Learn Before
Example of Tokenization into Words and Punctuation
A simple and straightforward approach to tokenization is to segment a text into individual English words and punctuation marks. For instance, given the text "I love the food here. It's amazing!", it can be broken down into the following sequence of tokens: .
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
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
Learn After
A piece of text is segmented into a sequence of smaller units by separating it into individual words and treating each punctuation mark as its own distinct unit. Given this method, which of the following options correctly represents the segmentation of the sentence: "She said, 'It's great!'"?
Applying Word and Punctuation Segmentation
Analyzing a Tokenization Function's Output