BERT explained: Training, Inference, BERT vs GPT/LLamA, Fine tuning, [CLS] token

Umar Jamil · Beginner ·🧠 Large Language Models ·2y ago
Full explanation of the BERT model, including a comparison with other language models like LLaMA and GPT. I cover topics like: training, inference, fine tuning, Masked Language Models (MLM), Next Sentence Prediction (NSP), [CLS] token, sentence embedding, text classification, question answering, self-attention mechanism. Everything is visually explained step by step. I also review the background knowledge in order to understand BERT, by starting from an introduction to large language models (LLM) and the attention mechanism. Slides PDF: https://github.com/hkproj/bert-from-scratch BERT paper:…
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Chapters (17)

Introduction
2:00 Language Models
3:10 Training (Language Models)
7:23 Inference (Language Models)
9:15 Transformer architecture (Encoder)
10:28 Input Embeddings
14:17 Positional Encoding
17:14 Self-Attention and causal mask
29:14 BERT (overview)
32:08 BERT vs GPT/LLaMA
34:25 Left context and right context
36:36 BERT pre-training
37:05 Masked Language Model
45:01 [CLS] token
48:26 BERT fine-tuning
49:00 Text classification
50:50 Question answering
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