Data is the most important component for building a machine learning model. Recently researchers from Google trained a CNN model for image classification on 300 million images and they demonstrated that even on a scale of hundreds of millions of examples adding more data helps to improve the model performance. Apparently, more data is better. But where can you get large datasets if you are doing research on text classification?

I found nice references to a few large text classification datasets in “Text Understanding from Scratch” paper by Xiang Zhang and Yann LeCun. The paper describes a character-level CNN model for text classification. Authors provide benchmarks of different CNN architectures and a few simple models on a few datasets. More recent version of this paper: “Character-level Convolutional Networks for Text Classification” contains more experimental results but it misses some details on dataset usage: which fields to use, how to truncate long texts, etc. If you are looking for information about datasets, read the older paper. If you want to learn more about the character level CNN models, read the latest paper.

Somebody uploaded the datasets to Google Drive, so you can download them here.

If you have other large text classification datasets, please share in comments to this post.