BERT

4 Steps

1. 1

   Understand BERT's Architecture: BERT uses a multi-layer bidirectional Transformer encoder. Familiarize yourself with the Transformer architecture, focusing on the encoder part. Key components include self-attention mechanisms and feed-forward networks.

2. 2

   Explore Masked Language Modeling (MLM): BERT is pre-trained using MLM. Understand how random words in a sentence are masked, and the model is trained to predict these masked words based on the context.

3. 3

   Grasp Next Sentence Prediction (NSP): BERT is also pre-trained using NSP. Understand how the model learns to predict whether two given sentences are consecutive in the original document.

4. 4

   Fine-tuning BERT for Downstream Tasks: Learn how to fine-tune BERT for specific NLP tasks like text classification, question answering, and named entity recognition. This involves adding a task-specific output layer on top of the pre-trained BERT model and training it on task-specific data.