Simon Willison’s Weblog

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2024-12-24 15:00:07

Finally, a replacement for BERT: Introducing ModernBERT (via) BERT was an early language model released by Google in October 2018. Unlike modern LLMs it wasn't designed for generating text. BERT was trained for masked token prediction and was generally applied to problems like Named Entity Recognition or Sentiment Analysis. BERT also wasn't very useful on its own - most applications required you to fine-tune a model on top of it.

In exploring BERT I decided to try out dslim/distilbert-NER, a popular Named Entity Recognition model fine-tuned on top of DistilBERT (a smaller distilled version of the original BERT model). Here are my notes on running that using uv run.

Jeremy Howard's Answer.AI research group, LightOn and friends supported the development of ModernBERT, a brand new BERT-style model that applies many enhancements from the past six years of advances in this space.

While BERT was trained on 3.3 billion tokens, producing 110 million and 340 million parameter models, ModernBERT trained on 2 trillion tokens, resulting in 140 million and 395 million parameter models. The parameter count hasn't increased much because it's designed to run on lower-end hardware. It has a 8192 token context length, a significant improvement on BERT's 512.