Fine-Tuning Transformers for NLP

In this tutorial, the process of fine-tuning two different transformer models, BERT and DistilBERT, for two NLP problems - sentiment analysis using Stanford Sentiment Treebank v2 (SST2) dataset and duplicate question detection using Quora Question Pairs (QQP) dataset is demonstrated. The HuggingFace Transformers Repo provides over 60 different network types including pre-trained models like DistilBERT and BERT, which are used in this tutorial. Training these models involves creating PyTorch datasets and dataloaders for the training and validation sets, defining a loss function, and implementing functions for model training/evaluation. The results show that fine-tuning pre-trained Transformers on downstream tasks can save significant time with performance often immediately high out of the box.

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