Over the last few years, we have seen tremendous progress in large language models (LLMs). Yet, while decoder-based transformers (like GPT and Llama) have stolen much of the spotlight—particularly for tasks involving text generation and chatbot interfaces—encoder-only models such as BERT (Bidirectional Encoder Representations from Transformers) have continued to power critical applications behind the scenes. Indeed, search engines, text classifiers, recommendation systems, knowledge extraction pipelines, and retrieval-augmented generation (RAG) frameworks all rely heavily on encoders to build high-quality vector representations.
ModernBERT is the new frontier in encoder-only architecture—designed to be smarter, faster, more efficient, and capable of handling longer contexts than its predecessors. In this in-depth blog post, we will walk you through every facet of ModernBERT, from architecture and training procedures to downstream evaluations, to potential use cases and limitations. We'll also examine how ModernBERT can be used in conjunction with emerging retrieval methods such as GraphRAG, helping you see how the two can coexist or complement each other in the world of large-scale information retrieval and question answering.
Why Encoders Still Matter
While generative models get most of the headlines these days, encoder-only transformers power innumerable tasks that require deep, bi-directional language understanding but not necessarily free-form text generation. These tasks include:
- Semantic search & vector retrieval: Turning documents and queries into high-quality embeddings for dense retrieval or recommendation.
- Classification & NLU: Sentiment analysis, natural language inference, and Named Entity Recognition (NER) frequently rely on strong encoder architectures.
- RAG pipelines: Retrieval-augmented generation, which pairs a generative LLM with a lightweight, high-accuracy encoder to fetch relevant context efficiently.
Historically, BERT has been the gold standard for encoders. But BERT, released in 2018, was limited to 512 tokens and trained on data from a narrower domain. Over the years, the research community identified numerous architectural, training, and efficiency improvements—yet the encoder-only domain had not fully absorbed them. ModernBERT changes this landscape, bringing encoder architectures firmly into the modern era with longer context windows, efficient memory usage, code-awareness, and state-of-the-art performance in tasks spanning classification and retrieval.