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BERT — Encoder-Only Transformers (Explained)
Learn what BERT is, masked language modeling, embeddings, and typical NLP uses.
What you’ll learn
- What “encoder-only” Transformer means.
- Masked Language Modeling (MLM) training objective.
- Why BERT is strong for classification + embeddings.
Encoder-only Transformers
BERT is built from Transformer encoder blocks that read the whole input bidirectionally.
This makes BERT excellent at understanding context for tasks like classification and retrieval.
Masked Language Modeling (MLM)
During training, some tokens are masked and the model learns to predict them using surrounding context.
This teaches deep contextual representations rather than left-to-right next-token prediction.
How it’s used
Fine-tune BERT for classification (sentiment, intent, topic) by adding a small head on top.
Use embeddings for semantic search and clustering when properly trained/pooled.
Key takeaways
- BERT reads context bidirectionally (encoder-only).
- MLM teaches contextual token representations.
- Great for understanding tasks; not primarily for long-form generation.
- Modern variants improve efficiency, size, and domain adaptation.
Want more ML topics added here (SVM, Naive Bayes, CNN, PCA, Decision Trees)?
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