Transformer layer in NLP – Layman’s terms
🔷 What are Transformer Layers?
Each layer in a Transformer is like a stage in a mental process — every stage helps a token (word) understand more about its meaning in the sentence.
A typical model like BERT-base has 12 layers, GPT-3 has 96+ layers, and each layer repeats the same process (self-attention + feed-forward), just with deeper understanding.
🔁 What happens at each layer (in simple terms)?
Let’s take a word like “love” in the sentence:
“I love you”
Initially:
- “love” has a 768-D static embedding from the embedding matrix.
✅ Layer 1:
- It looks at the other words (“I” and “you”).
- Updates its vector to capture basic relationships like subject-verb-object.
- Result: a new 768-D vector for “love”, more informed by the sentence.
✅ Layer 2:
- It repeats this: looks around again, with more subtlety.
- Maybe it now understands tone, emotion, or idiomatic use.
- Again, it updates the vector → new 768-D vector.
This repeats for L layers, producing a new version of the vector each time.
📈 What happens numerically?
- At each layer, the vector for each token is:
- Passed through self-attention math → mixes info from other words.
- Passed through neural network math → refines meaning.
- Gets a new 768-D vector per token per layer.
- These vectors keep changing, layer by layer, until the model finishes all L layers.
💡 Analogy:
Imagine you’re reading a sentence 12 times.
The first time, you get a rough sense.
By the 12th read, you’ve caught every nuance.
Each read = one layer.
Each layer = deeper understanding.
