Understanding Tokenizers and Embeddings in Large Language Models

1. What is a Tokenizer?

A tokenizer converts natural language text into numeric IDs that a model can process. It maps words, subwords, or characters to unique integers, forming the first step of language model input.

1.1 How Tokenizers Work

Tokenizers look up each token in a vocabulary table and replace it with its integer ID. For example, the Chinese sentence “我喜欢学习” might be tokenized as:

“我” → 1

“喜欢” → 2, 3

“学习” → 4, 5

1.2 Types of Tokenizers

Word‑level tokenizer: treats each word as a token (suitable for languages with clear word boundaries).

Subword‑level tokenizer: splits text into frequent subword units using algorithms such as BPE or WordPiece.

Character‑level tokenizer: treats each character as a token, useful for languages like Chinese.

1.3 Why Tokenizers Are Needed

Convert text to numbers for model consumption.

Build a vocabulary that maps tokens to IDs.

Improve model generalization by handling rare or unseen words through subword segmentation.

1.4 Tokenizer Example (JavaScript)

npm install @lenml/tokenizers

import { fromPreTrained } from "@lenml/tokenizer-llama3";
const tokenizer = fromPreTrained();
const tokens = tokenizer.apply_chat_template([
  { role: "system", content: "你是一个有趣的ai助手" },
  { role: "user", content: "好好，请问怎么去月球?" }
]); // tokens is a number[]
console.log(tokens);
const chat_content = tokenizer.decode(tokens);
console.log(chat_content);

2. What is an Embedding?

An embedding maps the integer IDs produced by a tokenizer to dense vector representations that capture semantic relationships between tokens.

2.1 How Embeddings Work

During the embedding stage, each ID is looked up in an embedding matrix, yielding a fixed‑dimensional vector (e.g., 300‑dimensional). For the token sequence [1, 2, 3, 4, 5] the corresponding vectors might be:

“我” → [0.25, -0.34, 0.15, ...]

“喜欢” → [0.12, 0.57, -0.22, ...], [0.11, -0.09, 0.31, ...]

“学习” → [0.33, -0.44, 0.19, ...], [0.09, 0.23, -0.41, ...]

These vectors are learned during model training and encode semantic information.

2.2 Types of Embeddings

Word Embedding : static vectors such as Word2Vec or GloVe, where a word has the same vector in all contexts.

Contextual Embedding : dynamic vectors generated by models like BERT or GPT, varying with surrounding text.

2.3 Why Embeddings Are Needed

Capture semantic relationships between words (similar words have nearby vectors).

Provide continuous representations that are amenable to gradient‑based learning.

Compress high‑dimensional linguistic information into fixed‑size vectors for efficient processing.

2.4 Implementing an Embedding Layer with TensorFlow.js

npm install @tensorflow/tfjs

const tf = require('@tensorflow/tfjs');
const vocabSize = 10000; // size of the vocabulary
const embeddingDim = 300; // dimension of each embedding vector
const embeddingLayer = tf.layers.embedding({inputDim: vocabSize, outputDim: embeddingDim});
const tokenIds = tf.tensor([[1045, 2293, 4083]]); // batch of token IDs
const embeddings = embeddingLayer.apply(tokenIds);
embeddings.print(); // display the resulting embedding vectors

Note: TensorFlow.js performance differs between browser and Node.js backends; refer to the official API for details. For large‑scale vectorization tasks, consider using dedicated AI platform APIs for embedding generation.

3. Relationship Between Tokenizer and Embedding

In LLM pipelines, the tokenizer first converts raw text into a sequence of integer IDs. The embedding layer then transforms those IDs into dense vectors that the model can process and learn from.

4. Conclusion

Tokenizer and embedding are foundational steps in large language models. Understanding how they work, their different types, and why they are essential equips beginners with the knowledge needed to dive deeper into LLM research and applications.