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--- |
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dataset_info: |
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features: |
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- name: question |
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dtype: string |
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- name: answer |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 130703 |
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num_examples: 250 |
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download_size: 54948 |
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dataset_size: 130703 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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task_categories: |
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- question-answering |
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language: |
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- ja |
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--- |
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|
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Japanese construction themes FAQs scraped from [https://www.city.yokohama.lg.jp/business/bunyabetsu/kenchiku/annai/faq/qa.html](https://www.city.yokohama.lg.jp/business/bunyabetsu/kenchiku/annai/faq/qa.html). |
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Downloaded using the following code: |
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|
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```python |
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import requests |
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from lxml import html |
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import pandas as pd |
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from datasets import Dataset |
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|
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hrefs = [ |
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"/business/bunyabetsu/kenchiku/annai/faq/ji-annnai.html", |
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"/business/bunyabetsu/kenchiku/tetsuduki/kakunin/qa-kakunin.html", |
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"/business/bunyabetsu/kenchiku/tetsuduki/teikihoukoku/seido/01.html", |
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"/business/bunyabetsu/kenchiku/tetsuduki/teikihoukoku/seido/07.html", |
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"/business/bunyabetsu/kenchiku/tetsuduki/doro/qa-doro.html", |
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"/business/bunyabetsu/kenchiku/tetsuduki/doro/qa-doro.html", |
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"/business/bunyabetsu/kenchiku/bosai/kyoai/jigyou/qanda.html", |
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"/business/bunyabetsu/kenchiku/tetsuduki/kyoka/43.html", |
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"/business/bunyabetsu/kenchiku/takuchi/toiawase/keikakuho/tokeihou.html", |
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"/business/bunyabetsu/kenchiku/takuchi/toiawase/kiseiho/takuzo.html", |
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"/business/bunyabetsu/kenchiku/takuchi/toiawase/keikakuho/q4-1.html", |
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"/business/bunyabetsu/kenchiku/kankyo-shoene/casbee/hairyo/qa.html", |
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"/business/bunyabetsu/kenchiku/tetsuduki/jorei/machizukuri/fukumachiqa.html", |
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"/business/bunyabetsu/kenchiku/kankyo-shoene/chouki/qa-chouki.html", |
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"/business/bunyabetsu/kenchiku/kankyo-shoene/huuti/qa-huuchi.html", |
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"/kurashi/machizukuri-kankyo/kotsu/toshikotsu/chushajo/jorei/qa.html", |
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] |
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url_stem = "https://www.city.yokohama.lg.jp" |
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|
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def get_question_text(url): |
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# Send a GET request to the webpage |
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response = requests.get(url) |
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|
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# Parse the HTML content |
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tree = html.fromstring(response.content) |
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question_data = [] |
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|
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# Use XPath to find the desired elements |
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for qa_element in tree.xpath('//div[@class="contents-area"]/section'): |
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question_data.append({ |
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"question": qa_element.xpath('.//div[@class="question-text"]/text()')[0], |
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"answer": "\n".join(qa_element.xpath('.//div[@class="answer-text"]/div/p/text()')) |
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}) |
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return question_data |
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qa_list = [] |
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for href in hrefs: |
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print(href) |
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qa_list.extend(get_question_text(url_stem + href)) |
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df = pd.DataFrame(qa_list) |
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|
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df.question = df.question.apply(lambda x: x[len(x.split()[0]):] if " " in x[:7] or "　" in x[:7] else x) |
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df.answer = df.answer.apply(lambda x: x[len(x.split()[0]):] if " " in x[:7] or "　" in x[:7] else x) |
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|
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df.question = df.question.str.strip() |
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df.answer = df.answer.str.strip() |
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|
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df.question = df.question.apply(lambda x: x[:-len(x.split("＜")[-1])-1] if "＜" in x else x) |
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df.answer = df.answer.apply(lambda x: x[:-len(x.split("＜")[-1])-1] if "＜" in x else x) |
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|
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df.question = df.question.str.strip() |
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df.answer = df.answer.str.strip() |
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|
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Dataset.from_pandas(df).push_to_hub("lightblue/architecture_faqs") |
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``` |
