Update README.md
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README.md
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@@ -17,3 +17,75 @@ configs:
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- split: train
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path: data/train-*
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- split: train
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path: data/train-*
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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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```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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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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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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# Parse the HTML content
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tree = html.fromstring(response.content)
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question_data = []
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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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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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df.question = df.question.str.strip()
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df.answer = df.answer.str.strip()
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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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df.question = df.question.str.strip()
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df.answer = df.answer.str.strip()
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Dataset.from_pandas(df).push_to_hub("lightblue/architecture_faqs")
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```
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