BERT is a transformer model that was developed by Google in 2018. What is BERT used for?It is used to diagnose and treat diseases.It is used to generate text, translate languages, and write different kinds of creative content.It is used to train other machine learning models, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks.It is used to solve many natural language processing tasks, such as question answering, text classification, and natural language inference.

What kind of transformer model is BERT?Recurrent Neural Network (RNN) encoder-decoder modelEncoder-only modelDecoder-only modelEncoder-decoder model

What is the name of the language modeling technique that is used in Bidirectional Encoder Representations from Transformers (BERT)?Recurrent Neural Network (RNN)TransformerLong Short-Term Memory (LSTM)Gated Recurrent Unit (GRU)

What is the primary advantage of using Bidirectional Encoder Representations from Transformers (BERT) in NLP?*1 pointIt can generate new text from scratchIt considers the context of a word from both directions.It reduces computational complexity.It focuses solely on syntactic parsing.

Which of the following NLP tasks can benefit from BERT-based models?*Stock market predictionSpeech synthesisSentiment analysisImage recognition

Which of the following is NOT a commonly used pre-trained language model for NLP tasks?Question 14Answera.BERT (Bidirectional Encoder Representations from Transformers)b.ELMO (Embeddings from Language Models)c.GPT (Generative Pre-trained Transformer)d.SVM (Support Vector Machine)