diff --git a/seacrowd/sea_datasets/vlogqa/__init__.py b/seacrowd/sea_datasets/vlogqa/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/seacrowd/sea_datasets/vlogqa/vlogqa.py b/seacrowd/sea_datasets/vlogqa/vlogqa.py new file mode 100644 index 000000000..0517cc497 --- /dev/null +++ b/seacrowd/sea_datasets/vlogqa/vlogqa.py @@ -0,0 +1,208 @@ +# 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 = "1.0.0" + + +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