Universal Language Model Fine-tuning for Text Classification
Table 6: Validation error rates for ULMFiT with different variations of LM fine-tuning.
['LM fine-tuning', 'IMDb', 'TREC-6', 'AG']
S5SS0SSS0Px4 Impact of LM fine-tuning We compare no fine-tuning against fine-tuning the full model Erhan et al. Fine-tuning the LM is most beneficial for larger datasets. ‘Discr’ and ‘Stlr’ improve performance across all three datasets and are necessary on the smaller TREC-6, where regular fine-tuning is not beneficial.