Upload dataset

JaQuAD.py

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+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@article{SkelterLabsInc:JaQuAD,
+title={{JaQuAD}: Japanese Question Answering Dataset for Machine Comprehension},
+author={Skelters Labs, Inc.},
+year={2022},
+}
+"""
+_DESCRIPTION = """\
+JaQuAD: Japanese Question Answering Dataset
+We introduce a human-annotated Japanese Question Answering Dataset.
+JaQuAD contains 39,696 question-answer pairs.
+Finetuning BERT-Japanese on JaQuAD achieves 78.92% for an F1 score and 63.38% for an exact match.
+Developed to provide a SQuAD-like QA dataset in Japanese.
+Questions are original and contexts are based on Japanese Wikipedia articles.
+"""
+_LICENSE = "CC BY-SA 3.0"
+_HOMEPAGE=""
+_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 f:
+                jaquad = json.load(f)
+                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"]]
+
+                            assert len(answer_starts) == len(answer_texts) == len(answer_types) == 1
+
+                            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
+languages:
+- ja
+licenses:
+- 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 Splits](#data-splits)
+- [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)
+  - [Contributions](#contributions)
+  
+## 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
+
+JaQuAD: Japanese Question Answering Dataset
+We introduce a human-annotated Japanese Question Answering Dataset.
+JaQuAD contains 39,696 question-answer pairs.
+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.
+Developed to provide a SQuAD-like QA dataset in Japanese. Questions are original and contexts are based on Japanese Wikipedia articles.
+
+### 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",
+        "answers_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.
+  - `answers_start`: a `int32` feature.
+  - `answer_type`: a `string` feature.
+
+### Data Splits
+
+The JaQuAD dataset has 3 splits: `train`, `validation`, and `test`. The splits contain disjoint sets of articles. However, the `test` split is not publicly released yet. The following table shows the statistics of each split.
+
+Dataset Split | 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.
+
+## 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
+
+TBA
+```bibtex
+@article{SkelterLabsInc:2022JaQuAD,
+       author = {Byunghoon, So and Kyuhong, Byun and Kyungwon, Kang and Seongjin, Cho},
+        title = {{JaQuAD}: Japanese Question Answering Dataset for Machine Reading Comprehension},
+         year = 2022,
+          eid = {arXiv:###},
+        pages = {arXiv:###},
+archivePrefix = {arXiv},
+       eprint = {###},
+}
+```
+
+### Contributions 
+
+Thanks to [@ByunghoonSo](https://github.com/w4-ByunghoonSo), [@khbyun](https://github.com/w4-khbyun), [@kangnak](https://github.com/kangnak), and [@sjcho](https://github.com/w4-sjcho) for adding this dataset.
+