An Integrated Data Processing Framework for Pretraining Foundation
  Models
Yiding Sun Feng Wang Yutao Zhu Wayne Xin Zhao Jiaxin Mao
The ability of the foundation models heavily relies on large-scale, diverse,
and high-quality pretraining data. In order to improve data quality,
researchers and practitioners often have to manually curate datasets from
difference sources and develop dedicated data cleansing pipeline for each data
repository. Lacking a unified data processing framework, this process is
repetitive and cumbersome. To mitigate this issue, we propose a data processing
framework that integrates a Processing Module which consists of a series of
operators at different granularity levels, and an Analyzing Module which
supports probing and evaluation of the refined data. The proposed framework is
easy to use and highly flexible. In this demo paper, we first introduce how to
use this framework with some example use cases and then demonstrate its
effectiveness in improving the data quality with an automated evaluation with
ChatGPT and an end-to-end evaluation in pretraining the GPT-2 model. The code
and demonstration videos are accessible on GitHub.