How are you all Hope everyone is doing well in the eternal mercy of the Most Merciful Creator. Today we will discuss about What is Tokenization? I will try to have a brief informative discussion today
Tokenization, in the context of natural language processing and machine learning, refers to the process of converting a sequence of text into smaller chunks known as tokens. These tokens can be as short as letters or as long as words. The primary reason this process is important is that it helps machines understand human language by breaking it down into bite-sized chunks, which are easier to analyze.

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Imagine you are trying to teach a child to read. Instead of diving straight into complex passages, you'll start by introducing them to individual letters, then syllables, and finally whole words. In a similar vein, tokenization breaks down large chunks of text into more digestible and understandable units for machines.
The primary goal of tokenization is to represent text in a way that is meaningful to machines without losing its context, by converting text into tokens, algorithms can more easily recognize patterns. This pattern recognition is crucial because it makes it possible for machines to understand and respond to human input.
For example, when a machine encounters the word "running", it does not see it as a single entity but rather as a combination of tokens from which it can analyze and extract meaning.
Tokenization methods vary based on the granularity of text decomposition and the specific requirements of the task at hand. These methods can range from breaking the text into individual words to breaking them into letters or even smaller units. Here are the different types of tokenization:

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Word Tokenization: This method breaks text into individual words. This is the most common method and is particularly useful for languages ​​with clear word boundaries, such as English.
Character Tokenization: Here, the text is divided into individual characters. This approach is useful for languages ​​that do not have clear word boundaries or for tasks that require granular analysis, such as spelling corrections.
Subword tokenization : Balancing between word and character tokenization, this method divides text into units that may be larger than a single letter but smaller than a full word.
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