BERT (Bidirectional Encoder Representations from Transformers) is a deep learning model developed by Google that revolutionized natural language processing (NLP). It is a large-scale, pre-trained model that learns contextual relationships by analyzing text bidirectionally, meaning it considers the context of words both before and after a target word.
The training process for BERT involves two stages:
- Pre-training: The model is trained on massive amounts of unannotated text data to learn language representations.
- Fine-tuning: The pre-trained model is then adapted to specific NLP tasks using smaller, task-specific datasets, enabling it to perform tasks like question answering, sentiment analysis, and text classification with high accuracy.
BERT’s bidirectional approach allows it to capture nuanced word meanings and improve NLP capabilities across a wide range of applications.