<p>There is an approach used in the paper Defending Against Neural Fake News ( <a href="https://arxiv.org/abs/1905.12616" rel="nofollow noreferrer">https://arxiv.org/abs/1905.12616</a>)</p>
<p>Their generative model was producing outputs of 1024 tokens and they wanted to use BERT for human vs machine generations. They extended the sequence length which BERT uses simply by initializing 512 more embeddings and training them while they were fine-tuning BERT on their dataset.</p>
How to use Bert for long text classification?
<p>We know that BERT has a max length limit of tokens = 512, So if an article has a length of much bigger than 512, such as 10000 tokens in text
How can BERT be used?</p>