bert-base-large-layer-count

IN premise — summaries/2026/08/24/reimers-2019-sentence-bert-s1-introduction.md

Created 2026-08-25T02:58:27+00:00

BERT base has 12 transformer layers and BERT large has 24 layers, both using multi-head self-attention with [CLS] and [SEP] special tokens.

Summary

This locks in the basic architecture facts about two widely-used language models: the smaller one stacks 12 layers of processing while the larger one doubles that to 24, but both use the same attention mechanism and the same special marker tokens to frame input sentences. It matters because it gives the system a fixed, agreed-upon reference point for any downstream reasoning about model depth, compute cost, or how the two variants compare in capability.