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BERT: Reading in Both Directions (2018) – Explained Simply 📚 Part 00 – Abstract – Why This Paper Exists Before BERT, most “pre-trained” language models read text in one direction – left-to-right or right-to-left. That worked well for generating text, but it was a poor fit for understanding text, where meaning often depends on what comes before and after a word. BERT opens with a clean claim: if we pre-train a model to read both ways at once, we get language representations that transfer far better to real NLP tasks. We unpack what was limiting the 2018 status quo, what BERT changes in pre-training, and why its results made “fine-tune one model for everything” the new default. No maths required – just intuition and clear explanations. --- 📄 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 01 – Introduction – The Unidirectionality Problem 💬 Questions or feedback? Drop a comment below! #BERT #NLP #Transformers #MachineLearning #DeepLearning #AI