What are the differences between BERT and ELMo?
BERT and ELMo are both NLP models, but they differ in architecture and training methods. BERT is bidirectional and uses transformers, while ELMo is based on LSTMs and provides contextual embeddings.
Can you explain how BERT's bidirectionality works?
Sure! BERT's bidirectionality means it looks at the context from both the left and right of a word simultaneously, allowing it to understand the meaning better than unidirectional models.
What are some use cases for BERT?
BERT is widely used for tasks like sentiment analysis, question answering, and named entity recognition due to its deep understanding of context.
Is ELMo still relevant in today's NLP?
While ELMo was groundbreaking, BERT and its successors have largely taken the lead in performance and versatility in NLP tasks.
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