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--- |
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license: cc-by-4.0 |
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task_categories: |
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- text-generation |
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- text-classification |
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language: |
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- ja |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Overview |
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This dataset provides a convenient and user-friendly format of data from [Aozora Bunko (青空文庫)](https://www.aozora.gr.jp/), a website that compiles public-domain books in Japan, ideal for Machine Learning applications. |
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[For Japanese] 日本語での概要説明を Qiita に記載しました: https://qiita.com/akeyhero/items/b53eae1c0bc4d54e321f |
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# Methodology |
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The code to reproduce this dataset is made available on GitHub: [globis-org/aozorabunko-exctractor](https://github.com/globis-org/aozorabunko-extractor). |
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## 1. Data collection |
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We firstly downloaded the [CSV file that lists all works](https://www.aozora.gr.jp/index_pages/person_all.html). The information extracted from this CSV is incorporated into the `meta` field. |
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Next, we filtered out any books not categorized as public domain. |
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We retrieved the main text of each book corresponding to every row in the CSV and incorporated it into the `text` field in UTF-8. |
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## 2. Deduplication |
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We removed entries where the `図書カードURL` (Library card URL) in this CSV did not match with the `作品ID` (Work ID) and `人物ID` (Person ID). |
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In addition, entries with text identical to previously encountered text were discarded. |
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## 3. Cleaning |
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The data in the `text` field was then cleaned in the following sequence: |
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1. Convert new lines to `\n` |
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2. Remove headers |
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3. Remove footnotes and add them to the `footnote` field |
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4. Convert inserted notes into regular parenthetical text |
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5. Remove ruby (phonetic guides) |
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6. Convert specific characters, such as external characters and iteration marks, into standard Unicode characters |
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7. Remove any remaining markup |
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8. Remove leading and trailing new lines and horizontal rules |
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# Tips |
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If you prefer to employ only modern Japanese, you can filter entries with: `row["meta"]["文字遣い種別"] == "新字新仮名"`. |
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# Example |
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```py |
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>>> from datasets import load_dataset |
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>>> ds = load_dataset('globis-university/aozorabunko-clean') |
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>>> ds |
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DatasetDict({ |
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train: Dataset({ |
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features: ['text', 'footnote', 'meta'], |
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num_rows: 16951 |
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}) |
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}) |
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>>> ds = ds.filter(lambda row: row['meta']['文字遣い種別'] == '新字新仮名') # only modern Japanese |
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>>> ds |
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DatasetDict({ |
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train: Dataset({ |
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features: ['text', 'footnote', 'meta'], |
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num_rows: 10246 |
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}) |
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}) |
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>>> book = ds['train'][0] # one of the works |
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>>> book['meta']['作品名'] |
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'ウェストミンスター寺院' |
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>>> text = book['text'] # main content |
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>>> len(text) |
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10639 |
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>>> print(text[:100]) |
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深いおどろきにうたれて、 |
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名高いウェストミンスターに |
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真鍮や石の記念碑となって |
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すべての王侯貴族が集まっているのをみれば、 |
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今はさげすみも、ほこりも、見栄もない。 |
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善にかえった貴人の姿、 |
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華美と俗世の |
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``` |
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# License |
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CC BY 4.0 |
