ModernBERT: A Next-Generation Encoder Model for Agentic AI and GenAI

Dec 30, 2024 by Sabber ahamed

In this blog post, I explore one of my model BERT and its successor ModernBERT, a next-generation encoder model developed by Answer.AI and LightOn. ModernBERT builds on the success of the original BERT model while introducing significant improvements in context length, architecture, and training data diversity.

The above image shows the performance comparison between ModernBERT and BERT on various NLP tasks. As you can see, ModernBERT outperforms BERT across the board, with significant improvements in GLUE score and runtime efficiency.

In this blog post, I multiple sources to provide a comprehensive overview of ModernBERT's key features, architectural enhancements, training advantages, and applications in modern AI systems.

The BERT Legacy

BERT (Bidirectional Encoder Representations from Transformers) revolutionized Natural Language Processing when it was introduced by Google in 2018 Here is the link of the original paper.

As the first deep learning model to process text bidirectionally, it dramatically improved performance on various NLP tasks like sentiment analysis, named entity recognition, and text classification. It was the starting point for many subsequent transformer-based models like GPT-2, RoBERTa, and T5.

BERT

The above image shows the differences in pre-training model architectures. BERT is a bidirectional model, while OpenAI's GPT is a left-to-right transformer model. ELMO uses the concatenation of independently trained left-to-right and right-to-left models.

However, despite its groundbreaking impact, BERT has some limitations that have become more pronounced as AI applications have evolved, specially in 2025, when the AI era has reached its peak. Some of the key challenges with BERT include:

Restricted context length of 512 tokens made it difficult to process longer documents or analyze complex text structures effectively
High computational resource demands meant significant infrastructure requirements for deployment and scaling
Lack of code understanding capabilities limited its utility in software development and code analysis applications
Limited training data diversity affected its performance across different domains and specialized fields

ModernBERT: A replacement for BERT?

ModernBERT, developed by Answer.AI and LightOn, represents a major advancement in encoder-only models. It maintains full backward compatibility with BERT while introducing significant improvements. More details on ModernBERT can be found on their blog post here.

Authors of this paper expect that this encoder based ModernBert will become new standard for many application specially in the RAG (Retrieval Augmented Generation) pipeline. I completely agree with this statement as I see the potential of this model in the future. I can immediately the potential of this model in the the classification, routing in LLMs, and in the RAG pipeline.

Key Architectural Improvements

ModernBERT

1. Extended Context Length

8,192 token sequence length (16x longer than BERT)
Enables processing of much longer documents and code snippets
Improves handling of complex technical documentation and large-scale analysis

2. Modernized Architecture

As you can see, the ModernBert uses Rotary Positional Embeddings (RoPE) for better token position understanding
It uses GeGLU activation layers replacing traditional MLP layers
Streamlined architecture with optimized bias terms
Additional normalization layer for training stability

3. Efficiency Innovations

Integration of Flash Attention 2 for faster computation. This is huge, as we see most of the big LLM are adopting this attention mechanism for faster computation.
Alternating attention patterns (global and local) for better efficiency
Hardware-aware design optimized for common GPU configurations

Training Advantages

ModernBERT was trained on 2 trillion tokens from diverse sources, including:

Web documents
Programming code
Scientific articles
Technical documentation

This diverse training data makes it more robust and versatile than traditional BERT models trained primarily on Wikipedia.

Key Differences from Original BERT

Efficiency

1. Performance

Superior accuracy across multiple benchmarks
2-4x faster processing speed
Better memory efficiency
Higher scores on code-related tasks

2. Scalability

Handles variable-length inputs more efficiently
Better batch processing capabilities
More efficient resource utilization

3. Versatility

Broader domain understanding
Better code comprehension
Improved long-text processing

Applications in Agentic AI and GenAI

ModernBERT's capabilities make it particularly valuable for modern AI applications specilly in the Retrival Augmented Generation (RAG) pipeline. My favorite application I can think of the Routing in LLMs. In the agentic application we use multiple agents. This is where the routing comes into play. We can use ModernBERT to classify next step for the agents.

Here's a basic example of using ModernBERT for text classification:

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "answerdotai/ModernBERT-base"
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Prepare input
prompt = "Based on the data.. and following user input.. "
inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

Conclusion

ModernBERT represents a significant step forward in encoder model technology, particularly for enterprise and production applications. Its combination of improved architecture, efficient processing, and broader training makes it an excellent choice for modern AI systems, especially in scenarios requiring reliable, fast, and accurate text processing.

For organizations building AI systems, ModernBERT offers a compelling balance of performance and efficiency, making it particularly valuable for production deployments where both accuracy and resource utilization are critical concerns.