22 Questions
What is the first step in processing text according to the description?
Cutting the text into pieces called tokens
What does BERT use for tokenization?
WordPiece tokenization
What are embedding vectors associated with each token?
A vector of real numbers
What does BERT use to create the Key, Query, and Value vectors?
Different projections
How many layers of attention does a complete BERT model use?
12
What do positional embeddings contain information about?
Position in the sequence
What is the purpose of adding positional embeddings to input embeddings?
To add information about the sequence before attention is applied
What does the non-linearity introduced by the softmax function allow in BERT?
Achieve more complex transformations of the embeddings
What does the large vector, obtained by concatenating the outputs from each head, represent?
Contextualized embedding vector per token
What do the 768 components in the contextualized embedding vector represent?
Information about the token's context
What does the use of 12 heads in BERT allow for?
Calculation of different relationships using different projections
What do embeddings carry information about?
Token meanings and allow mathematical operations for semantic changes
What does the attention mechanism calculate for every possible pair of embedding vectors in the input sequence?
Scalar product
What happens to scaled values in the attention mechanism?
They are passed through a softmax activation function, exponentially amplifying large values and normalizing them
How are new contextualized embedding vectors created for each token?
Through a linear combination of input embeddings
What do contextualized embeddings contain for a particular sequence of tokens?
A fraction of every input embedding
In what way do tokens with strong relationships result in contextualized embeddings?
Combining input embeddings in roughly equal parts
What are Key, Query, and Value vectors created through?
Linear projections with 64 components
How can projections be thought of in the context of attention mechanisms?
Focusing on different directions of the vector space, representing different semantic aspects
What does multi-head attention form by repeating the process with different projections?
Allowing each head to focus on different projections
What are models free to learn in the context of projections for language tasks?
Whatever projections allow it to solve language tasks efficiently
How many times can the same process be repeated with different projections forming multi-head attention?
Many times
Study Notes
Understanding BERT's Attention Mechanism
- Embeddings carry information about token meanings and allow mathematical operations for semantic changes
- Attention mechanisms, like scaled dot-product self-attention in BERT, enhance the representativeness of token values in the context of a sentence
- Attention mechanism calculates scalar product for every possible pair of embedding vectors in the input sequence
- Scaled values are passed through a softmax activation function, exponentially amplifying large values and normalizing them
- New contextualized embedding vectors are created for each token through a linear combination of input embeddings
- Contextualized embeddings contain a fraction of every input embedding for a particular sequence of tokens
- Tokens with strong relationships result in contextualized embeddings combining input embeddings in roughly equal parts
- Tokens with weak relationships result in contextualized embeddings nearly identical to the input embeddings
- Key, Query, and Value vectors are created through linear projections with 64 components, focusing on different semantic aspects
- Projections can be thought of as focusing on different directions of the vector space, representing different semantic aspects
- Multi-head attention forms by repeating the process with different Key, Query, and Value projections, allowing each head to focus on different projections
- The model is free to learn whatever projections allow it to solve language tasks efficiently, and the same process can be repeated many times with different projections forming multi-head attention
Test your knowledge of BERT's attention mechanism with this quiz. Explore how embeddings, attention mechanisms, and contextualized embeddings work together to enhance the representation of token values in a sentence.
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