first version

dataset_dict.json

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+{"splits": ["train", "test"]}

test/data-00000-of-00001.arrow

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+size 485808

test/dataset_info.json

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+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "title": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "summary": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "category": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

test/state.json

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+{
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+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "82a2f0e3fd5f277f",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}

train/data-00000-of-00001.arrow

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train/dataset_info.json

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+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "title": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "id": {
+      "dtype": "int64",
+      "_type": "Value"
+    },
+    "summary": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "category": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

train/state.json

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+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "5a2d02e6a2e569cd",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}

wikiextract.py

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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
+
+"""
+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
+            data = []
+            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()
+                    data.extend(category_data)
+
+        return data
+
+
+def main():
+    nltk.download("punkt")
+    extractor = WikiExtractor()
+    data = extractor()
+    dataset = Dataset.from_pandas(pd.DataFrame(data))
+
+    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("topics.dataset")
+
+
+if __name__ == "__main__":
+    main()