Transformer Embeddings: Converting Words into Numbers That AI Can Understand

RAHUL KUMAR
Aug 20
4 min read

Imagine trying to explain the concept of "love" to a computer. How would you do it? Computers only understand numbers, not the emotional richness of human language. This is where transformer embeddings come to the rescue — they act as a bridge between human language and machine understanding, converting words into numerical representations that capture their meaning and context.

What Are Transformer Embeddings?

Transformer embeddings are numerical vector representations of text that capture the semantic meaning and relationships between words. Unlike traditional word representations that assign a single fixed value to each word, transformer embeddings are context-aware, meaning the same word can have different numerical representations depending on the surrounding words.baeldung+2

Think of embeddings as a sophisticated translation system. When you read the word "bank," your brain automatically understands whether it refers to a financial institution or the side of a river based on the context. Transformer embeddings work similarly — they create different numerical patterns for "bank" in "bank account" versus "river bank".milvus+1

The Building Blocks: From Words to Vectors

Step 1: Tokenization — Breaking Down Language

Before any embedding magic happens, text must be broken down into smaller units called tokens. These tokens can be complete words, parts of words (subwords), or even individual characters, depending on the tokenizer used.rahullokurte

Original Text	Tokens	Token IDs
"I love AI"	["I", "love", "AI"]	baeldung+1
"The cat sat"	["The", "cat", "sat"]	linkedin

Step 2: Token to Vector Conversion

Each token ID is then converted into a dense vector through an embedding matrix. This matrix, typically containing hundreds or thousands of dimensions, is learned during training to capture meaningful relationships between words.baeldung+1

For example, if we have a vocabulary of 50,000 words and choose 768 dimensions for our embeddings, our embedding matrix would be 50,000 × 768 in size. Each row represents one word's numerical pattern.linkedin+1

Step 3: Adding Positional Information

Since transformers process all words simultaneously (unlike humans who read sequentially), they need to understand word order. Positional encodings are added to token embeddings to preserve the sequence information.machinelearningmastery+1

The original transformer paper introduced a clever mathematical approach using sine and cosine functions to create unique positional patterns:

Even dimensions: sin⁡(position/100002i/d)\sin(\text{position}/10000^{2i/d})sin(position/100002i/d)
Odd dimensions: cos⁡(position/100002i/d)\cos(\text{position}/10000^{2i/d})cos(position/100002i/d)

This ensures that "dog bites man" has a different meaning than "man bites dog".rahullokurte+1

Types of Transformer Embeddings

Static vs. Dynamic Embeddings

Feature	Static Embeddings (Word2Vec, GloVe)	Dynamic Embeddings (Transformers)
Context Awareness	Fixed representation per worddev+1	Changes based on surrounding wordsmilvus+1
Example	"bank" always has same vectordeeplearning	"bank" differs in "river bank" vs "bank loan"milvus
Training Speed	Faster to computereddit	More computationally intensivespacy
Performance	Good for basic tasksspacy	Superior for complex language understandingmilvus

Fixed vs. Learned Positional Embeddings

Transformers can use two approaches for positional information:niser+1

Fixed Positional Embeddings: Use mathematical functions (sine/cosine) that don't change during training. These generalize well to longer sequences than seen during training.ibm

Learned Positional Embeddings: Treat position information as trainable parameters, allowing the model to learn optimal positional representations for specific tasks.niser

The Magic Behind Embedding Dimensions

Modern transformer models typically use embedding dimensions ranging from 384 to 1024. Popular models include:milvus

Small models: 384 dimensions (all-MiniLM-L6-v2)
Standard models: 768 dimensions (BERT, GPT-2)
Large models: 1024+ dimensions (larger GPT variants)

The choice represents a trade-off between performance and efficiency. Smaller embeddings are faster and require less memory, while larger embeddings can capture more nuanced semantic relationships.milvus

How Embeddings Capture Meaning

Semantic Relationships

Well-trained embeddings capture fascinating relationships. For example, the mathematical relationship "king - man + woman ≈ queen" emerges naturally from the training process. Words with similar meanings cluster together in the high-dimensional space.developers.google +1

Context Sensitivity

Unlike older approaches, transformer embeddings adjust their representations based on context. The word "bright" will have different numerical patterns in:milvus

"The bright student solved the problem" (intelligent)
"The bright light hurt my eyes" (luminous)

Real-World Applications

Transformer embeddings power many technologies you use daily:

Search engines: Understanding query intent and matching relevant contenthuggingface
Chatbots and virtual assistants: Comprehending natural language requestshuggingface
Translation services: Capturing meaning across different languagesbaeldung
Content recommendation: Finding similar articles or productshuggingface

Getting Started: A Simple Example

Here's how transformer embeddings work in practice using Python:

python

import torch import torch.nn as nn # Create an embedding layer vocab_size = 10000 # Number of unique words embedding_dim = 512 # Size of each embedding vector embedding_layer = nn.Embedding(vocab_size, embedding_dim) # Convert token IDs to embeddings token_ids = torch.tensor([1, 15, 247]) # "I love AI" embeddings = embedding_layer(token_ids) # Result: 3 words × 512 dimensions each print(embeddings.shape) # torch.Size([3, 512])

Each word is now represented as a 512-dimensional vector that captures its meaning and can be processed by the transformer model.linkedin

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