Update README.md

README.md

@@ -19,3 +19,77 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+
+Created by the following code:
+```py
+!pip install -Uq datasets
+
+import requests
+from bs4 import BeautifulSoup, Comment
+import pandas as pd
+from datasets import Dataset
+
+
+def get_content(url):
+    response = requests.get(url)
+    if response.status_code == 200:
+        soup = BeautifulSoup(response.text, 'html.parser')
+    return soup
+
+url = "https://huggingface.co/blog/community"
+
+soup = get_content(url)
+articles = soup.find_all("article")
+titles = [article.h4.text for article in articles]
+links = [f'https://hf.co{article.find("a", class_="block px-3 py-2 cursor-pointer").get("href")}' for article in articles]
+
+def get_article(soup):
+    # Find all comments in the document
+    comments = soup.find_all(string=lambda text: isinstance(text, Comment))
+
+    # Initialize variables to store the start and end comments
+    start_comment = None
+    end_comment = None
+
+    # Identify the start and end comments
+    for comment in comments:
+        comment_text = comment.strip()
+        if comment_text == 'HTML_TAG_START':
+            start_comment = comment
+        elif comment_text == 'HTML_TAG_END':
+            end_comment = comment
+
+    # Check if both comments were found
+    if start_comment and end_comment:
+        # Collect all elements between the start and end comments
+        contents = []
+        current = start_comment.next_sibling
+        while current and current != end_comment:
+            contents.append(current)
+            current = current.next_sibling
+
+        # Convert the contents to a string
+        between_content = ''.join(str(item) for item in contents)
+
+        # Output the extracted content
+        return between_content
+    else:
+        return "Start or end comment not found."
+
+article_soups = [get_content(link) for link in links]
+articles = [get_article(article_soup) for article_soup in article_soups]
+
+# Assuming titles, links, articles are your lists
+df = pd.DataFrame({
+    'title': titles,
+    'link': links,
+    'article': articles
+})
+
+# Create a Hugging Face Dataset object
+dataset = Dataset.from_pandas(df)
+
+# Push the dataset to the Hugging Face Hub
+dataset.push_to_hub("ariG23498/community-blogs")
+```