transformers/docs/source/en/model_doc/modernbert.md

ModernBERT

Overview

The ModernBERT model was proposed in Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference by Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Galalgher, Raja Bisas, Faisal Ladhak, Tom Aarsen, Nathan Cooper, Grifin Adams, Jeremy Howard and Iacopo Poli.

It is a refresh of the traditional encoder architecture, as used in previous models such as BERT and RoBERTa.

It builds on BERT and implements many modern architectural improvements which have been developed since its original release, such as:

• Rotary Positional Embeddings to support sequences of up to 8192 tokens.
• Unpadding to ensure no compute is wasted on padding tokens, speeding up processing time for batches with mixed-length sequences.
• GeGLU Replacing the original MLP layers with GeGLU layers, shown to improve performance.
• Alternating Attention where most attention layers employ a sliding window of 128 tokens, with Global Attention only used every 3 layers.
• Flash Attention to speed up processing.
• A model designed following recent The Case for Co-Designing Model Architectures with Hardware, ensuring maximum efficiency across inference GPUs.
• Modern training data scales (2 trillion tokens) and mixtures (including code ande math data)

The abstract from the paper is the following:

Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.

The original code can be found here.

Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ModernBert.

• A notebook on how to finetune for General Language Understanding Evaluation (GLUE) with Transformers, also available as a Google Colab notebook. 🌎

• A script on how to finetune for text similarity or information retrieval with Sentence Transformers. 🌎
• A script on how to finetune for information retrieval with PyLate. 🌎

• Masked language modeling task guide

• [ModernBertForQuestionAnswering] is supported by this example script and colab notebook.

ModernBertConfig

autodoc ModernBertConfig

ModernBertModel

autodoc ModernBertModel - forward

ModernBertForMaskedLM

autodoc ModernBertForMaskedLM - forward

ModernBertForSequenceClassification

autodoc ModernBertForSequenceClassification - forward

ModernBertForTokenClassification

autodoc ModernBertForTokenClassification - forward

ModernBertForQuestionAnswering

autodoc ModernBertForQuestionAnswering - forward

Usage tips

The ModernBert model can be fine-tuned using the HuggingFace Transformers library with its official script for question-answering tasks.