# Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!

StatQuest with Josh Starmer

Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!! - YouTube

[Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!](https://www.youtube.com/watch?v=GDN649X_acE) [StatQuest with Josh Starmer](https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw)

## Historical Context and Transformer Evolution

- **2017**: First Transformer had encoder + decoder parts connected for seq2seq translation  
  - Example: "pizza is great" → "La pizza es magnifico"  
  - Encoder crunches numbers to encode input, decoder decodes to translation  
- **Post-2017 discovery**: Both parts work independently  
  - Decoder-only → basis for ChatGPT (text generation)  
  - Encoder-only → basis for BERT and many other models  
- Decoder-only models gained popularity (ChatGPT, Gemini), overshadowing encoder-only  
- **Question**: Do people still use encoder-only Transformers? **Answer**: Yes, significant understated power

## Word Embeddings Fundamentals

- **Purpose**: Convert tokens (words, word bits, symbols) into numbers for neural networks  
- **Simple approach**: Assign random numbers to each word  
  - Problem: Similar words (great/awesome) get different numbers  
  - Neural network needs more complexity, can't transfer learning between similar words  
- **Better approach**: Similar words should get similar numbers  
  - Same word in different contexts needs multiple numbers  
  - Example: "great" positive vs sarcastic negative usage

## Neural Network Word Embedding Training

- **Simple neural network setup**:  
  - Input for each unique word  
  - Two activation functions → two word embeddings per word  
  - Weights initialized with random numbers  
  - Training data: "Pizza is great" and "pizza is awesome"  
- **Training objective**: Each word predicts next word  
  - Pizza → is  
  - is → great/awesome  
- **Result after training**: great and awesome cluster together (similar embeddings)  
- **More context improves embeddings**: Use multiple preceding words instead of just one  
  - Example: "the pizza came out" → "of"

## Positional Encoding

- **Problem**: Current approach ignores word order  
  - "the pizza came out of" = "pizza out came the of" (same inputs/outputs)  
- **Solution**: Positional encoding tracks word order  
- **Critical importance**: "Squatch eats pizza" vs "pizza eats Squatch"  
  - Same words, completely different meanings  
- **Multiple implementation methods** (details out of scope)

## Self-Attention Mechanism

- **Purpose**: Track relationships among words  
- **Example**: "the pizza came out of the oven and it tasted good"  
  - Word "it" could refer to pizza or oven  
  - Attention correctly associates "it" with "pizza"  
- **How it works**:  
  - Calculates similarity between each word and all other words (including itself)  
  - Uses similarities to determine encoding impact  
  - Words with higher similarity scores have larger impact on encoding

## Context-Aware Embeddings

- **Combination of three layers**:  
  - Word embedding (converts to numbers)  
  - Positional encoding (tracks word order)  
  - Self-attention (establishes word relationships)  
- **Result**: Context-aware/contextualized embeddings  
- **Capability**: Cluster similar sentences or documents (not just words)

## Applications

### Retrieval Augmented Generation (RAG)

- Break documents into text blocks  
- Create context-aware embeddings for each block  
- When given prompt "what is pizza", find most similar text chunks  
- **Foundation for document search and retrieval**

### Classification Tasks

- Use context-aware embeddings as inputs to neural networks  
- **Example**: Sentiment analysis (positive/negative sentiments about pizza on LinkedIn)  
- **Alternative**: Use embeddings in logistic regression for classification

## Key Advantages

- Encoder-only Transformers create versatile context-aware embeddings  
- Wide variety of applications beyond text generation  
- **Significant but understated power** compared to decoder-only models