Tokenizers and Vector Embeddings

Tokenizers are essential components in natural language processing (NLP). They transform text into a format that machines can understand and process. Vector embeddings provide a numerical representation of this tokenized data.

Understanding Tokens:

A tokenizer breaks down text into smaller units called tokens. These tokens can be words, sub-words, or characters. For example, the sentence "Hello, world!" could be tokenized into:

• Tokens: "Hello", ",", "world", "!"

Vector Embeddings Explained:

Vector embeddings convert these tokens into numerical vectors. Each vector represents a token in a multi-dimensional space. The position of these vectors in the space reflects the semantic meaning of the tokens. Tokens with similar meanings are located closer to each other.

Process Overview:

1. Input: Text is fed into the tokenizer.

2. Tokenization: The tokenizer breaks the text into tokens.

3. Embedding: Each token is mapped to a corresponding vector.

4. Output: A sequence of vectors representing the input text.

JavaScript Example:

// Sample text
const text = "This is a sample sentence.";

// Simplified tokenizer (for demonstration)
function simpleTokenizer(text) {
  return text.toLowerCase().split(" ");
}

// Dummy embedding function (for demonstration)
function getEmbedding(token) {
  const embeddings = {
    "this": [0.1, 0.2, 0.3],
    "is": [0.4, 0.5, 0.6],
    "a": [0.7, 0.8, 0.9],
    "sample": [0.10, 0.11, 0.12],
    "sentence.": [0.13, 0.14, 0.15]
  };
  return embeddings[token] || [0, 0, 0]; // Return zero vector if token not found
}

// Tokenize the text
const tokens = simpleTokenizer(text);

// Generate embeddings for each token
const embeddings = tokens.map(token => getEmbedding(token));

// Output the embeddings
console.log(embeddings);

Explanation of the Code:

• simpleTokenizer: A basic function that splits the text into tokens based on spaces.

• getEmbedding: A function that simulates the embedding process. In a real-world scenario, this would involve a pre-trained model.

• The code tokenizes the input text, then retrieves a vector embedding for each token. The result is an array of vector embeddings.