Supervised Fine-Tuning vs Training From Scratch
Developers should use Supervised Fine-Tuning when they have a limited amount of labeled data for a specific task but want to achieve high accuracy by building on a pre-trained model's general knowledge 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.
Supervised Fine-Tuning
Developers should use Supervised Fine-Tuning when they have a limited amount of labeled data for a specific task but want to achieve high accuracy by building on a pre-trained model's general knowledge
Supervised Fine-Tuning
Nice PickDevelopers should use Supervised Fine-Tuning when they have a limited amount of labeled data for a specific task but want to achieve high accuracy by building on a pre-trained model's general knowledge
Pros
- +It is particularly valuable in domains like NLP for tasks such as sentiment analysis or named entity recognition, where pre-trained models like BERT or GPT provide a strong foundation
- +Related to: transfer-learning, pre-trained-models
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 Supervised Fine-Tuning if: You want it is particularly valuable in domains like nlp for tasks such as sentiment analysis or named entity recognition, where pre-trained models like bert or gpt provide a strong foundation 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 Supervised Fine-Tuning offers.
Developers should use Supervised Fine-Tuning when they have a limited amount of labeled data for a specific task but want to achieve high accuracy by building on a pre-trained model's general knowledge
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