercontent.com/VinAIResearch/ViText2SQL/master/data/word-level/test.json", |
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"validation": "https://raw.githubusercontent.com/VinAIResearch/ViText2SQL/master/data/word-level/dev.json", |
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}, |
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"syllable-level": { |
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"train": "https://raw.githubusercontent.com/VinAIResearch/ViText2SQL/master/data/syllable-level/train.json", |
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"test": "https://raw.githubusercontent.com/VinAIResearch/ViText2SQL/master/data/syllable-level/test.json", |
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"validation": "https://raw.githubusercontent.com/VinAIResearch/ViText2SQL/master/data/syllable-level/dev.json", |
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}, |
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} |
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_LOCAL = False |
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_LANGUAGES = ["vie"] |
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_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION] |
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_SEACROWD_VERSION = "2024.06.20" |
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class ViText2SQLDataset(datasets.GeneratorBasedBuilder): |
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"""Vitext2sql dataset is a Text-to-SQL semantic parsing dataset for Vietnamese.""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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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="Vitext2sql word level source schema", |
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schema="source", |
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subset_id="vitext2sql", |
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), |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_source_syllable", |
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version=SOURCE_VERSION, |
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description="Vitext2sql syllable level source schema", |
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schema="source", |
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subset_id="vitext2sql", |
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), |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_seacrowd_t2t", |
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version=SEACROWD_VERSION, |
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description="Vitext2sql SEACrowd schema for word-level", |
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schema="seacrowd_t2t", |
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subset_id="vitext2sql", |
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), |
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SEACrowdConfig( |
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name=f"{_DATASETNAME}_seacrowd_syllable_t2t", |
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version=SEACROWD_VERSION, |
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description="Vitext2sql SEACrowd schema for syllable-level", |
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schema="seacrowd_t2t", |
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subset_id="vitext2sql", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "vitext2sql_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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"query_toks": [datasets.Value("string")], |
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"query_toks_no_value": [datasets.Value("string")], |
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"question": datasets.Value("string"), |
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"question_toks": [datasets.Value("string")], |
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"sql": datasets.Value("large_string"), |
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} |
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) |
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elif self.config.schema == "seacrowd_t2t": |
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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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if "syllable" in self.config.name: |
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level_urls = _URLS["syllable-level"] |
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else: |
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level_urls = _URLS["word-level"] |
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data_files = dl_manager.download_and_extract(level_urls) |
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split_generators = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": data_files["test"], |
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}, |
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), |
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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_files["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_files["validation"], |
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}, |
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), |
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] |
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return split_generators |
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: |
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df = pd.read_json(filepath) |
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if self.config.schema == "source": |
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for i, row in df.iterrows(): |
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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"])} |
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yield i, entry |
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elif self.config.schema == "seacrowd_t2t": |
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for i, row in df.iterrows(): |
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entry = { |
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"id": str(i), |
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"text_1": row["question"], |
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"text_2": row["query"], |
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"text_1_name": "question", |
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"text_2_name": "sql_query", |
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} |
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yield i, entry |
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