What kind of transformer model is BERT?Recurrent Neural Network (RNN) encoder-decoder modelEncoder-only modelDecoder-only modelEncoder-decoder model
Question
What kind of transformer model is BERT?
- Recurrent Neural Network (RNN) encoder-decoder model
- Encoder-only model
- Decoder-only model
- Encoder-decoder model
Solution
BERT (Bidirectional Encoder Representations from Transformers) is an encoder-only model. Unlike traditional models that use RNNs (Recurrent Neural Networks) or older transformer architectures that include both encoders and decoders, BERT operates solely with the encoder part of the transformer architecture. It is designed to understand the context of a word by looking at the words that come before and after it (bidirectional context), making it particularly effective for tasks like sentence classification, named entity recognition, and more.
In summary, BERT is not an RNN encoder-decoder model nor a decoder-only model; it strictly uses the encoder architecture to process input text.
Similar Questions
what are the advantages of using transformer networks over RNNs in the field of natural language processing with deep learning?
Question 7Which transformer-based model architecture is well-suited to the task of text translation?1 pointSequence-to-sequenceAutoencoderAutoregressive
Which of the following NLP tasks can benefit from BERT-based models?*Stock market predictionSpeech synthesisSentiment analysisImage recognition
The ______________ mechanism in transformers allows for capturing relationships between all words in a sequence simultaneously, rather than sequentially.
This model archictecture has multiple attention layers.Deep-Q modelsSSL modelsTransformers
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.