Encoder-Only Models: How BERT Changed NLP Forever

Before BERT, NLP was a patchwork of specialized models for different tasks. After BERT, a single pre-trained model could be fine-tuned for virtually any language understanding task. BERT (Bidirectional Encoder Representations from Transformers), released by Google in 2018, demonstrated the power of encoder-only Transformer architectures and launched the era of pre-trained language models that we know today.

What Is an Encoder-Only Model?

An encoder-only model uses only the encoder stack of the original Transformer architecture. The critical difference from decoder-only models (like GPT) is that encoder models use bidirectional attention: each token can attend to all other tokens in the sequence, both before and after it. This gives the model a complete view of the input context.

In contrast, decoder models use causal (masked) attention, where each token can only see tokens that come before it. This makes decoders ideal for text generation but less effective for understanding tasks where you want the model to consider the full context.

"BERT's key insight was that understanding language requires looking at context in both directions. You cannot fully understand a word without seeing what comes after it."

How BERT Was Trained

BERT introduced two pre-training objectives that taught it deep language understanding.

Masked Language Modeling (MLM)

BERT randomly masks 15% of the tokens in each input sequence and trains to predict the masked tokens. Unlike autoregressive models that predict the next token, MLM forces the model to use bidirectional context -- looking at tokens both before and after the mask to make its prediction. This produces representations that capture the full context of each word.

For example, in the sentence "The cat sat on the [MASK]," BERT uses both "The cat sat on the" and any tokens that might follow to predict that [MASK] should be "mat" or "floor." This bidirectional understanding is the key advantage of encoder models.

Next Sentence Prediction (NSP)

BERT was also trained to predict whether two sentences appeared consecutively in the original text. This objective was designed to teach the model about relationships between sentences, useful for tasks like question answering and natural language inference. Later research showed that NSP was less important than originally thought, and some successor models (like RoBERTa) dropped it entirely.

The BERT Family

BERT spawned a family of improved encoder-only models, each addressing specific limitations.

RoBERTa (Robustly Optimized BERT)

Facebook's RoBERTa showed that BERT's training procedure was significantly under-optimized. By training longer, on more data, with larger batches, and without NSP, RoBERTa achieved substantial improvements across all benchmarks. It demonstrated that better training, not just better architecture, could unlock significant gains.

ALBERT (A Lite BERT)

ALBERT addressed BERT's memory consumption by sharing parameters across layers and factorizing the embedding matrix. This made ALBERT much smaller in terms of parameter count while maintaining competitive performance.

DeBERTa (Decoupled BERT)

Microsoft's DeBERTa improved on BERT by introducing disentangled attention that separates content and position information, and an enhanced mask decoder. DeBERTa was the first model to surpass human performance on the SuperGLUE benchmark, representing a major milestone for NLU.

Sentence-BERT

Sentence-BERT modified the BERT architecture to produce semantically meaningful sentence embeddings. This made it practical to use BERT for sentence similarity, semantic search, and clustering -- tasks where comparing pairs of sentences with standard BERT was computationally expensive.

What Encoder-Only Models Excel At

Encoder-only models are the architecture of choice for understanding tasks:

• Text classification: Sentiment analysis, topic classification, spam detection, intent recognition.
• Named Entity Recognition (NER): Identifying people, organizations, locations, and other entities in text.
• Question answering (extractive): Finding the answer span within a given passage.
• Semantic similarity: Determining how similar two pieces of text are.
• Token classification: Part-of-speech tagging, chunking, and other token-level tasks.
• Embedding generation: Creating dense vector representations of text for search and retrieval.

Encoder Models in the Age of LLMs

With the rise of large decoder-only models like GPT-4 and Claude, some have questioned whether encoder-only models are still relevant. The answer is a definitive yes, for several reasons.

First, encoder models are vastly more efficient for classification and embedding tasks. A BERT-base model has 110 million parameters and can run on a single CPU. Using a 70B parameter LLM for text classification is massive overkill for most applications.

Second, the embeddings produced by encoder models are often superior for retrieval tasks. Models like E5, BGE, and GTE -- all based on encoder architectures -- power the retrieval component of RAG systems that serve even the largest LLMs.

Third, for production systems that need to classify millions of requests per day, encoder models offer the best combination of accuracy, speed, and cost.

The Legacy of BERT

BERT's legacy extends far beyond any single model. It established the paradigm of pre-training on large corpora followed by task-specific fine-tuning. It demonstrated that unsupervised pre-training objectives could produce powerful general-purpose representations. And it showed that Transformers could transform NLP, setting the stage for everything that followed. The era of GPT and large language models stands on the foundation that BERT helped build.