Datasets:
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da1a91a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | from bs4 import BeautifulSoup
from datasets import load_dataset
def get_titles(file_path):
# get the titles from html file (html file is downloaded from https://de.wikipedia.org/wiki/Wikipedia:Exzellente_Artikel)
with open(file_path, 'r') as f:
html_content = f.read()
soup = BeautifulSoup(html_content, 'html.parser')
# Find the <tbody> element (you can modify this based on your HTML structure)
tbody = soup.find('tbody')
# Extract all <tr> elements within the <tbody> element
trs = tbody.find_all('tr')
# Remove the first two <tr> elements
trs = trs[2:]
# Extract title attributes from the remaining <tr> elements
titles = []
for tr in trs:
if tr is None or tr.find('a') is None:
continue
a_tags = tr.find_all('a')
for a_tag in a_tags:
if a_tag and 'title' in a_tag.attrs:
titles.append(a_tag['title'])
return titles
if __name__ == '__main__':
titles_exzellent = get_titles('exzellent.txt')
#titles_lesenswert = get_titles('lesenswert.txt')
titles = titles_exzellent #+ titles_lesenswert
titles = list(set(titles))
with open('titles.txt', 'w') as f:
for title in titles:
f.write(title + '\n')
# Get wikipedia dataset
dataset = load_dataset("graelo/wikipedia", "20230901.de", split="train")
# Filter dataset
dataset = dataset.filter(lambda example: example['title'] in titles, num_proc=64)
dataset.map(lambda x: {'text': f"# {x['title']}\n\n{x['text']}"}, remove_columns=['title'], num_proc=64)
# Save dataset
used_title = [example['title'] for example in dataset]
non_used_title = [title for title in titles if title not in used_title]
print(f'Number of used titles: {len(used_title)}')
print(f'Number of non used titles: {len(non_used_title)}')
print(non_used_title[:20])
dataset.push_to_hub("LeoLM/wiki_de_exzellent", private=True)
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