🔥 Welcome to Half 4 of our LLM Nice-Tuning sequence! On this hands-on session, we have now applied wonderful tuning on BERT primarily based mannequin BERT base mannequin distilbert/distilbert-base-uncased for TEXT CLASSIFICATION job.
Now we have proven finish to finish working with our personal easy dataset with this straightforward mannequin to clarify all complicated & sophisticated wonderful tuning course of in easy phrases & simple to grasp rationalization.
📌 On this video, you’ll study:
✅ Recap of Atmosphere Setup on Google Colab
✅ Dataset Preparation
✅ Mannequin and Tokenizer Setup
✅ Information Preprocessing
✅ Coaching Configuration
✅ Analysis Metrics
✅ Initialize Coach
✅ Begin Coaching
✅ Analysis and Testing
✅ Saving and Loading the Mannequin
✅ Clarify Generate Output folders/information/configs and many others of Coaching Course of
In Subsequent half, we’re going to cowl
✅ 7 Totally different Testing methodologies for Testing FineTuned Mannequin to enusre effectiveness & accuracy of finetuned mannequin
✅ Troubleshooting Widespread Points on google colab if anybody faces
🐞 Reminiscence Points
🐞 Poor Efficiency
🐞 CUDA Out of Reminiscence
🔧 Whether or not you’re a newbie or an ML fanatic, this video will present you find out how to deliver your LLM fine-tuning concepts to life utilizing accessible instruments!
