BERT

Bidirectional Encoder Representations from Transformers

A pre-trained deep learning model developed by Google AI researchers for natural language understanding tasks. BERT is based on the Transformer architecture, which was introduced by Vaswani et al. in 2017, and it has significantly advanced the state-of-the-art in various natural language processing (NLP) tasks.

The key innovation in BERT is its bidirectional training approach, which allows the model to learn the context of a word from both its left and right sides simultaneously. This bidirectional context learning enables BERT to capture a more comprehensive understanding of the language and leads to improved performance on a wide range of NLP tasks, such as sentiment analysis, question-answering, and named entity recognition.

BERT is pre-trained on a large corpus of text, learning to generate context-based representations of words and phrases. After pre-training, BERT can be fine-tuned for specific NLP tasks with a smaller labeled dataset, making it a versatile and powerful tool for various language understanding applications. BERT’s success has spurred the development of many other Transformer-based models, such as GPT, RoBERTa, and T5, which have continued to advance the field of NLP.

• Abbreviation: BERT
• Source: Google BERT