WARC-DL: Scalable Web Archive Processing for Deep Learning

TL;DR

WARC-DL is a scalable pipeline designed for processing massive web archives to facilitate deep learning applications, addressing the lack of dedicated tools for such large-scale data handling.

Contribution

The paper introduces WARC-DL, the first publicly available scalable framework for deep learning-enabled processing of web archives.

Findings

Supports inference and training of neural networks on petabyte-scale data

Provides a scalable, open-source solution for web archive processing

Enables advanced machine learning research using large web datasets

Abstract

Web archives have grown to petabytes. In addition to providing invaluable background knowledge on many social and cultural developments over the last 30 years, they also provide vast amounts of training data for machine learning. To benefit from recent developments in Deep Learning, the use of web archives requires a scalable solution for their processing that supports inference with and training of neural networks. To date, there is no publicly available library for processing web archives in this way, and some existing applications use workarounds. This paper presents WARC-DL, a deep learning-enabled pipeline for web archive processing that scales to petabytes.