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BERT

BERT is a revolutionary bidirectional transformer model that set new standards in NLP through pre-training and fine-tuning.

intermediate2-4 weeks4 steps

The play

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.
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.
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.
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.

Starter code

Start by reading the original BERT paper: 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'.

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