Practical Text Classification With Large Pre-Trained Language Models

Understand

Multi-emotion sentiment classification is a natural language processing (NLP) problem with valuable use cases on real-world data.

• We demonstrate that large-scale unsupervised language modeling combined with finetuning offers a practical solution to this task on difficult datasets, including those with label class imbalance and domain-specific context.
• By training an attention-based Transformer network (Vaswani et al.
• 2017) on 40GB of text (Amazon reviews) (McAuley et al.