Transformers
Understand the attention mechanism and transformer architecture powering modern NLP.
2 min read
The Transformer Revolution#
Transformers replaced RNNs for NLP. They process all tokens in parallel using attention, not sequential processing.
Key Innovation
"Attention Is All You Need" (2017) - no recurrence, no convolution, just attention.
Self-Attention#
Each token attends to all other tokens:
python
# Simplified attention
def attention(Q, K, V):
scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(d_k)
weights = torch.softmax(scores, dim=-1)
return torch.matmul(weights, V)
Transformer Architecture#
Embedding + Position
Convert tokens to vectors, add position info
Multi-Head Attention
Multiple attention mechanisms in parallel
Feed Forward
Process each position independently
Repeat N times
Stack encoder/decoder layers
Using Transformers (Hugging Face)#
python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModel.from_pretrained("bert-base-uncased")
inputs = tokenizer("Hello world!", return_tensors="pt")
outputs = model(**inputs)
Famous Transformer Models#
| Feature | Model | Type | Use Case |
|---|---|---|---|
| BERT | Encoder | Understanding, classification | |
| GPT | Decoder | Text generation | |
| T5 | Encoder-Decoder | Any text-to-text task |
Key Takeaways#
Remember
Transformers dominate NLP. Use pretrained models (BERT, GPT) and fine-tune for your task. Attention allows parallel processing and captures long-range dependencies.
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