Transformers are a family of neural-network architectures Transformers are a family of neural-network architectures used in modern AI systems. Most of them are implemented in Hugging Face Transformers , and many popular models such as BERT , GPT-2 , RoBERTa , and LLaMA are based on them.
Below is a priority-wise list of major Transformer architectures , with explanations and typical uses.
1️⃣ Encoder–Decoder Architecture (Most Complete Transformer)
Example models
Structure
Input Text
↓
Encoder
↓
Decoder
↓
Generated Output
How it works
Encoder understands the input.
Decoder generates output token-by-token.
Uses
Machine translation
Summarization
Question answering
Text generation
Chat systems
Example
Input: Translate English to French
Output: Bonjour le monde
2️⃣ Decoder-Only Architecture (Modern LLMs)
Example models
Structure
Prompt
↓
Transformer Decoder
↓
Next Token Prediction
↓
Generated Text
How it works
The model predicts the next word repeatedly .
Uses
Chatbots
Code generation
Story writing
reasoning AI
conversational agents
Example
Prompt: Explain SQL injection
Output: SQL injection is a web security vulnerability...
This architecture powers most modern AI assistants .
3️⃣ Encoder-Only Architecture
Example models
Structure
Text
↓
Encoder Layers
↓
Embedding Representation
↓
Task Head
Uses
Transformers are a family of neural-network architectures | Tech3Space Docs | tech3space App
Text classification
sentiment analysis
vulnerability detection
information retrieval
embeddings
Input: "SQL injection detected"
Output: Attack
Your model RobertaForSequenceClassification belongs to this category.
4️⃣ Encoder + Classification Head
BERT + classifier
RoBERTa + classifier
Text
↓
Encoder
↓
[CLS] token
↓
Linear layer
↓
Label
Uses
spam detection
cybersecurity attack classification
document categorization
intent detection
Input: phishing email detected
Output: Phishing
5️⃣ Token Classification Architecture
BertForTokenClassificationRobertaForTokenClassification Sentence
↓
Encoder
↓
Token-level predictions
Uses
Named Entity Recognition (NER)
malware indicators
extracting IP addresses
Text:
"Attack from IP 192.168.1.10"
Output:
192.168.1.10 → IP_Address
6️⃣ Question Answering Architecture Context + Question
↓
Encoder
↓
Start + End Token Prediction
Uses
knowledge extraction
document QA
search engines
Question: What is SQL injection?
Answer: A vulnerability allowing database manipulation.
7️⃣ Masked Language Model (MLM) Text with masked words
↓
Predict missing token
Input: SQL injection is a [MASK] attack
Output: web
Uses
pretraining models
language understanding
8️⃣ Causal Language Model Prompt
↓
Predict next token
↓
Generate sequence
Input: "Cybersecurity is important because"
Output: "it protects systems from attacks..."
9️⃣ Embedding Models
sentence transformers
BERT embeddings
Text
↓
Encoder
↓
Vector representation
Uses
search engines
RAG systems
semantic similarity
🔟 Vision Transformers (ViT) Image
↓
Patch embeddings
↓
Transformer
↓
Prediction
Uses
image classification
object detection
computer vision
Priority Ranking (Most Important Today) Priority Architecture Used For 1 Decoder-Only ChatGPT-style AI 2 Encoder-Decoder Translation / summarization 3 Encoder-Only classification / embeddings 4 Classification Head detection tasks 5 Token Classification entity extraction 6 Question Answering document QA 7 Masked LM pretraining 8 Causal LM text generation 9 Embedding models vector search 10 Vision Transformer images
For Your Cybersecurity Project Best architecture combination:
Input security log
↓
RoBERTa classifier
↓
Attack type
↓
Vector search (FAISS)
↓
LLM explanation
This hybrid system is used in AI-powered threat intelligence platforms .
✅ If you want, I can also show you a complete map of 40+ transformer architectures used in AI today , including DeBERTa, Mistral, Falcon, Gemma, Mixtral, and others , and explain which ones are best for research and projects.