will be in main repo

wikiextract.py

@@ -1,190 +0,0 @@
-"""
-    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:
-            data = self.process_page(page)
-            data["category"] = category.removeprefix("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()
-    pages = list(extractor())
-
-    def gen():
-        for page in pages:
-            yield page
-
-    dataset = Dataset.from_generator(gen)
-    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.push_to_hub("tarekziade/wikipedia-topics")
-
-
-if __name__ == "__main__":
-    main()