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"""
Creates a text/category dataset using Wikipedia.
Explores the 40 root categories and their sub-categories to collect pages that are seen only on
each root category. The produced dataset provides 200 pages per category.
Author: Tarek Ziadé / Mozilla
"""
import os
from collections import defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
from threading import Lock
import wikipediaapi
from datasets import Dataset, DatasetDict
import nltk
from nltk.tokenize import sent_tokenize
import pandas as pd
from tqdm import tqdm
_LIMIT_PER_CAT = 200
_ROOT_CATS = [
"Academic_disciplines",
"Business",
"Communication",
"Concepts",
"Culture",
"Economy",
"Education",
"Energy",
"Engineering",
"Entertainment",
"Entities",
"Ethics",
"Food_and_drink",
"Geography",
"Government",
"Health",
"History",
"Human_behavior",
"Humanities",
"Information",
"Internet",
"Knowledge",
"Language",
"Law",
"Life",
"Lists",
"Mass media",
"Mathematics",
"Military",
"Nature",
"People",
"Philosophy",
"Politics",
"Religion",
"Science",
"Society",
"Sports",
"Technology",
"Time",
"Universe",
]
class WikiExtractor:
def __init__(self):
self.visited_page_ids = defaultdict(set)
self.all_ids = set()
self.client = wikipediaapi.Wikipedia("MediaWikiCat Project", "en", timeout=30)
self.data_lock = Lock()
self.pbar = None
def fetch_pages_from_category(
self,
root_category_name,
category_name,
limit_per_category=_LIMIT_PER_CAT,
depth=0,
max_depth=10,
):
if len(self.visited_page_ids[root_category_name]) >= limit_per_category:
return []
if depth > max_depth: # Limit the recursion depth
return []
cat = self.client.page(category_name)
pages = []
# Fetch pages from the current category
for c in cat.categorymembers.values():
if (
c.ns == wikipediaapi.Namespace.MAIN
and c.pageid not in self.visited_page_ids
):
if c.pageid in self.all_ids:
continue
pages.append(c)
with self.data_lock: # Ensure thread-safe updates
self.visited_page_ids[root_category_name].add(c.pageid)
self.all_ids.add(c.pageid)
if len(self.visited_page_ids[root_category_name]) >= limit_per_category:
break
# Fetch pages from subcategories
for subcat in cat.categorymembers.values():
if subcat.ns == wikipediaapi.Namespace.CATEGORY:
pages += self.fetch_pages_from_category(
root_category_name,
subcat.title,
limit_per_category,
depth + 1,
max_depth,
)
return pages
def preprocess_content(self, text):
sentences = sent_tokenize(text)[:5]
return " ".join(sentences)
def process_page(self, page):
if page.summary:
summary = self.preprocess_content(page.summary)
else:
summary = self.preprocess_content(page.text)
summary = self.preprocess_content(summary)
return {
"title": page.title,
"id": page.pageid,
"summary": summary,
}
def process_category(self, category):
category_data = []
category = f"Category:{category}"
pages = self.fetch_pages_from_category(category, category)
for page in pages:
try:
data = self.process_page(page)
except Exception as e:
import pdb
pdb.set_trace()
continue
data["category"] = category
category_data.append(data)
if self.pbar is not None:
self.pbar.update(1)
return category_data
def __call__(self):
with tqdm(
total=len(_ROOT_CATS) * _LIMIT_PER_CAT, desc="Processing Categories"
) as pbar:
self.pbar = pbar
with ThreadPoolExecutor(max_workers=15) as executor:
future_to_category = {
executor.submit(self.process_category, category): category
for category in _ROOT_CATS
}
for future in as_completed(future_to_category):
category_data = future.result()
for item in category_data:
yield item
def main():
nltk.download("punkt")
extractor = WikiExtractor()
dataset = Dataset.from_generator(extractor)
train_test_split = dataset.train_test_split(test_size=0.1)
dataset_dict = DatasetDict(
{"train": train_test_split["train"], "test": train_test_split["test"]}
)
dataset_dict.save_to_disk(os.path.dirname(__file__))
if __name__ == "__main__":
main()
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