File size: 5,296 Bytes
55a16f5 f2cd704 55a16f5 f2cd704 55a16f5 a138ba1 55a16f5 6516639 55a16f5 6516639 55a16f5 |
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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 |
"""
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()
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.save_to_disk(os.path.dirname(__file__))
dataset.save_to_disk(os.path.dirname(__file__))
if __name__ == "__main__":
main()
|