Do-BERT

# Summary

The `undefined` website discusses Do-BERT, an advancement in NLP that builds upon the Transformer architecture, offering a novel approach to language modeling with its bidirectional nature.

# Abstract

The `undefined` website delves into the intricacies of Do-BERT, a cutting-edge NLP model that has significantly impacted the field with its bidirectional understanding of language context. BERT, which stands for Bidirectional Encoder Representations from Transformers, was developed by Jacob Devlin and his team at Google in 2018. It represents a shift from traditional sequential models like RNNs and LSTMs, which were limited in their ability to retain information over long sequences. BERT's architecture is based on the Transformer model introduced in the paper "Attention Is All You Need," which emphasizes the importance of attention mechanisms in understanding language. The website provides a comprehensive overview of BERT through a sketchnote and references various authoritative sources and guides for users interested in exploring BERT further.

# Opinions

- Traditional methods like RNNs and LSTMs were once prevalent in language modeling but were insufficient for capturing long-range dependencies in text.
- The Transformer architecture, particularly the attention mechanism, is deemed revolutionary for language understanding.
- BERT's bidirectional approach to pre-training is considered a significant leap forward in NLP, providing a more nuanced understanding of language context.
- The references listed on the website suggest a curated selection of resources that are highly regarded for understanding BERT and related concepts in NLP.

Do-BERT

(Image source: Pixabay)

BERT (Bidirectional Encoder Representations from Transformers) has taken the world of NLP (Natural Language Processing) by storm.

Language-text is essentially a sequence of words. So, traditional methods like RNNs (Recurrent Neural Networks) and LSTMs (Long Short Term Memory) used to be ubiquitous in Language Modeling (predicting next word. Remember, typing SMS?). But they would not remember previous words a bit far away. Then came ‘Attention is All you need’ and its architecture called, `Transformer’.

BERT is a Transformer-based machine learning technique for NLP pre-training developed by in 2018 by Jacob Devlin and his colleagues from Google.

Following sketchnote gives overview of BERT:

References

“Transformer: A Novel Neural Network Architecture for Language Understanding” — Google AI Blog (link)

“A Visual Guide to Using BERT for the First Time” — Jay Alammar (link)

“The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)” — Jay Alammar (link)

“The Illustrated Transformer” — Jay Alammar (link)

“Explaining BERT Simply Using Sketches” — Rahul Agarwal (link)

“Attention Is All You Need” — Ashish Vaswani et al. (link)

Originally published at LinkedIn