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Developers interested in a powerful large language model
About BERT
BERT is a large language model and a method of pre-training language representations. Pre-training refers to how BERT is first trained on a large source of text, such as Wikipedia. You can then apply the training results to other Natural Language Processing (NLP) tasks, such as question answering and sentiment analysis. With BERT and AI Platform Training, you can train a variety of NLP models in about 30 minutes.
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"BERT Implementation" Posted 2024-08-27
Pros: When BERT model implemented on stress detection use case, BERT as it handles context of the text was easily able to identify negation sentence like detecting "I am NOT happy" as a stressful text which was not happening in other models like logistic regression, decision tree, random forest, multinomial naive bayes, CNN, RNN, LSTM etc.
Cons: difficulty in finding a suitable multilingual datastet to train the model for both hind and english use cases.
Overall: BERT provided results with high accuracy. BERT allowed flexibility to cdeo and handle edge cases better.
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