Podcast
Questions and Answers
What is the first step in processing text according to the description?
What is the first step in processing text according to the description?
- Implementing the self-attention mechanism
- Cutting the text into pieces called tokens (correct)
- Converting WordPiece tokens into embedding vectors
- Associating each token with an embedding vector
What does BERT use for tokenization?
What does BERT use for tokenization?
- WordPiece tokenization (correct)
- Character tokenization
- Syllable tokenization
- Sentence tokenization
What are embedding vectors associated with each token?
What are embedding vectors associated with each token?
- A predefined command
- A vector of real numbers (correct)
- A sequence of characters
- A binary representation
What does BERT use to create the Key, Query, and Value vectors?
What does BERT use to create the Key, Query, and Value vectors?
How many layers of attention does a complete BERT model use?
How many layers of attention does a complete BERT model use?
What do positional embeddings contain information about?
What do positional embeddings contain information about?
What is the purpose of adding positional embeddings to input embeddings?
What is the purpose of adding positional embeddings to input embeddings?
What does the non-linearity introduced by the softmax function allow in BERT?
What does the non-linearity introduced by the softmax function allow in BERT?
What does the large vector, obtained by concatenating the outputs from each head, represent?
What does the large vector, obtained by concatenating the outputs from each head, represent?
What do the 768 components in the contextualized embedding vector represent?
What do the 768 components in the contextualized embedding vector represent?
What does the use of 12 heads in BERT allow for?
What does the use of 12 heads in BERT allow for?
What do embeddings carry information about?
What do embeddings carry information about?
What does the attention mechanism calculate for every possible pair of embedding vectors in the input sequence?
What does the attention mechanism calculate for every possible pair of embedding vectors in the input sequence?
What happens to scaled values in the attention mechanism?
What happens to scaled values in the attention mechanism?
How are new contextualized embedding vectors created for each token?
How are new contextualized embedding vectors created for each token?
What do contextualized embeddings contain for a particular sequence of tokens?
What do contextualized embeddings contain for a particular sequence of tokens?
In what way do tokens with strong relationships result in contextualized embeddings?
In what way do tokens with strong relationships result in contextualized embeddings?
What are Key, Query, and Value vectors created through?
What are Key, Query, and Value vectors created through?
How can projections be thought of in the context of attention mechanisms?
How can projections be thought of in the context of attention mechanisms?
What does multi-head attention form by repeating the process with different projections?
What does multi-head attention form by repeating the process with different projections?
What are models free to learn in the context of projections for language tasks?
What are models free to learn in the context of projections for language tasks?
How many times can the same process be repeated with different projections forming multi-head attention?
How many times can the same process be repeated with different projections forming multi-head attention?
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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
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