Day 35 - BERT: Bidirectional Encoder Representations from Transformers

Introduction

Today's focus for Day 35 of the #75DaysOfLLM journey was on BERT (Bidirectional Encoder Representations from Transformers), a groundbreaking model that revolutionized NLP by introducing deep bidirectional language representations.

Introduction to BERT

BERT, developed by Google, marked a new era in NLP by learning context from both directions—left-to-right and right-to-left. This bidirectional approach allowed BERT to understand the full context of a word by looking at both its preceding and following tokens, unlike traditional unidirectional models.

Key Features of BERT

1. Bidirectional Training

BERT’s key innovation is its deep bidirectional training using transformers, allowing it to consider the context from both directions. This approach enables BERT to capture more nuanced information, making it highly effective for a variety of NLP tasks.

2. Masked Language Modeling (MLM)

Instead of traditional left-to-right language modeling, BERT uses Masked Language Modeling (MLM), where random words in the sentence are masked, and the model learns to predict these masked tokens based on context. MLM enables BERT to learn rich, contextual word representations.

3. Next Sentence Prediction (NSP)

BERT incorporates Next Sentence Prediction (NSP) to understand sentence-level relationships. In NSP, the model is trained to predict if two sentences follow each other in a document, enhancing its performance in tasks requiring an understanding of sentence order, like question answering and dialogue.

How BERT Works

1. Tokenization: BERT uses WordPiece tokenization, which breaks down words into subword units, enabling it to handle unknown words and rare word variations.
2. Embedding Layers: Input embeddings in BERT include token, segment, and position embeddings, providing the model with word-level, sentence-level, and positional information.
3. Transformer Layers: BERT uses multiple transformer layers to process tokens, extracting contextual representations from the input.

Applications of BERT

With its powerful context representation, BERT performs exceptionally well on NLP tasks such as:

• Question Answering: Achieving state-of-the-art results on benchmarks like SQuAD.
• Sentiment Analysis: Classifying sentiment with high accuracy due to its deep understanding of context.
• Named Entity Recognition (NER): Identifying entities with improved precision, benefiting from BERT’s token-level understanding.

Limitations and Considerations

• High Computational Demand: BERT’s large model size requires significant computational resources for training and inference.
• Fixed Input Length: BERT has a limited input length, making it less ideal for very long texts.

Conclusion

BERT set a new standard for NLP by introducing bidirectional context and reshaping model training through MLM and NSP. It remains a foundational model in NLP and serves as the backbone for many later innovations.