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
- T5
- BART
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
- GPT-2
- LLaMA
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
- BERT
- RoBERTa
Structure
Text
↓
Encoder Layers
↓
Embedding Representation
↓
Task Head
Uses
- Text classification
- sentiment analysis
- vulnerability detection
- information retrieval
- embeddings
Example
Input: "SQL injection detected"
Output: Attack
Your model RobertaForSequenceClassification belongs to this category.
4️⃣ Encoder + Classification Head
Example models
- BERT + classifier
- RoBERTa + classifier
Structure
Text
↓
Encoder
↓
[CLS] token
↓
Linear layer
↓
Label
Uses
- spam detection
- cybersecurity attack classification
- document categorization
- intent detection
Example
Input: phishing email detected
Output: Phishing
5️⃣ Token Classification Architecture
Example models
BertForTokenClassificationRobertaForTokenClassification
Structure
Sentence
↓
Encoder
↓
Token-level predictions
Uses
- Named Entity Recognition (NER)
- malware indicators
- extracting IP addresses
Example
Text:
"Attack from IP 192.168.1.10"
Output:
192.168.1.10 → IP_Address
6️⃣ Question Answering Architecture
Example models
BertForQuestionAnswering
Structure
Context + Question
↓
Encoder
↓
Start + End Token Prediction
Uses
- knowledge extraction
- document QA
- search engines
Example
Question: What is SQL injection?
Answer: A vulnerability allowing database manipulation.
7️⃣ Masked Language Model (MLM)
Example models
- BERT
- RoBERTa
Structure
Text with masked words
↓
Predict missing token
Example
Input: SQL injection is a [MASK] attack
Output: web
Uses
- pretraining models
- language understanding
8️⃣ Causal Language Model
Example models
- GPT-2
- LLaMA
Structure
Prompt
↓
Predict next token
↓
Generate sequence
Example
Input: "Cybersecurity is important because"
Output: "it protects systems from attacks..."
9️⃣ Embedding Models
Example models
- sentence transformers
- BERT embeddings
Structure
Text
↓
Encoder
↓
Vector representation
Example output
[0.21, -0.44, 0.90, ...]
Uses
- search engines
- RAG systems
- semantic similarity
🔟 Vision Transformers (ViT)
Example
- Vision Transformer
Structure
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.