Word embedding and tokenization are fundamental steps in processing text data for machine learning models. Tokenization is the process of breaking down text into smaller pieces, called tokens, which could be words or subwords. Once the text is tokenized, word embedding comes into play to convert these tokens into numerical vectors that can be understood and processed by computers. Word embeddings are a way to turn words into numbers so that computers can work with them.
How it works
Imagine you have a bunch of words; we can place each word on a map where similar words are placed near each other. For example, on this map, “king” might be close to “queen,” and “cat” might be close to “dog.” We create this map by looking at how words are used together in sentences. If words often appear together, like “coffee” and “mug,” they will be near each other on the map. Each word gets its own unique spot on the map, which is described by a list of numbers (called a vector). We can then use these numbers to help computers understand the meaning of words and how they relate to each other, which is super helpful for things like making smart chatbots or translating languages. In other words The process of word embedding takes into account the semantic relationship between different words. For instance, words with similar meanings or usage are mapped close to each other in the map (vector space).
The process of word embedding
The process of word embedding is influenced by several factors, including the Context Window, Dimensionality, and Vocabulary Size the higher the better, but there exist the constraint of computational and memory cost. The Context Window is the range of words around a target word that the model looks at to understand the context. Dimensionality refers to the number of elements in the vector representing each word. On the other hand, Vocabulary Size is the total number of unique words or tokens that the model will learn embeddings. These factors are crucial in designing and training efficient models capable of understanding and generating human-like text.
ChatGPT learns word embedding
ChatGPT learns word embedding by processing vast amounts of text from various sources like books, social media, websites, and Reddit discussions and learns to represent words as vectors based on their contextual usage.
ChatGPT uses a clever method of tokenization, it breaks down words into smaller parts, much like breaking down a big puzzle into smaller pieces. For instance, it can split the word “banana” into smaller chunks like “ban” and “ana”. By doing this, ChatGPT can better grasp the meaning of words and how they relate to each other, which in turn helps in understanding and crafting sentences more effectively. This smart way of looking at words helps it understand and create sentences better. Want to see how ChatGPT breaks down your name or your sentence? Check it out https://platform.openai.com/tokenizer