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indoqa.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{IndoQA,
+  author = {{Jakarta Artificial Intelligence Research}}
+  title = {IndoQA: Building Indonesian QA dataset},
+  year = {2023}
+  url = {https://huggingface.co/datasets/jakartaresearch/indoqa}
+}
+"""
+
+_DATASETNAME = "indoqa"
+
+_DESCRIPTION = """\
+IndoQA is a monolingual question-answering dataset of Indonesian language (ind).
+It comprises 4,413 examples with 3:1 split of training and validation sets.
+The datasets consists of a context paragraph along with an associated question-answer pair.
+"""
+
+_HOMEPAGE = "https://jakartaresearch.com/"
+_LICENSE = Licenses.CC_BY_ND_4_0.value
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://drive.google.com/uc?id=1ND893H5x2gaPRRMJVajQ4hgqpopHoD0u",
+        "validation": "https://drive.google.com/uc?id=1mq_foV72riXb1KVBirJzTFZEe7oa8f4f",
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class IndoQADataset(datasets.GeneratorBasedBuilder):
+    """IndoQA: A monolingual Indonesian question-answering dataset comprises 4,413 instances of QA-pair with context."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    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_qa",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_qa",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "category": datasets.Value("string"),
+                    "span_start": datasets.Value("int32"),
+                    "span_end": datasets.Value("int32"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_qa":
+            features = schemas.qa_features
+            features["meta"]["span_start"] = datasets.Value("int32")
+            features["meta"]["span_end"] = datasets.Value("int32")
+
+        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."""
+        urls = _URLS[_DATASETNAME]
+        data_paths = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_paths["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_paths["validation"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        with open(filepath, "r", encoding="utf-8") as file:
+            datas = json.load(file)
+
+        if self.config.schema == "source":
+            for key, data in enumerate(datas):
+                yield key, data
+
+        elif self.config.schema == "seacrowd_qa":
+            for key, data in enumerate(datas):
+                yield key, {
+                    "id": f'{data["id"]}',
+                    "question_id": data["id"],
+                    "document_id": "",
+                    "question": data["question"],
+                    "type": data["category"],
+                    "choices": [],
+                    "context": data["context"],
+                    "answer": [data["answer"]],
+                    "meta": {
+                        "span_start": data["span_start"],
+                        "span_end": data["span_end"],
+                    },
+                }