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multispider.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+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
+
+
+# This template is based on the following template from the datasets package:
+# https://github.com/huggingface/datasets/blob/master/templates/new_dataset_script.py