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Can you delineate what BERT is and the reasons for its efficacy? Also, could you sketch and explicate its architecture?

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Answers

Anonymous

4Strong
The BERT model is a pretrained language model, it is one of the first language models that was introduced, the framework is based on the paper "Attention is All you need". It takes a sequence as an input (usually text data) and outputs a context aware representation of the input data. The context aware means that the representation of each item in the sequence is calculated with respect to the other items in the sequence, and this is done by a mechanism called self attention. 
More specifically the BERT model consists of multiple attention blocks, where each attention blocks consists of: a propositional embedding, multiple self-attention heads, and a feed forward layer. The positional embedding applies a function to each element in the sequence so to inject position information of the item in the sequence. The self attention mechanism is essentially a weighted average, where for each item in the sequence, the weights are coming from how each item attend to the other items in the sequence, essentially how similar it is to the other items in the sequence. 
Bert is generally stronger than the its traditional counterparts such as Word2Vec and Glob, because it has contextual embedding. Moreover, it is trained in a large amount of data, therefore it is very strong in modeling language related tasks. Many NLP tasks such as sentiment analysis or part of speech tagging that required a lot of feature engineering now can be done using BERT. 

Anonymous

3.3Strong
BERT has a decoder architecture, and the main building are transformers with classification head at its end.
Compared to RNNs, this architecture addresses two problems: vanishing/exploding gradient issues and memory loss. The self attention mechanism inside the transformer allows the model to do an all-to-all comparison of the tokens.
The model is trained with a massive amount of text where certain words (tokens) are masked and the classifier head must predict the missing word.
  • Can you delineate what BERT is and the reasons for its efficacy? Also, could you sketch and explicate its architecture?
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  • What makes the BERT model a commendable choice in machine learning, and how would you describe its architecture?
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  • How does BERT stand out in natural language processing, and can you illustrate and interpret its architecture?
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  • What constitutes the BERT model's advantages, and how would you visualize and expound upon its underlying architecture?
  • What's the BERT model and why is it good? Draw out BERT's architecture and explain it.
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Interview question asked to Machine Learning Engineers interviewing at Apple, Quora, Avito and others: Can you delineate what BERT is and the reasons for its efficacy? Also, could you sketch and explicate its architecture?.