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Previous studies observed that finetuned models may be better base modelsthan the vanilla pretrained model. Such a model, finetuned on some sourcedataset, may provide a better starting point for a new finetuning process on adesired target dataset. Here, we perform a systematic analysis of thisintertraining scheme, over a wide range of English classification tasks.Surprisingly, our analysis suggests that the potential intertraining gain canbe analyzed independently for the target dataset under consideration, and for abase model being considered as a starting point. This is in contrast to currentperception that the alignment between the target dataset and the source datasetused to generate the base model is a major factor in determining intertrainingsuccess. We analyze different aspects that contribute to each. Furthermore, weleverage our analysis to propose a practical and efficient approach todetermine if and how to select a base model in real-world settings. Last, werelease an updating ranking of best models in the HuggingFace hub perarchitecture https://ibm.github.io/model-recycling/.

Where to start? Analyzing the potential value of intermediate models