Performance of Transfer Learning Model vs. Traditional Neural Network in Low System Resource Environment
Recently, the use of pre-trained model to build neural network based on transfer learning methodology is increasingly popular. These pre-trained models present the benefit of using less computing resources to train model with smaller amount of training data. The rise of state-of-the-art models such as BERT, XLNet and GPT boost accuracy and benefit as a base model for transfer leanring. However, these models are still too complex and consume many computing resource to train for transfer learning with low GPU memory. We will compare the performance and cost between lighter transfer learning model and purposely built neural network for NLP application of text classification and NER model.
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Methods
Adam •
Attention Dropout •
BERT •
BPE •
Cosine Annealing •
Dense Connections •
Discriminative Fine-Tuning •
Dropout •
GELU •
GPT •
Layer Normalization •
Linear Layer •
Linear Warmup With Cosine Annealing •
Linear Warmup With Linear Decay •
Multi-Head Attention •
Residual Connection •
Scaled Dot-Product Attention •
SentencePiece •
Softmax •
Weight Decay •
WordPiece •
XLNet
