Encoder
The component of a transformer that processes input text into internal representations. BERT-style models are "encoder-only" and excel at understanding tasks.
In-Depth Explanation
The encoder in transformer architecture processes input sequences to create rich contextual representations. Unlike decoders, encoders can look at the entire input simultaneously using bidirectional attention - each token can attend to all other tokens, regardless of position.
Encoder models like BERT work by:
- Processing the entire input sequence at once
- Using self-attention to let each token gather information from all other tokens
- Building up contextual representations through multiple layers
- Outputting embeddings that capture deep contextual understanding
Key characteristics of encoder models:
- Bidirectional: See the full context in both directions
- Non-generative: Produce representations, not new text
- Efficient: Process entire sequences in parallel
- Versatile understanding: Excellent for classification, NER, and embeddings
While decoder-only models (GPT) dominate headlines, encoder models remain crucial for:
- Generating high-quality embeddings
- Text classification and sentiment analysis
- Named entity recognition
- Search relevance ranking
- Semantic similarity computation
Business Context
Encoder models are ideal for classification, sentiment analysis, and generating embeddings for search applications where understanding matters more than generation.
How Clever Ops Uses This
We use encoder models in our RAG pipelines for embedding generation. They're also essential for classification systems that route customer enquiries and categorise documents.
Example Use Case
"Using an encoder model to generate embeddings for semantic search, converting documents and queries into comparable vector representations."