BERT Explained Simply – Part 01 – The Unidirectionality Problem скачать в хорошем качестве

BERT Explained Simply – Part 01 – The Unidirectionality Problem

BERT: Reading in Both Directions (2018) – Explained Simply 📚 Part 01 – Introduction – The Unidirectionality Problem In this episode, we unpack the core limitation that held NLP back before BERT – unidirectional language modelling. Most pre-trained models in 2018 could only read left-to-right (or right-to-left). That’s fine for generating text, but it’s a poor fit for understanding text – because real meaning often depends on both what came before and what comes after a word. We’ll break down: Why one-direction reading creates a built-in “blind spot” Why this matters for tasks like QA, NLI, and sentiment The simple intuition behind why “reading both ways” changes everything No maths required – just clear examples and intuition. --- 📄 Paper: https://arxiv.org/abs/1810.04805 ✍️ Authors: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova 🎬 Playlist (start here):    • Attention Is All You Need (2017) – Transfo...   ➡️ Next: Part 02 – Related Work – The Landscape BERT Emerged From 💬 Questions or feedback? Drop a comment below! #BERT #NLP #Transformers #MachineLearning #DeepLearning #AI