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vitext2sql.py

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+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":
+            # The sql column is an unstructured JSON,
+            # in the meantime just treat it as large string.
+            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