Transformers vs Sentence Transformers: Understanding the Difference

In modern natural language processing (NLP), the term Transformer is everywhere. But if you’ve been working with semantic search, embeddings, or retrieval-based systems, you’ve probably come across Sentence Transformers as well. While they are related, they serve different purposes. This article explores their differences, use cases, and practical examples, including why Sentence Transformers simplify working with tokenization and models.

What is a Transformer?

A Transformer is a neural network architecture introduced in 2017 in the seminal paper “Attention Is All You Need”. It forms the backbone of most modern NLP models such as:

• BERT

• GPT series

• RoBERTa

• T5

• LLaMA

Key Features

• Self-Attention Mechanism: Allows the model to understand relationships between all tokens in a sequence simultaneously.

• Token-level Embeddings: Outputs a vector for each token in the input.

• Flexible Architecture: Supports encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5) variants.

• Applications: Text classification, Named Entity Recognition (NER), language modeling, text generation.

Example: Token-Level Embeddings Using BERT

from transformers import AutoTokenizer, AutoModel
import torch

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModel.from_pretrained("bert-base-uncased")

sentence = "I love AI"
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)

print(outputs.last_hidden_state.shape)

Output: (1, 4, 768) → Each token gets a 768-dimensional embedding.

What is a Sentence Transformer?

A Sentence Transformer is a specialized model built on top of Transformer architectures, fine-tuned to produce semantic sentence embeddings.

• Purpose: Convert entire sentences or paragraphs into a single vector.

• Key Use Cases: Semantic search, text similarity, clustering, retrieval-augmented generation (RAG).

• Popular Models: all-MiniLM-L6-v2, paraphrase-mpnet-base-v2.

How It Works

1. Transformer Encoder converts tokens into contextual embeddings.

2. Pooling Layer combines token embeddings into a fixed-size vector.

3. Fine-Tuning with contrastive learning ensures semantically similar sentences are close in vector space.

Example: Sentence Embeddings

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
sentences = [
    "How do I reset my password?",
    "What is the process to change my account password?"
]
embeddings = model.encode(sentences)
print(embeddings.shape)

Outputs one 384-dimensional vector per sentence.

Why Not Just Use Transformers?

While BERT or GPT can produce token embeddings, using them directly for semantic similarity often fails because:

• CLS token embeddings are not optimized for similarity tasks.

• Averaging token embeddings gives poor semantic representation.

• Sentence Transformers are fine-tuned specifically to map semantically similar sentences close together.

Transformers vs Sentence Transformers

Feature	Transformer	Sentence Transformer
Level	Architecture	Application-level model
Output	Token embeddings	Sentence embeddings
Output Shape	(tokens, hidden)	(embedding_dim)
Training Objective	LM / MLM	Semantic similarity (contrastive/triplet loss)
Pooling	Not included	Included
Use Cases	Generation, NER, QA	Search, RAG, clustering
Library	transformers	sentence-transformers

AutoTokenizer & AutoModel vs SentenceTransformer

When using Hugging Face Transformers, you typically need:

1. AutoTokenizer → Converts text into token IDs

2. AutoModel → Processes IDs into embeddings or predictions

Example

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModel.from_pretrained("bert-base-uncased")

sentence = "Hello world"
inputs = tokenizer(sentence, return_tensors="pt")
outputs = model(**inputs)

• inputs: token IDs, attention masks

• outputs: embeddings per token

• You would then need manual pooling to get a sentence vector

SentenceTransformer Simplifies This

With Sentence Transformers, all steps are combined:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')
sentences = ["Hello world", "How are you?"]
embeddings = model.encode(sentences)

• Tokenization is handled automatically

• Model embeddings are computed

• Pooling applied internally

• Outputs ready-to-use sentence vectors

SentenceTransformer = AutoTokenizer + AutoModel + Pooling + Fine-tuning

Practical Use Cases

Transformers

• Text generation (chatbots)

• Token classification (NER, POS)

• Question answering (span prediction)

Sentence Transformers

• FAQ retrieval

• Semantic search in vector databases

• Duplicate detection

• RAG pipelines in LLMs

Visual Intuition

Transformer:

Sentence → Tokens → Transformer → Token embeddings

Sentence Transformer:

Sentence → Tokens → Transformer → Pooling → Sentence embedding

Conclusion

• Transformers are the core architecture for token-level NLP tasks and generation.

• Sentence Transformers are optimized for semantic similarity, providing one vector per sentence, and handle tokenization and pooling automatically.

If your project involves embeddings, semantic search, or connecting an LLM to a knowledge base, Sentence Transformers are the go-to choice.