JPxxx/url-pretraining-dataset

URL Pre-training Corpus

This is a large-scale unlabeled corpus of ~68 million public URLs sampled from Common Crawl monthly snapshots (2020–2025). It was created for, and used in, the paper "From Lab to Production: Malicious URL Detection on Real-World Data" (anonymous submission) to pre-train transformer-based models on URL-specific structural and language patterns.

The corpus provides temporal and structural diversity, covering a wide range of domains, path/query structures, and IP-based URLs.

Intended Use

• Primary use: Masked Language Modeling (MLM) or other self-supervised pre-training objectives for URL-focused models.
• Ideal for initializing models that will be fine-tuned on downstream URL classification tasks (e.g., malicious URL detection).
• Enables domain-specific tokenization and representation learning for structural/semantic URL patterns.

Dataset Details

• Total samples: ~68 million URLs
• Columns:
  • url: Raw URL string (string)
  • year: Year of the Common Crawl snapshot the URL was sampled from (integer, 2020–2025)
• Filtering:
  • Only URLs with successful HTTP 200 responses.
  • Deduplicated.
  • Limited to ≤150 URLs per site-identifier (second-level domain or IP) to reduce bias toward popular sites.
• Sources: Randomly sampled shards from Common Crawl monthly crawls (2020–2025).

How to Load

from datasets import load_dataset

# Standard loading (downloads indexed data)
dataset = load_dataset("JPxxx/url-pretraining-dataset")

# For memory-efficient iteration over the full dataset
dataset = load_dataset("JPcooldev/url-pretraining-dataset", streaming=True)
for example in dataset["train"].take(5):
    print(example)

The dataset is provided as a single large split (train) due to its unsupervised nature. Use streaming=True to avoid downloading the entire dataset at once.

Statistics

• Temporal diversity: URLs from 2020 to 2025.
• Structural diversity: Includes short landing-page URLs, deep paths, complex queries, and IP-based addresses.

Limitations

• Derived from public Common Crawl data (public domain) — may contain outdated links (filtered to HTTP 200 at crawl time).
• No labels — purely for pre-training.

Citation

If you use this dataset, please cite:

@article{lab2prod2026,
  title={From Lab to Production: Malicious URL Detection on Real-World Data},
  author={Anonymous},
  year={2026},
  note = {Submitted for publication}
}