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qasina.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.
+
+
+import json
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@misc{rizqullah2023qasina,
+title={QASiNa: Religious Domain Question Answering using Sirah Nabawiyah},
+author={Muhammad Razif Rizqullah and Ayu Purwarianti and Alham Fikri Aji},
+year={2023},
+eprint={2310.08102},
+archivePrefix={arXiv},
+primaryClass={cs.CL}
+}
+"""
+
+_DATASETNAME = "qasina"
+
+_DESCRIPTION = """\
+Question Answering Sirah Nabawiyah Dataset (QASiNa) is Extractive \
+QA Dataset which build to perform QA task in Sirah Nabawiyah domain.
+"""
+
+_HOMEPAGE = "https://github.com/rizquuula/QASiNa"
+
+_LANGUAGES = ["ind"]
+
+_LICENSE = Licenses.MIT.value
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://github.com/rizquuula/QASiNa/raw/main/QASiNa.json",
+}
+
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+# TODO: Name the dataset class to match the script name using CamelCase instead of snake_case
+class QasinaDataset(datasets.GeneratorBasedBuilder):
+    """Question Answering Sirah Nabawiyah Dataset (QASiNa) is \
+    Extractive QA Dataset which build to perform QA task in Sirah Nabawiyah domain."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "qa"
+
+    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(
+                {
+                    "context_id": datasets.Value("int32"),
+                    "context": datasets.Value("string"),
+                    "question_answers": datasets.Sequence({"type": datasets.Value("string"), "question": datasets.Value("string"), "answer": datasets.Value("string"), "answer_start": datasets.Value("int32"), "question_id": datasets.Value("int32")}),
+                    "context_length": datasets.Value("int32"),
+                    "context_title": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.qa.features
+            features["meta"] = {"context_title": datasets.Value("string"), "answer_start": datasets.Value("int32"),"context_length": datasets.Value("int32"), "type": 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]:
+        urls = _URLS[_DATASETNAME]
+        filepath = dl_manager.download(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": filepath,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        with open(filepath) as file:
+            dataset = json.load(file)
+
+        if self.config.schema == "source":
+            for i, line in enumerate(dataset):
+                yield i, {
+                    "context_id": line["context_id"],
+                    "context": line["context"],
+                    "question_answers": [
+                        {
+                            "type": subline["type"],
+                            "question": subline["question"],
+                            "answer": subline["answer"],
+                            "answer_start": subline["answer_start"],
+                            "question_id": subline["question_id"],
+                        }
+                        for subline in line["question_answers"]
+                    ],
+                    "context_length": line["context_length"],
+                    "context_title": line["context_title"],
+                }
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            for line in dataset:
+                for question_answer in line["question_answers"]:
+                    id = question_answer["question_id"]
+
+                    yield id, {
+                        "id": id,
+                        "question_id": question_answer["question_id"],
+                        "document_id": line["context_id"],
+                        "question": question_answer["question"],
+                        "type": "extractive",
+                        "choices": [],
+                        "context": line["context"],
+                        "answer": [question_answer["answer"]],
+                        "meta": {
+                            "context_title": line["context_title"],
+                            "answer_start": question_answer["answer_start"],
+                            "context_length": line["context_length"],
+                            "type": question_answer["type"],
+                        },
+                    }