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🤖 BERT Breakdown

Bidirectional understanding that changed NLP forever

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Transformer Architecture

The Bidirectional Revolution

🎯 What is BERT?

BERT (Bidirectional Encoder Representations from Transformers) was introduced by Google in 2018. Unlike previous models that read text left-to-right or right-to-left, BERT reads in both directions simultaneously, giving it deeper contextual understanding.

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Key Innovation

Uses only the encoder from Transformers, trained with masked language modeling to understand context from both directions.

❌ Before BERT

  • •Unidirectional reading (left-to-right)
  • •Limited context understanding
  • •Separate models for different tasks
  • •Task-specific architectures needed

✅ With BERT

  • •Bidirectional context understanding
  • •Deep semantic comprehension
  • •Single model, multiple tasks
  • •Transfer learning via fine-tuning
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Search & QA

Google Search uses BERT to better understand search queries and context

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Classification

Sentiment analysis, intent detection, and content categorization

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Entity Recognition

Named entity recognition and relationship extraction

📈 Impact

BERT achieved state-of-the-art results on 11 NLP tasks, improving accuracy by 5-10% on many benchmarks. It established the pre-train + fine-tune paradigm that dominates modern NLP.