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JaQuAD.py

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-'''Dataset loading script for JaQuAD.
-We refer to https://huggingface.co/datasets/squad_v2/blob/main/squad_v2.py
-'''
-import json
-import os
-
-import datasets
-
-_CITATION = '''
-@article{SkelterLabsInc:JaQuAD,
- title = {{JaQuAD}: Japanese Question Answering Dataset for Machine
-          Reading Comprehension},
-author = {Byunghoon So  and
-          Kyuhong Byun  and
-          Kyungwon Kang  and
-          Seongjin Cho},
-  year = {2022},
-}
-'''
-_DESCRIPTION = '''Japanese Question Answering Dataset (JaQuAD), released in
-2022, is a human-annotated dataset created for Japanese Machine Reading
-Comprehension. JaQuAD is developed to provide a SQuAD-like QA dataset in
-Japanese. JaQuAD contains 39,696 question-answer pairs. Questions and answers
-are manually curated by human annotators. Contexts are collected from Japanese
-Wikipedia articles.
-'''
-_LICENSE = 'CC BY-SA 3.0'
-_HOMEPAGE = 'https://skelterlabs.com/en/'
-_URL = 'https://huggingface.co/datasets/SkelterLabsInc/JaQuAD/raw/main/data/'
-
-
-class JaQuAD(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version('0.1.0')
-
-    def _info(self):
-        features = datasets.Features({
-            'id': datasets.Value('string'),
-            'title': datasets.Value('string'),
-            'context': datasets.Value('string'),
-            'question': datasets.Value('string'),
-            'question_type': datasets.Value('string'),
-            'answers':
-                datasets.features.Sequence({
-                    'text': datasets.Value('string'),
-                    'answer_start': datasets.Value('int32'),
-                    'answer_type': datasets.Value('string'),
-                }),
-        })
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        urls_to_download = {
-            'train': [
-                os.path.join(_URL, f'train/jaquad_train_{i:04d}.json')
-                for i in range(30)
-            ],
-            'dev': [
-                os.path.join(_URL, f'dev/jaquad_dev_{i:04d}.json')
-                for i in range(4)
-            ],
-        }
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={'filepaths': downloaded_files['train']},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={'filepaths': downloaded_files['dev']},
-            ),
-        ]
-
-    def _generate_examples(self, filepaths):
-        for filename in filepaths:
-            with open(filename, encoding='utf-8') as ifile:
-                jaquad = json.load(ifile)
-                for article in jaquad['data']:
-                    title = article.get('title', '').strip()
-                    for paragraph in article['paragraphs']:
-                        context = paragraph['context'].strip()
-                        for qa in paragraph['qas']:
-                            qa_id = qa['id']
-                            question = qa['question'].strip()
-                            question_type = qa['question_type']
-
-                            answer_starts = [
-                                answer['answer_start']
-                                for answer in qa['answers']
-                            ]
-                            answer_texts = [
-                                answer['text'].strip()
-                                for answer in qa['answers']
-                            ]
-                            answer_types = [
-                                answer['answer_type']
-                                for answer in qa['answers']
-                            ]
-
-                            yield qa_id, {
-                                'title': title,
-                                'context': context,
-                                'question': question,
-                                'question_type': question_type,
-                                'id': qa_id,
-                                'answers': {
-                                    'text': answer_texts,
-                                    'answer_start': answer_starts,
-                                    'answer_type': answer_types,
-                                },
-                            }

