Question Answering | NLP | QA | Tranformer | Natural Language Processing | Python | Theory | Code

Question & Answering! Looking to develop a model that can provide answers to any question you have? Well, in this video, I cover the high level overview on the architecture of QA Models (based on BERT). I also go into depth on what QA Modeling is, how it can be applied, and how it is used in the real world. Lastly, I cover the pretraining and fine-tuning phases of the QA Modeling process. Feel free to support me! Do know that just viewing my content is plenty of support! 😍 ☕Consider supporting me! ☕ Watch Next? BERT → Transformers → Resources Huggingface: 🔗 My Links 🔗 Github: My Website: Github Repository for Notebooks! Answering Modeling 📓 Requirements 🧐 Understanding of Python Google Account ⌛ Timeline ⌛ 0:00 - Categories of Question & Answering 3:20 - Additional Resources for Question & Answering 4:05 - Architecture and Backend of RoBERTa QA 5:12 - Implementation of Extractive QA (RoBERTa) 6:00 - Transfer Learning (Out of the Box Predictions) 8:45 - RoBERTa Architecture & Fine-Tuning QA Model via CLI 10:00 - Fine-Tuning QA Model with Libraries 13:15 - Pre-Training QA Model 🏷️Tags🏷️: Python,Natural Language Processing, BERT, Question and Answering, QA, Question, Answering, Tutorial, Machine Learning, Huggingface, Google, Colab, Google Colab, Chatbot, Encoder, Decoder, Neural, Network, Neural network, theory, explained, Implementation, code, how to, deep, learning, deep learning, tasks, QA, Q&A, Extractive, Abstractive, Extractive QA, Abstractive QA, 🔔Current Subs🔔: 3,220