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idk_mrc.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
+import os
+import re
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@misc{putri2022idk,
+    doi = {10.48550/ARXIV.2210.13778},
+    url = {https://arxiv.org/abs/2210.13778},
+    author = {Putri, Rifki Afina and Oh, Alice},
+    title = {IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension},
+    publisher = {arXiv},
+    year = {2022}
+}
+
+"""
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+
+_ALL_DATASETS = ["idk_mrc", "trans_squad", "tydiqa", "model_gen", "human_filt"]
+_DATASETNAME = _ALL_DATASETS[0]  # idk_mrc
+_BASELINES = _ALL_DATASETS[1:]   # trans_squad, tydiqa, model_gen, human_filt
+
+_DESCRIPTION = """\
+I(n)dontKnow-MRC (IDK-MRC) is an Indonesian Machine Reading Comprehension dataset that covers
+answerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA,
+the new unanswerable question in IDK-MRC is generated using a question generation model and human-written question.
+Each paragraph in the dataset has a set of answerable and unanswerable questions with the corresponding answer.
+
+Besides IDK-MRC (idk_mrc) dataset, several baseline datasets also provided:
+1. Trans SQuAD (trans_squad): machine translated SQuAD 2.0 (Muis and Purwarianti, 2020)
+2. TyDiQA (tydiqa): Indonesian answerable questions set from the TyDiQA-GoldP (Clark et al., 2020)
+3. Model Gen (model_gen): TyDiQA + the unanswerable questions output from the question generation model
+4. Human Filt (human_filt): Model Gen dataset that has been filtered by human annotator
+"""
+
+_HOMEPAGE = "https://github.com/rifkiaputri/IDK-MRC"
+
+_LICENSE = "CC-BY-SA 4.0"
+
+_URLS = {
+    _DATASETNAME: {
+        "test": "https://raw.githubusercontent.com/rifkiaputri/IDK-MRC/master/dataset/idk_mrc/test.json",
+        "train": "https://raw.githubusercontent.com/rifkiaputri/IDK-MRC/master/dataset/idk_mrc/train.json",
+        "validation": "https://raw.githubusercontent.com/rifkiaputri/IDK-MRC/master/dataset/idk_mrc/valid.json",
+    },
+    "baseline": {
+        "test": "https://raw.githubusercontent.com/rifkiaputri/IDK-MRC/master/dataset/baseline/{name}/test.json",
+        "train": "https://raw.githubusercontent.com/rifkiaputri/IDK-MRC/master/dataset/baseline/{name}/train.json",
+        "validation": "https://raw.githubusercontent.com/rifkiaputri/IDK-MRC/master/dataset/baseline/{name}/valid.json",
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_NUSANTARA_VERSION = "1.0.0"
+
+
+def nusantara_config_constructor(name, schema, version):
+    """
+    Construct NusantaraConfig with idk_mrc_{schema} format for the main dataset &
+    idk_mrc_baseline_{name}_{schema} format for the baseline datasets.
+    Suported dataset names: see _ALL_DATASETS
+    """
+    if schema != "source" and schema != "nusantara_qa":
+        raise ValueError(f"Invalid schema: {schema}")
+
+    if name not in _ALL_DATASETS:
+        raise ValueError(f"Invalid dataset name: {name}")
+
+    if name == "idk_mrc":
+        return NusantaraConfig(
+            name="idk_mrc_{schema}".format(schema=schema),
+            version=datasets.Version(version),
+            description="IDK-MRC with {schema} schema".format(schema=schema),
+            schema=schema,
+            subset_id="idk_mrc",
+        )
+    else:
+        return NusantaraConfig(
+            name="idk_mrc_baseline_{name}_{schema}".format(name=name, schema=schema),
+            version=datasets.Version(version),
+            description="IDK-MRC baseline ({name}) with {schema} schema".format(name=name, schema=schema),
+            schema=schema,
+            subset_id="idk_mrc",
+        )
+
+
+class IdkMrc(datasets.GeneratorBasedBuilder):
+    """IDK-MRC is an Indonesian MRC dataset that covers answerable and unanswerable questions"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        nusantara_config_constructor(name, schema, version)
+        for name in _ALL_DATASETS for schema, version in zip(["source", "nusantara_qa"], [_SOURCE_VERSION, _NUSANTARA_VERSION])
+    ]
+
+    DEFAULT_CONFIG_NAME = "idk_mrc_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "context": datasets.Value("string"),
+                    "qas": [
+                        {
+                            "id": datasets.Value("string"),
+                            "is_impossible": datasets.Value("bool"),
+                            "question": datasets.Value("string"),
+                            "answers": [
+                                {
+                                    "text": datasets.Value("string"),
+                                    "answer_start": datasets.Value("int64")
+                                }
+                            ]
+                        }
+                    ],
+                }
+            )
+
+        elif self.config.schema == "nusantara_qa":
+            features = schemas.qa_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."""
+        if self.config.name == "idk_mrc_source" or self.config.name == "idk_mrc_nusantara_qa":
+            data_name = "idk_mrc"
+            train_data_path = dl_manager.download_and_extract(_URLS[_DATASETNAME]["train"])
+            validation_data_path = dl_manager.download_and_extract(_URLS[_DATASETNAME]["validation"])
+            test_data_path = dl_manager.download_and_extract(_URLS[_DATASETNAME]["test"])
+        else:
+            try:
+                data_name = re.search("baseline_(.+?)_(source|nusantara_qa)", self.config.name).group(1)
+            except AttributeError:
+                raise ValueError(f"Invalid config name: {self.config.name}")
+
+            if data_name not in _BASELINES:
+                raise ValueError(f"Invalid baseline dataset name: {data_name}")
+
+            train_data_path = dl_manager.download_and_extract(_URLS["baseline"]["train"].format(name=data_name))
+            validation_data_path = dl_manager.download_and_extract(_URLS["baseline"]["validation"].format(name=data_name))
+            test_data_path = dl_manager.download_and_extract(_URLS["baseline"]["test"].format(name=data_name)) if data_name != "trans_squad" else ""
+
+        data_split = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_data_path,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(validation_data_path),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(test_data_path),
+                },
+            ),
+        ]
+
+        if data_name == "trans_squad":
+            # trans_squad doesn't have test split
+            return data_split[:2]
+
+        return data_split
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        with open(filepath) as json_file:
+            examples = json.load(json_file)
+
+        if self.config.schema == "source":
+            # Dataset doesn't have predefined context ID, use python enumeration.
+            # The examples in the source schema are grouped by paragraph context;
+            # each context can have multiple questions.
+            for key, example in enumerate(examples):
+                yield key, example
+
+        elif self.config.schema == "nusantara_qa":
+            for key, example in enumerate(examples):
+                for qa in example["qas"]:
+                    # Use question ID as key
+                    yield str(qa["id"]), {
+                        "id": qa["id"],
+                        "question_id": qa["id"],
+                        "document_id": str(key),
+                        "question": qa["question"],
+                        "type": "extractive",
+                        "choices": [],
+                        "context": example["context"],
+                        "answer": [ans["text"] for ans in qa["answers"]],
+                    }