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What is BERT good at?
BERT is undoubtedly a breakthrough in the use of Machine Learning for Natural Language Processing. The fact that it’s approachable and allows fast fine-tuning will likely allow a wide range of practical applications in the future.
Is BERT considered deep learning?
BERT, which stands for Bidirectional Encoder Representations from Transformers, is based on Transformers, a deep learning model in which every output element is connected to every input element, and the weightings between them are dynamically calculated based upon their connection.
Is BERT an attention model?
BERT also stacks multiple layers of attention, each of which operates on the output of the layer that came before. Through this repeated composition of word embeddings, BERT is able to form very rich representations as it gets to the deepest layers of the model.
What’s the difference between GPT and Bert in Elmo?
GPT was trained for 1M steps with a batch size of 32,000 words; BERT was trained for 1M steps with a batch size of 128,000 words. GPT used the same learning rate of 5e-5 for all fine-tuning experiments; BERT chooses a task-specific fine-tuning learning rate which performs the best on the development set.
Are there any pre trained models for Bert?
Google Research recently open-sourced the tensorflow implementation of BERT and also released the following pre-trained models: BERT-Base, Multilingual Cased (New, recommended): 104 languages, 12-layer, 768-hidden, 12-heads, 110M parameters BERT-Base, Chinese: Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M parameters
What kind of learning rate does Bert use?
GPT used the same learning rate of 5e-5 for all fine-tuning experiments; BERT chooses a task-specific fine-tuning learning rate which performs the best on the development set. Summary of various models, Figure: From Lil’Log by Lilian.