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NLP - Chapter 11 Questions and Answers 2024 $12.99   Add to cart

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NLP - Chapter 11 Questions and Answers 2024

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Exam of 2 pages for the course NLP at NLP (NLP - Chapter 11)

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  • October 31, 2024
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  • 2024/2025
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NLP - Chapter 11

Transformers decoder - answer takes its own previous output and the encoder's output
as input

BERT - answer Bidirectional Encoder Representations from Transformers

BERT Paper - answer Pré-training of deep bidirectional transformers for language
understanding

How are input embeddings calculated in BERT? - answer the sum of the token
embeddings, the segmentation embeddings, and the position embeddings

BERT_BASE and BERT_LARGE – answer BERT_BASE has 12 layers, hidden size of
768, and 110M total parameters. BERT_LARGE has 24 layers, hidden size of 1024,
and 340M total parameters

difference between Bert and Distilbert - answerDistilbert is a distilled version of Bert,
with a reduced number of layers, parameters, and weights. It aims to maintain 97% of
Bert's performance while reducing its size by 44%. Both use a vocabulary of 30K
subwords.

Bidirectional encoders - answerhave access to tokens both before and after the current
one, allowing for greater context understanding

masked language model (MLM) - answerInstead of next word prediction, we use a cloze
task where a word is masked and we ask what word fits best. BERT training

What is the purpose of the next sentence prediction (NSP) task in training BERT? -
answerThe NSP task determines if a given sentence naturally follows another sentence.

Explain how contextual embeddings are extracted from BERT. - answerwe feed input
tokens into a bidirectional self-attention model like BERT, and the output layer provides
the contextual vectors for each token.

pretraining - answerspend a long time training the LM on massive corpora; roughly 40
epochs (passes over full training set) minimum

domain-adaptive pretraining - answerafter pretraining, continue pretraining using a
domain-specific corpus

keep pretraining - answermany studies show that continuing pretraining beyond
standard limits still shows downstream benefits

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