BERT-Base Uncased: Transformer for NLP Tasks

BERT is a transformer model pretrained on a vast English corpus using self-supervised learning. It learns from raw text without human labeling, leveraging an automated process to generate inputs and labels. This enables it to utilize large amounts of publicly available data for training

Masked Language Modeling (MLM) randomly masks 15% of words in a sentence and predicts them, allowing BERT to learn bidirectional representations. Unlike RNNs, which process words sequentially, and GPT, which masks future tokens, BERT sees the entire context. Next Sentence Prediction (NSP) trains BERT to determine if two concatenated sentences were originally consecutive, enhancing its understanding of text relationships.