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vlogqa.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 = """\
+@inproceedings{ngo-etal-2024-vlogqa,
+    title = "{V}log{QA}: Task, Dataset, and Baseline Models for {V}ietnamese Spoken-Based Machine Reading Comprehension",
+    author = "Ngo, Thinh  and
+        Dang, Khoa  and
+        Luu, Son  and
+        Nguyen, Kiet  and
+        Nguyen, Ngan",
+    editor = "Graham, Yvette  and
+        Purver, Matthew",
+    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = mar,
+    year = "2024",
+    address = "St. Julian{'}s, Malta",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.eacl-long.79",
+    pages = "1310--1324",
+}
+"""
+
+_DATASETNAME = "vlogqa"
+
+_DESCRIPTION = """\
+VlogQA is a Vietnamese spoken language corpus for machine reading comprehension. It
+consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from
+YouTube videos around food and travel.
+"""
+
+_HOMEPAGE = "https://github.com/sonlam1102/vlogqa"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = f"""{Licenses.OTHERS.value} |
+The user of VlogQA developed by the NLP@UIT research group must respect the following
+terms and conditions:
+1. The dataset is only used for non-profit research for natural language processing and
+   education.
+2. The dataset is not allowed to be used in commercial systems.
+3. Do not redistribute the dataset. This dataset may be modified or improved to serve a
+   research purpose better, but the edited dataset may not be distributed.
+4. Summaries, analyses, and interpretations of the properties of the dataset may be
+   derived and published, provided it is not possible to reconstruct the information from
+   these summaries.
+5. Published research works that use the dataset must cite the following paper:
+   Thinh Ngo, Khoa Dang, Son Luu, Kiet Nguyen, and Ngan Nguyen. 2024. VlogQA: Task,
+   Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension.
+   In Proceedings of the 18th Conference of the European Chapter of the Association for
+   Computational Linguistics (Volume 1: Long Papers), pages 1310–1324, St. Julian’s,
+   Malta. Association for Computational Linguistics.
+"""
+
+_LOCAL = True  # need to signed a user agreement, see _HOMEPAGE
+
+_URLS = {}  # local dataset
+
+_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
+_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}"  # qa
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class VlogQADataset(datasets.GeneratorBasedBuilder):
+    """Vietnamese spoken language corpus around food and travel for machine reading comprehension"""
+
+    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(
+                {
+                    "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answers": datasets.Sequence(
+                        {
+                            "text": datasets.Value("string"),
+                            "answer_start": datasets.Value("int32"),
+                        }
+                    ),
+                }
+            )
+        elif self.config.schema == _SEACROWD_SCHEMA:
+            features = SCHEMA_TO_FEATURES[TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]]  # qa_features
+            features["meta"] = {
+                "answers_start": datasets.Sequence(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."""
+        if self.config.data_dir is None:
+            raise ValueError("This is a local dataset. Please pass the `data_dir` kwarg (where the .json is located) to load_dataset.")
+        else:
+            data_dir = Path(self.config.data_dir)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "file_path": data_dir / "train.json",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "file_path": data_dir / "dev.json",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "file_path": data_dir / "test.json",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, file_path: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        with open(file_path, "r", encoding="utf-8") as file:
+            data = json.load(file)
+
+        key = 0
+        for example in data["data"]:
+
+            if self.config.schema == "source":
+                for paragraph in example["paragraphs"]:
+                    for qa in paragraph["qas"]:
+                        yield key, {
+                            "id": qa["id"],
+                            "title": example["title"],
+                            "context": paragraph["context"],
+                            "question": qa["question"],
+                            "answers": qa["answers"],
+                        }
+                        key += 1
+
+            elif self.config.schema == _SEACROWD_SCHEMA:
+                for paragraph in example["paragraphs"]:
+                    for qa in paragraph["qas"]:
+                        yield key, {
+                            "id": str(key),
+                            "question_id": qa["id"],
+                            "document_id": example["title"],
+                            "question": qa["question"],
+                            "type": None,
+                            "choices": [],  # escape multiple_choice qa seacrowd test, can't be None
+                            "context": paragraph["context"],
+                            "answer": [answer["text"] for answer in qa["answers"]],
+                            "meta": {
+                                "answers_start": [answer["answer_start"] for answer in qa["answers"]],
+                            },
+                        }
+                        key += 1