BERT Skill
BERT, or Bidirectional Encoder Representations from Transformers, is a pre-trained natural language processing (NLP) model developed by Google. Introduced by researchers at Google AI in 2018, BERT has significantly advanced the field of language understanding by capturing contextual relationships and nuances in text.BERT belongs to the transformer architecture, a type of neural network architecture that has proven highly effective in various NLP tasks. What sets BERT apart is its bidirectional approach to understanding words in a sentence, considering both left and right context. This bidirectional understanding enables BERT to capture intricate dependencies and meanings in language.BERT is pre-trained on large amounts of textual data and can be fine-tuned for specific NLP tasks such as sentiment analysis, named entity recognition, and question answering. The pre-trained model is available in different sizes, from base to large, depending on the computational resources available and the specific application requirements.Due to its exceptional performance in a wide range of language understanding tasks, BERT has become a cornerstone in NLP research and applications. It has been adopted by various industries, including search engines, chatbots, and sentiment analysis tools, contributing to the development of more accurate and context-aware natural language processing systems.
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