BERT and GPT Models

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Here’s a clear, well-organized breakdown of BERT and GPT Models — ideal for notes, presentations, or a quick concept refresh:

🤖 BERT vs. GPT Models in NLP

📌 Overview

Both BERT and GPT are Transformer-based models that have transformed natural language processing — but they serve different purposes and use different architectures.

🧠 BERT (Bidirectional Encoder Representations from Transformers)

🔍 What is BERT?

• Developed by Google AI in 2018
• Uses only the encoder part of the Transformer architecture
• Trained to understand the context of a word in both directions (left and right)

🏗️ Architecture

• Bidirectional attention: sees the whole sentence at once
• Pre-trained on:
  • Masked Language Modeling (MLM): Randomly masks words and predicts them
  • Next Sentence Prediction (NSP): Predicts if one sentence follows another

🔧 Use Cases

• Text classification
• Named entity recognition (NER)
• Question answering (e.g., SQuAD)
• Sentence similarity tasks

📌 Notable Variants

🧠 GPT (Generative Pretrained Transformer)

🔍 What is GPT?

• Developed by OpenAI
• Uses only the decoder part of the Transformer
• Unidirectional: predicts the next word using only the previous words (left-to-right)

🏗️ Architecture

• Trained with causal (autoregressive) language modeling: learns to predict the next word in a sequence
• Fine-tuned for tasks like text generation, summarization, translation, etc.

🔧 Use Cases

• Text generation (stories, articles, code)
• Chatbots (like ChatGPT)
• Summarization and translation
• Creative writing

📌 Evolution

🔄 Key Differences at a Glance

🧪 Real-World Applications

• BERT: Search engines (Google), sentence analysis, medical text analysis
• GPT: ChatGPT, content generation, language translation, coding assistants

Would you like a comparison diagram or a side-by-side code example using BERT and GPT?