README.md

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----
-annotations_creators:
-- crowdsourced
-language_creators:
-- crowdsourced
-- found
-language:
-- ja
-license:
-- cc-by-sa-3.0
-multilinguality:
-- monolingual
-paperswithcode_id: null
-pretty_name: "JaQuAD: Japanese Question Answering Dataset"
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- question-answering
-task_ids:
-- extractive-qa
----
-
-# Dataset Card for JaQuAD
-
-## Table of Contents
-
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splitting](#data-splitting)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Acknowledgements](#acknowledgements)
-
-## Dataset Description
-
-- **Repository:** https://github.com/SkelterLabsInc/JaQuAD
-- **Paper:** [JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension]()
-- **Point of Contact:** [jaquad@skelterlabs.com](jaquad@skelterlabs.com)
-- **Size of dataset files:** 24.6 MB
-- **Size of the generated dataset:** 48.6 MB
-- **Total amount of disk used:** 73.2 MB
-
-### Dataset Summary
-
-Japanese Question Answering Dataset (JaQuAD), released in 2022, is a
-human-annotated dataset created for Japanese Machine Reading Comprehension.
-JaQuAD is developed to provide a SQuAD-like QA dataset in Japanese.
-JaQuAD contains 39,696 question-answer pairs.
-Questions and answers are manually curated by human annotators.
-Contexts are collected from Japanese Wikipedia articles.
-Fine-tuning [BERT-Japanese](https://huggingface.co/cl-tohoku/bert-base-japanese)
-on JaQuAD achieves 78.92% for an F1 score and 63.38% for an exact match.
-
-### Supported Tasks
-
-- `extractive-qa`: This dataset is intended to be used for `extractive-qa`.
-
-### Languages
-
-Japanese (`ja`)
-
-## Dataset Structure
-
-### Data Instances
-
-- **Size of dataset files:** 24.6 MB
-- **Size of the generated dataset:** 48.6 MB
-- **Total amount of disk used:** 73.2 MB
-
-An example of 'validation':
-```python
-{
-    "id": "de-001-00-000",
-    "title": "イタセンパラ",
-    "context": "イタセンパラ(板鮮腹、Acheilognathuslongipinnis)は、コイ科のタナゴ亜科タナゴ属に分類される淡水>魚の一種。\n別名はビワタナゴ(琵琶鱮、琵琶鰱)。",
-    "question": "ビワタナゴの正式名称は何?",
-    "question_type": "Multiple sentence reasoning",
-    "answers": {
-        "text": "イタセンパラ",
-        "answer_start": 0,
-        "answer_type": "Object",
-    },
-},
-```
-
-### Data Fields
-
-- `id`: a `string` feature.
-- `title`: a `string` feature.
-- `context`: a `string` feature.
-- `question`: a `string` feature.
-- `question_type`: a `string` feature.
-- `answers`: a dictionary feature containing:
-  - `text`: a `string` feature.
-  - `answer_start`: a `int32` feature.
-  - `answer_type`: a `string` feature.
-
-### Data Splitting
-
-JaQuAD consists of three sets, `train`, `validation`, and `test`. They were
-created from disjoint sets of Wikipedia articles. The `test` set is not publicly
-released yet. The following table shows statistics for each set.
-
-Set | Number of Articles | Number of Contexts | Number of Questions
---------------|--------------------|--------------------|--------------------
-Train | 691 | 9713 | 31748
-Validation | 101 | 1431 | 3939
-Test | 109 | 1479 | 4009
-
-
-## Dataset Creation
-
-### Curation Rationale
-
-The JaQuAD dataset was created by [Skelter Labs](https://skelterlabs.com/) to
-provide a SQuAD-like QA dataset in Japanese. Questions are original and based
-on Japanese Wikipedia articles.
-
-### Source Data
-
-The articles used for the contexts are from [Japanese Wikipedia](https://ja.wikipedia.org/).
-88.7% of articles are from the curated list of Japanese high-quality Wikipedia
-articles, e.g., [featured articles](https://ja.wikipedia.org/wiki/Wikipedia:%E8%89%AF%E8%B3%AA%E3%81%AA%E8%A8%98%E4%BA%8B)
-and [good articles](https://ja.wikipedia.org/wiki/Wikipedia:%E7%A7%80%E9%80%B8%E3%81%AA%E8%A8%98%E4%BA%8B).
-
-### Annotations
-
-Wikipedia articles were scrapped and divided into one more multiple paragraphs
-as contexts. Annotations (questions and answer spans) are written by fluent
-Japanese speakers, including natives and non-natives. Annotators were given a
-context and asked to generate non-trivial questions about information in the
-context.
-
-### Personal and Sensitive Information
-
-No personal or sensitive information is included in this dataset. Dataset
-annotators has been manually verified it.
-
-## Considerations for Using the Data
-
-Users should consider that the articles are sampled from Wikipedia articles but
-not representative of all Wikipedia articles.
-
-### Social Impact of Dataset
-
-The social biases of this dataset have not yet been investigated.
-
-### Discussion of Biases
-
-The social biases of this dataset have not yet been investigated. Articles and
-questions have been selected for quality and diversity.
-
-### Other Known Limitations
-
-The JaQuAD dataset has limitations as follows:
-- Most of them are short answers.
-- Assume that a question is answerable using the corresponding context.
-
-This dataset is incomplete yet. If you find any errors in JaQuAD, please contact
-us.
-
-## Additional Information
-
-### Dataset Curators
-
-Skelter Labs: [https://skelterlabs.com/](https://skelterlabs.com/)
-
-### Licensing Information
-
-The JaQuAD dataset is licensed under the [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license.
-
-### Citation Information
-
-```bibtex
-@misc{so2022jaquad,
-      title={{JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension}},
-      author={ByungHoon So and Kyuhong Byun and Kyungwon Kang and Seongjin Cho},
-      year={2022},
-      eprint={2202.01764},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
-```
-
-### Acknowledgements
-
-This work was supported by [TPU Research Cloud (TRC) program](https://sites.research.google/trc/).
-For training models, we used cloud TPUs provided by TRC. We also thank
-annotators who generated JaQuAD.
-

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