Closes #612 | Add Dataloader AC-IQuAD

seacrowd/sea_datasets/ac_iquad/ac_iquad.py

@@ -0,0 +1,236 @@
+# 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.
+
+"""
+This is an automatically-produced question answering dataset \
+    generated from Indonesian Wikipedia articles. Each entry \
+    in the dataset consists of a context paragraph, the \
+    question and answer, and the question's equivalent SPARQL \
+    query. Questions are separated into two subsets: simple \
+    (question consists of a single SPARQL triple pattern) and \
+    complex (question consists of two triples plus an optional \
+    typing triple).
+"""
+import json
+import os
+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 = """\
+@article{afa5bf8149d6406786539c1ea827087d,
+    title = "AC-IQuAD: Automatically Constructed Indonesian Question Answering Dataset by Leveraging Wikidata",
+    abstract = "Constructing a question-answering dataset can be prohibitively expensive, making it difficult for researchers
+    to make one for an under-resourced language, such as Indonesian. We create a novel Indonesian Question Answering dataset
+    that is produced automatically end-to-end. The process uses Context Free Grammar, the Wikipedia Indonesian Corpus, and
+    the concept of the proxy model. The dataset consists of 134 thousand simple questions and 60 thousand complex questions.
+    It achieved competitive grammatical and model accuracy compared to the translated dataset but suffers from some issues
+    due to resource constraints.",
+    keywords = "Automatic dataset construction, Question answering dataset, Under-resourced Language",
+    author = "Kerenza Doxolodeo and Krisnadhi, {Adila Alfa}",
+    note = "Publisher Copyright: {\textcopyright} 2024, The Author(s).",
+    year = "2024",
+    doi = "10.1007/s10579-023-09702-y",
+    language = "English",
+    journal = "Language Resources and Evaluation",
+    issn = "1574-020X",
+    publisher = "Springer Netherlands",
+}
+"""
+
+_DATASETNAME = "ac_iquad"
+
+_DESCRIPTION = """
+This is an automatically-produced question answering dataset \
+    generated from Indonesian Wikipedia articles. Each entry \
+    in the dataset consists of a context paragraph, the \
+    question and answer, and the question's equivalent SPARQL \
+    query. Questions are separated into two subsets: simple \
+    (question consists of a single SPARQL triple pattern) and \
+    complex (question consists of two triples plus an optional \
+    typing triple).
+"""
+
+_HOMEPAGE = "https://www.kaggle.com/datasets/realdeo/indonesian-qa-generated-by-kg"
+
+_LANGUAGES = ["ind"]
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://github.com/muhammadravi251001/ac-iquad/raw/main/data/ac_iquad.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "1.0.0"
+
+
+class ACIQuADDataset(datasets.GeneratorBasedBuilder):
+    """
+    This is an automatically-produced question answering dataset \
+    generated from Indonesian Wikipedia articles. Each entry \
+    in the dataset consists of a context paragraph, the \
+    question and answer, and the question's equivalent SPARQL \
+    query. Questions are separated into two subsets: simple \
+    (question consists of a single SPARQL triple pattern) and \
+    complex (question consists of two triples plus an optional \
+    typing triple).
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SEACROWD_SCHEMA_NAME = "qa"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_simple_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_simple",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_simple_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}_simple",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_complex_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_complex",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_complex_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}_complex",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features_dict = {
+                "question": datasets.Value("string"),
+                "sparql": datasets.Value("string"),
+                "answer": datasets.Value("string"),
+                "context": datasets.Value("string"),
+                "answerline": datasets.Value("string"),
+            }
+
+            if self.config.subset_id.split("_")[2] == "complex":
+                features_dict["type"] = datasets.Value("string")
+
+            features = datasets.Features(features_dict)
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.qa_features
+
+            if self.config.subset_id.split("_")[2] == "complex":
+                features["meta"] = {"sparql": datasets.Value("string"), "answer_meta": datasets.Value("string"), "type": datasets.Value("string")}
+
+            else:
+                features["meta"] = {"sparql": datasets.Value("string"), "answer_meta": datasets.Value("string")}
+
+        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."""
+
+        subset = self.config.name.split("_")[2]
+        data_dir = dl_manager.download_and_extract(_URLS[_DATASETNAME])
+
+        if subset == "simple":
+            subset = "single"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, f"{subset}_train.json"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, f"{subset}_test.json"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        with open(filepath, "r", encoding="utf-8") as file:
+            data_json = json.load(file)
+
+        df = pd.json_normalize(data_json)
+
+        for index, row in df.iterrows():
+
+            if self.config.schema == "source":
+                example = row.to_dict()
+
+                if self.config.subset_id.split("_")[2] == "complex":
+                    example["type"] = example.pop("tipe", None)
+
+            elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+
+                subset = self.config.name.split("_")[2]
+                if subset == "simple":
+                    row["answerline"] = f"[{row['answerline']}]"
+
+                example = {
+                    "id": str(index),
+                    "question_id": "question_id",
+                    "document_id": "document_id",
+                    "question": row["question"],
+                    "type": "extractive",
+                    "choices": [],
+                    "context": row["context"],
+                    "answer": eval(row["answerline"]),
+                    "meta": {"sparql": row["sparql"], "answer_meta": row["answer"]},
+                }
+
+                if self.config.subset_id.split("_")[2] == "complex":
+                    example["meta"]["type"] = row["tipe"]
+
+            yield index, example