Slices_of_me

  Here are several ways in which tokenization can impact the accuracy of these models: 1.  Granularity of Tokens Word vs. Subword vs. Character Tokenization : The choice of tokenization method affects how the model interprets language. For instance, word tokenization may lose nuances in compound words or phrases, while subword tokenization (like Byte Pair Encoding or WordPiece) can handle rare words and morphological variations better. Character tokenization captures every detail but may lead to longer sequences that are harder for models to process effectively. Impact on Context : The granularity of tokens can influence how well the model understands context. For example, splitting "New York" into two tokens ("New" and "York") may lead to a loss of meaning, affecting the model's ability to understand references to the city. 2.  Handling of Special Cases Punctuation and Special Characters : How a tokenizer handles punctuation, special characters, and w...

  1. Natural Language Processing (NLP) Definition: Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. The goal of NLP is to enable machines to understand, interpret, and generate human language in a way that is both meaningful and useful. Key Components: Syntax:  The arrangement of words and phrases to create well-formed sentences. Semantics:  The meaning of words and phrases in context. Pragmatics:  The context in which language is used, including the social and cultural factors that influence meaning. Further Reading: Natural Language Processing - Wikipedia 2. Tokenization Definition: Tokenization is the process of breaking down text into smaller units, called tokens. Tokens can be words, phrases, or even characters. This step is crucial in NLP as it allows the system to analyze and process the text more effectively. Example: In the sentence "I love p...