Mastering WordPiece: The Algorithm Behind BERT & DistilBERT скачать в хорошем качестве

Mastering WordPiece: The Algorithm Behind BERT & DistilBERT

How do models like BERT handle words they've never seen before? The secret lies in WordPiece Tokenization. In this video, we take a deep dive into the subword tokenization algorithm invented by Google that solved the "Out-Of-Vocabulary" crisis in NLP. We will move beyond simple definitions and visualize exactly how WordPiece builds its vocabulary using a Likelihood-based scoring formula (distinct from BPE) and how it slices new text using a Greedy Longest-Match strategy. In this video, you will learn: • Why Word-Level and Character-Level tokenization failed. • The math behind the WordPiece training phase (calculating pair scores). • The difference between Training (Vocabulary Building) and Inference (Tokenizing). • Step-by-step walkthrough of tokenizing words like "hugs" vs. "bugs". • Comparison: WordPiece vs. Byte Pair Encoding (BPE).