Contents
What is cross layer parameter sharing?
In a Google AI blog, they say that “the network often learned to perform similar operations at various layers, using different parameters of the network. This possible redundancy is eliminated in ALBERT by parameter-sharing across the layers, i.e., the same layer is applied on top of each other.”.
How many parameters does Albert have?
31 million parameters
The paper shares the parameters for the whole block. Compared to the 110 million parameters of BERT-base, the ALBERT model only has 31 million parameters while using the same number of layers and 768 hidden units.
Is ALBERT faster than BERT?
The ALBERT xxlarge model performs significantly better than BERT large while it has 70% fewer parameters. This means that they can train faster than the BERT model. In fact, it’s about 1.7 times faster.
What is ALBERT vs BERT?
For Example, BERT base has 9x more parameters than the ALBERT base, and BERT Large has 18x more parameters than ALBERT Large. Dataset used: Similar to the BERT, ALBERT is also pre-trained on the English Wikipedia and Book CORPUS dataset which together contains 16 GB of uncompressed data.
Why do we share parameters?
Parameter sharing is used in all conv layer within the network. Parameter sharing reduces the training time; this is a direct advantage of the reduction of the number of weight updates that have to take place during backpropagation.
How does cross layer parameter sharing improve efficiency?
Cross-layer parameter sharing: The authors of this model also proposed the parameter sharing between different layers of the model to improve efficiency and decrease redundancy.
How many parameters does an Albert model have?
Compared to the 110 million parameters of BERT-base, the ALBERT model only has 31 million parameters while using the same number of layers and 768 hidden units. The effect on accuracy is minimal for embedding size of 128.
ALBERT solves this problem by factorizing the large vocabulary embedding matrix into two smaller matrices. This separates the size of the hidden layers from the size of the vocabulary embeddings. This allows us to grow the hidden size without significantly increasing the parameter size of the vocabulary embeddings.
How many parameters does a Bert Large Model have?
BERT-large, being a complex model, has 340 million parameters because of its 24 hidden layers and lots of nodes in the feed-forward network and attention heads. If you wanted to build upon the work on BERT and bring improvements to it, you would require large compute requirements to train from scratch and iterate on it.