Embeddings Quiz

Quiz covering Transformers and Attention

Questions in this quiz

A preview of the 5 questions covered. Start the quiz above to answer them, check your score, and read the explanations.

1. 1

   In the context of NLP, what is the primary purpose of an embedding vector?

2. 2

   Why is 'cosine similarity' commonly used when comparing two word embeddings?

3. 3

   What happens if two words appear in similar contexts during the training of an embedding model like Word2Vec?

4. 4

   In Transformer-based models, what is the purpose of 'Positional Encodings' added to token embeddings?

5. 5

   If you are using a pre-trained embedding model and notice that the model fails to represent the nuances of domain-specific jargon (e.g., medical or legal terms), what is the most technically sound way to resolve this?