# Lessons Learned from Applying off-the-shelf BERT: There is no Silver Bullet

arXiv: https://arxiv.org/abs/2009.07238 · HTML: https://mlsanity.com/paper/2009.07238/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Three-level Hierarchical Transformer Networks for Long-sequence and Multiple Clinical Documents Classification](https://mlsanity.com/paper/2104.08444/) (2021)

## Abstract (arXiv)

One of the challenges in the NLP field is training large classification models, a task that is both difficult and tedious. It is even harder when GPU hardware is unavailable. The increased availability of pre-trained and off-the-shelf word embeddings, models, and modules aim at easing the process of training large models and achieving a competitive performance. We explore the use of off-the-shelf BERT models and share the results of our experiments and compare their results to those of LSTM networks and more simple baselines. We show that the complexity and computational cost of BERT is not a guarantee for enhanced predictive performance in the classification tasks at hand.