BERT NLP Tutorial 1- Introduction | BERT Machine Learning | KGP Talkie
In this video, I will explain the BERT research paper.
To understand transformers we first must understand the attention mechanism. The Attention mechanism enables the transformers to have extremely long term memory. A transformer model can “attend” or “focus” on all previous tokens that have been generated. Recurrent neural networks (RNN) are also capable of looking at previous inputs too. But the power of the attention mechanism is that it doesn’t suffer from short term memory. RNNs can theoretically access information arbitrarily far in the past, but in practice, they have a hard time keeping that information in their internal state.
BERT is designed to pre-train deep bidirectional representations from the unlabeled text by jointly conditioning on both left and right contexts in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.
Proper language representation is key for general-purpose language understanding by machines. Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary. For example, the word “bank” would have the same representation in “bank deposit” and in “riverbank”.Contextual models instead generate a representation of each word that is based on the other words in the sentence. BERT, as a contextual model, captures these relationships in a bidirectional way. BERT was built upon recent work and clever ideas in pre-training contextual representations including Semi-supervised Sequence Learning, Generative Pre-Training, ELMo, the OpenAI Transformer, ULMFit, and the Transformer. Although these models are all unidirectional or shallowly bidirectional, BERT is fully bidirectional. In this sentiment analysis with BERT for python video, you will learn various aspects of sentiment analysis. To improve this model you can try sentiment analysis using xlnet.
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