Duplicate from globis-university/aozorabunko-clean

Co-authored-by: Akihiro Katsura <akeyhero@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- text-generation
+- text-classification
+language:
+- ja
+size_categories:
+- 10K<n<100K
+---
+
+# Overview
+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.
+
+[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
+
+# Methodology
+
+The code to reproduce this dataset is made available on GitHub: [globis-org/aozorabunko-exctractor](https://github.com/globis-org/aozorabunko-extractor).
+
+## 1. Data collection
+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.
+Next, we filtered out any books not categorized as public domain.
+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.
+
+## 2. Deduplication
+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).
+In addition, entries with text identical to previously encountered text were discarded.
+
+## 3. Cleaning
+The data in the `text` field was then cleaned in the following sequence:
+
+1. Convert new lines to `\n`
+2. Remove headers
+3. Remove footnotes and add them to the `footnote` field
+4. Convert inserted notes into regular parenthetical text
+5. Remove ruby (phonetic guides)
+6. Convert specific characters, such as external characters and iteration marks, into standard Unicode characters
+7. Remove any remaining markup
+8. Remove leading and trailing new lines and horizontal rules
+
+# Tips
+
+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"`.
+
+# Example
+
+```py
+>>> from datasets import load_dataset
+>>> ds = load_dataset('globis-university/aozorabunko-clean')
+>>> ds
+DatasetDict({
+    train: Dataset({
+        features: ['text', 'footnote', 'meta'],
+        num_rows: 16951
+    })
+})
+>>> ds = ds.filter(lambda row: row['meta']['文字遣い種別'] == '新字新仮名')  # only modern Japanese
+>>> ds
+DatasetDict({
+    train: Dataset({
+        features: ['text', 'footnote', 'meta'],
+        num_rows: 10246
+    })
+})
+>>> book = ds['train'][0]  # one of the works
+>>> book['meta']['作品名']
+'ウェストミンスター寺院'
+>>> text = book['text']  # main content
+>>> len(text)
+10639
+>>> print(text[:100])
+深いおどろきにうたれて、
+名高いウェストミンスターに
+真鍮や石の記念碑となって
+すべての王侯貴族が集まっているのをみれば、
+今はさげすみも、ほこりも、見栄もない。
+善にかえった貴人の姿、
+華美と俗世の
+```
+
+# License
+CC BY 4.0

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