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GenAI – WordPiece Tokenization
GenAI – Word Piece Tokenization Table Of Contents: What Is Word Piece Tokenization ? Characteristics Of Word Piece Tokenization. Advantages Of Word Piece Tokenization. Disadvantages Of Word Piece Tokenization. How Word Piece Tokenization Works ? Model Uses Word Piece Tokenization. (1) What Is Word Piece Tokenization ? (2) Meaning Of Maximize The Likelihood Of The Training Corpus . (3) Characteristics Of Word Piece Tokenization ? (4) What Is Likelihood Based Merging ? (5) How The Model Will Calculate The Likelihood Of The Merge ? (6) Word Piece Tokenization Requires Pre Tokenization.
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GenAI – Byte Pair Encoding
GenAI – Byte Pair Encoding Tokenization Table Of Contents: What Is Byte Pair Encoding. Intuition Behind Byte Pair Encoding. Why Use BPE Tokenization. Example Of BPE Tokenization. Benefits Of BPE Tokenization. Tools Used For BPE Tokenization. (1) What Is Byte Pair Encoding? (2) Simple Example Of Byte Pair Encoding. (3) Intuition Behind Byte Pair Encoding. (3) Why Use Byte Pair Encoding ? (4) How Byte Pair Encoding Works ? Step-by-Step Example of BPE: (5) How To Decide The Merge Rule In Byte Pair Encoding ? (6) Limitation Of BPE.
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GenAI – Character Level Tokenization
GenAI – Character Level Tokenization Table Of Contents: What Is Character Level Tokenization ? Why To Use Character Level Tokenization ? How It Works Step By Step ? Advantages Of Character Level Tokenization. Limitation Of Character Level Tokenization. Model Used For Character Level Tokenization. (1) What Is Character Level Tokenization ? (2) Advantages Of Character Level Tokenization . (3) Disadvantages Of Character Level Tokenization . (4) How does a character-level tokenizer — which breaks text into individual characters — still manage to form meaningful words or understand language? (5) How Character Level Tokenization Works ?
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GenAI – Word Level Tokenization
GenAI – Word Level Tokenization. Table Of Contents: What Is Word Level Tokenization? How words are separated (spaces, punctuation). Handling punctuation and special characters. Case sensitivity (e.g., “Apple” vs. “apple”) Handling contractions (e.g., “don’t” → “do” + “n’t”) Handling Hyphenated words (e.g., “state-of-the-art”) Handling Numbers, URLs, email addresses, emojis Applications Of Word Level Tokenization. Limitations Of Word Level Tokenization. Tools for Word Tokenization (1) What Is Word Level Tokenization ? (2) How Words Are Separated ? (3) Handling Punctuation and Special Characters. (4) Handling Case Sensitivity (e.g., “Apple” vs. “apple”) (5) Handling contractions (e.g., “don’t” → “do” + “n’t”) (6)
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GenAI – What Are Tokens In LLM ?
GenAI – Tokens In LLM Table Of Contents: What Are Tokens In LLM ? Definition Of Tokens. Examples Of Tokens. How Tokenization Works? Why Tokens Matter ? Tokens Vs Character Vs Words. Tokenization Techniques. Tokenization Tools. (1) What Is Tokenization ? (2) Definition Of Tokens. (3) Examples Of Tokens. (4) Why Tokens Matter ? (5) Tokens Vs Character Vs Words (6) What Is Token Id & How LLM Going To Use It ? (7) For The Same Word Will I Get Same Token Id ? (7) Different Tokenization Techniques. Word Level Tokenization. Character Level Tokenization. Sub-word Tokenization.(Most Popular) Byte Pair
