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vimmrc.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.configs import SEACrowdConfig
+from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA,
+                                      Licenses, Tasks)
+
+_CITATION = """
+@ARTICLE{vimmrc,
+    author={Nguyen, Kiet Van and Tran, Khiem Vinh and Luu, Son T. and Nguyen, Anh Gia-Tuan and Nguyen, Ngan Luu-Thuy},
+    journal={IEEE Access},
+    title={Enhancing Lexical-Based Approach With External Knowledge for Vietnamese Multiple-Choice Machine Reading Comprehension},
+    year={2020},
+    volume={8},
+    pages={201404-201417},
+    doi={10.1109/ACCESS.2020.3035701}}
+"""
+
+_DATASETNAME = "vimmrc"
+
+_DESCRIPTION = """
+ViMMRC, a challenging machine comprehension corpus with multiple-choice questions,
+intended for research on the machine comprehension of Vietnamese text. This corpus
+includes 2,783 multiple-choice questions and answers based on a set of 417 Vietnamese
+texts used for teaching reading comprehension for 1st to 5th graders.
+"""
+
+_HOMEPAGE = "https://sites.google.com/uit.edu.vn/kietnv/datasets#h.1qeaynfs79d1"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = f"{Licenses.UNKNOWN.value} | The corpus is freely available at our website for research purposes."
+
+_LOCAL = False
+
+_URL = "https://drive.google.com/file/d/14Rq-YANUv8qyi4Ze8ReEAEu_uxgcV_Yk/view"  # ~2mb
+
+_SUPPORTED_TASKS = [Tasks.COMMONSENSE_REASONING]
+_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}"  # qa
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class ViMMRCDataset(datasets.GeneratorBasedBuilder):
+    """A Vietnamese machine comprehension corpus with multiple-choice questions"""
+
+    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=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{_SEACROWD_SCHEMA}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=_SEACROWD_SCHEMA,
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "file_path": datasets.Value("string"),
+                    "article": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "choices": datasets.Sequence(datasets.Value("string")),
+                    "answer": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == _SEACROWD_SCHEMA:
+            features = SCHEMA_TO_FEATURES[TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]]  # 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."""
+        # check if gdown is installed
+        try:
+            import gdown
+        except ImportError as err:
+            raise ImportError("Please install `gdown` to enable reliable data download from google drive.") from err
+
+        # download data from gdrive
+        output_dir = Path.cwd() / "data" / "vimmrc"
+        output_dir.mkdir(parents=True, exist_ok=True)
+        output_file = output_dir / "vimmrc.zip"
+        if not output_file.exists():
+            gdown.download(_URL, str(output_file), fuzzy=True)
+        else:
+            print(f"File already downloaded: {str(output_file)}")
+
+        # extract data
+        data_dir = Path(dl_manager.extract(output_file)) / "ViMMRC"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": data_dir / "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data_dir": data_dir / "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_dir": data_dir / "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        # a data_dir consists of several json files
+        json_files = sorted(list(data_dir.glob("*.json")))
+
+        key = 0
+        for json_file in json_files:
+            with open(json_file, "r", encoding="utf-8") as file:
+                # load per json file
+                data = json.load(file)
+                assert len(data["questions"]) == len(data["options"]) == len(data["answers"]), f"Mismatched data length on {str(json_file)}"
+
+                for idx, question in enumerate(data["questions"]):
+
+                    # get answer based on the answer key
+                    if data["answers"][idx] == "A":
+                        answer = data["options"][idx][0]
+                    elif data["answers"][idx] == "B":
+                        answer = data["options"][idx][1]
+                    elif data["answers"][idx] == "C":
+                        answer = data["options"][idx][2]
+                    elif data["answers"][idx] == "D":
+                        answer = data["options"][idx][3]
+
+                    if self.config.schema == "source":
+                        yield key, {
+                            "file_path": str(json_file),
+                            "article": data["article"],
+                            "question": question,
+                            "choices": data["options"][idx],
+                            "answer": answer,
+                        }
+                        key += 1
+
+                    elif self.config.schema == _SEACROWD_SCHEMA:
+                        yield key, {
+                            "id": key,
+                            "question_id": None,
+                            "document_id": str(json_file),
+                            "question": question,
+                            "type": "multiple_choice",
+                            "choices": data["options"][idx],
+                            "context": data["article"],
+                            "answer": [answer],
+                            "meta": None,
+                        }
+                        key += 1