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ALBERT
A Lite BERT for Self-supervised Learning of Language Representations.
ALBERT
ALBERT (A Lite BERT) was developed to solve the "parameter explosion" problem. It's designed to be much lighter to store while staying just as smart as BERT.
Level 1 — Sharing is Caring
In a normal BERT model, every layer has its own unique weight. In ALBERT, all layers share the exact same weights. This reduces the number of parameters by a massive amount.
Level 2 — Parameter Reduction Secrets
- Factorized Embedding: Separates vocabulary size from hidden layer size.
- Cross-layer Parameter Sharing: All Transformer layers are identical.
- SOP (Sentence Order Prediction): A harder version of BERT's NSP that makes the model learn better logic.
Level 3 — Performance vs Memory
ALBERT-XXLarge is smarter than BERT-Large but significantly harder to train because even though it has fewer stored parameters, it still does the same amount of computation.
# BERT-Base: 110M Params
# ALBERT-Base: 12M Params (9x reduction!)
from transformers import pipeline
albert_qa = pipeline("question-answering", model="albert-base-v2")