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Recently, the use of pre-trained model to build neural network based ontransfer learning methodology is increasingly popular. These pre-trained modelspresent the benefit of using less computing resources to train model withsmaller amount of training data. The rise of state-of-the-art models such asBERT, XLNet and GPT boost accuracy and benefit as a base model for transferleanring. However, these models are still too complex and consume manycomputing resource to train for transfer learning with low GPU memory. We willcompare the performance and cost between lighter transfer learning model andpurposely built neural network for NLP application of text classification andNER model.

Performance of Transfer Learning Model vs. Traditional Neural Network in Low System Resource Environment