Base Model Selection for Finetuning via Preliminary Ranking
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Solution Overview
Problem
The selection of base models for finetuning in machine learning is challenging due to variability in target datasets, with some datasets being indifferent to intertraining, while others benefit significantly, making it difficult to identify appropriate base models for improved performance.
Innovation Solution
A system that ranks finetuned machine learning models based on performance over representative datasets, allowing for the selection of candidate models and pretrained models based on performance metrics, and determines whether a target dataset is sensitive to intertraining by comparing their performance, thereby optimizing base model selection for further finetuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple candidate models are finetuned and evaluated on target datasets to identify the best base model, then model selection accuracy is improved, but computational costs and time consumption increase significantly
Solution Approach 1:
The system performs preliminary ranking of candidate models using representative datasets before actual target dataset evaluation. This preliminary action creates a ranked list that guides subsequent finetuning, so only top-ranked models are fully evaluated on target datasets, reducing overall computational time while maintaining selection accuracy
Solution Approach 2:
The model selection process is segmented into multiple stages: initial ranking on representative datasets, selective finetuning of top candidates, and final evaluation on target datasets. This segmentation allows the system to process many candidate models efficiently by applying different evaluation depths to different models based on their ranking
2Reliability
If comprehensive evaluation of candidate models is performed on target datasets, then reliability of base model selection is improved, but computational resources and costs increase
Solution Approach 1:
The system applies partial evaluation to most candidate models (ranking on representative datasets) and reserves full comprehensive evaluation (finetuning on target datasets) only for top-ranked models. This partial action approach maintains reliability for the final selection while significantly reducing overall computational resource consumption
3Manufacturing precision
If the system finetunes and evaluates all candidate models on all target datasets, then selection precision is improved, but the complexity of the selection process increases
Solution Approach 1:
The evaluation process is made dynamic and adaptive based on model performance at each stage. Models that perform well in preliminary ranking receive more thorough evaluation, while those that perform poorly are eliminated early. This dynamic approach maintains high selection precision while reducing process complexity compared to uniform evaluation of all models
Data Source
AI summary
Systems and techniques that facilitate base model selection are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise a comparison component that that finetunes a pretrained machine learning model and one or more candidate models selected based on the ranking of the plurality of finetuned machine learning models on one or more target datasets, compares performance of the one or more candidate models to a defined performance metric, and selects a base model from the pretrained machine learning model and the one or more candidate models based on the performance of the one or more candidate models over the one or more target datasets.