Phase 0: Attention Mechanisms · ~45 minutes · Python
BERT — Masked Language Modeling
GPT predicts the next word. BERT predicts a missing word. One sentence of difference — and half a decade of everything embedding-shaped.
Hiring signal: Understanding of bert — masked language modeling internals
What you will learn
- Implement bert — masked language modeling from scratch
- Understand the math and intuition behind the algorithm
- Use production libraries for the same task
- Ship a reusable artifact
Introduction
Type: Build Languages: Python Prerequisites: Phase 7 · 05 (Full Transformer), Phase 5 · 02 (Text Representation) Time: ~45 minutes
The Problem
In 2018 every NLP task — sentiment, NER, QA, entailment — trained its own model from scratch on its own labeled data. There was no pre-trained "understand English" checkpoint you could fine-tune. ELMo (2018) showed you could pre-train contextual embeddings with a bidirectional LSTM; it helped but did not generalize.
BERT (Devlin et al. 2018) asked: what if we took a transformer encoder, trained it on every sentence on the internet, and forced it to predict missing words from context on both sides? Then you fine-tune one head on your downstream task. Parameter efficiency was a revelation.
The result: within 18 months BERT and its variants (RoBERTa, ALBERT, ELECTRA) dominated every NLP leaderboard that existed. By 2020 every search engine, content moderation pipeline, and semantic-search system on earth had a BERT inside.
In 2026 encoder-only models are still the right tool for classification, retrieval, and structured extraction — they run 5–10× faster per token than decoders and their embeddings are the backbone of every modern retrieval stack. ModernBERT (Dec 2024) pushed the architecture to 8K context with Flash Attention + RoPE + GeGLU.
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