Fine Tuning vs Training From Scratch
Developers should use fine tuning when they have a limited amount of labeled data for a specific task, such as custom text classification, image recognition for niche objects, or adapting language models to specialized domains like legal or medical texts meets developers should use training from scratch when working with highly specialized or novel datasets where pre-trained models are unavailable or ineffective, such as in niche scientific research or custom industrial applications. Here's our take.
Fine Tuning
Developers should use fine tuning when they have a limited amount of labeled data for a specific task, such as custom text classification, image recognition for niche objects, or adapting language models to specialized domains like legal or medical texts
Fine Tuning
Nice PickDevelopers should use fine tuning when they have a limited amount of labeled data for a specific task, such as custom text classification, image recognition for niche objects, or adapting language models to specialized domains like legal or medical texts
Pros
- +It is particularly valuable for achieving high accuracy with less computational resources compared to training a model from scratch, making it essential for real-world applications where data is scarce or expensive to collect
- +Related to: transfer-learning, machine-learning
Cons
- -Specific tradeoffs depend on your use case
Training From Scratch
Developers should use training from scratch when working with highly specialized or novel datasets where pre-trained models are unavailable or ineffective, such as in niche scientific research or custom industrial applications
Pros
- +It is also appropriate when computational resources are sufficient and the goal is to avoid biases or limitations from pre-trained models, ensuring the model is tailored specifically to the task at hand
- +Related to: machine-learning, deep-learning
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Fine Tuning if: You want it is particularly valuable for achieving high accuracy with less computational resources compared to training a model from scratch, making it essential for real-world applications where data is scarce or expensive to collect and can live with specific tradeoffs depend on your use case.
Use Training From Scratch if: You prioritize it is also appropriate when computational resources are sufficient and the goal is to avoid biases or limitations from pre-trained models, ensuring the model is tailored specifically to the task at hand over what Fine Tuning offers.
Developers should use fine tuning when they have a limited amount of labeled data for a specific task, such as custom text classification, image recognition for niche objects, or adapting language models to specialized domains like legal or medical texts
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