Transfer learning, particularly models like Allen AI’s ELMO, OpenAI’s Open-GPT, and Google’s BERT allowed researchers to smash multiple benchmarks with minimal task-specific fine-tuning and provided the rest of the NLP community with pretrained models that could easily (with less data and less compute time) be fine-tuned and implemented to produce state of the art results. Optimizer & Learning Rate SchedulerĢ018 was a breakthrough year in NLP. I’ve also published a video walkthrough of this post on my YouTube channel! Contents The Colab Notebook will allow you to run the code and inspect it as you read through.The blog post includes a comments section for discussion.This post is presented in two forms–as a blog post here and as a Colab Notebook here. More broadly, I describe the practical application of transfer learning in NLP to create high performance models with minimal effort on a range of NLP tasks. ![]() In this tutorial I’ll show you how to use BERT with the huggingface PyTorch library to quickly and efficiently fine-tune a model to get near state of the art performance in sentence classification. ![]() See Revision History at the end for details. ![]() ![]() Revised on 3/20/20 - Switched to tokenizer.encode_plus and added validation loss. Chris McCormick About Membership Blog Archive Become an NLP expert with videos & code for BERT and beyond → Join NLP Basecamp now! BERT Fine-Tuning Tutorial with PyTorch
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