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Tokenization Strategies
Consider the sentence: 'State-of-the-art models often struggle with out-of-vocabulary words.' Propose two different, valid ways this sentence could be broken down into a sequence of smaller units (tokens). For each method, briefly explain the reasoning or rule you applied.
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Data Science
Foundations of Large Language Models Course
Computing Sciences
Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
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Different standards for tokenization
Inference Process with a Fine-Tuned Model
Example of Tokenization into Words and Punctuation
Example of Word and Punctuation Tokenization
Methods of Tokenization
A language model is given the sentence: 'The researcher is studying neuroplasticity.' It processes the sentence using two different methods, resulting in two different sequences of tokens.
Method A:
['The', 'researcher', 'is', 'studying', 'neuroplasticity', '.']Method B:['The', 'researcher', 'is', 'study', 'ing', 'neuro', 'plasticity', '.']Assuming the model has never encountered the word 'neuroplasticity' during its training but has seen words like 'neuroscience' and 'plasticity' separately, which method is more advantageous for helping the model understand the new word, and why?
Tokenization Strategies
Evaluating Tokenization for a Specialized Chatbot