The de-facto standard in many natural language processing (NLP) tasks nowadays is to use a transformer. Text generation? Transformer. Question-and-answering? Transformer. Language classification? Transformer!
However, one of the problems with many of these models (a problem that is not just restricted to transformer models) is that we cannot process long pieces of text.
Almost every article I write on Medium contains 1000+ words, which, when tokenized for a transformer model like BERT, will produce 1000+ tokens. BERT (and many other transformer models) will consume 512 tokens max - truncating anything beyond this length.
Although I think you may struggle to find value in processing my Medium articles, the same applies to many useful data sources - like news articles or Reddit posts.
We will take a look at how we can work around this limitation. In this article, we will find the sentiment for long posts from the /r/investing subreddit. This video will cover:
High-Level Approach
Getting Started
- Data
- Initialization
Tokenization
Preparing The Chunks
- Split
- CLS and SEP
- Padding
- Reshaping For BERT
Making Predictions
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