fine-tuning a model with the Trainer API is required to make predictions from large dataset to get better results.
Fine-tuning a model with the Trainer API is required when you want to adapt a pre-trained model to a specific task or domain. Here are a few reasons why fine-tuning is necessary:
Transfer Learning: Pre-trained models are trained on large-scale datasets and have learned general patterns and representations. By fine-tuning a pre-trained model, you can leverage this learned knowledge and adapt it to your specific task. This is especially useful when you have limited labeled data for your task, as the pre-trained model can provide a good starting point.
Domain Adaptation: Fine-tuning allows you to adapt a model to a specific domain. If the pre-trained model was trained on data from a different domain than your target task, fine-tuning helps the model learn domain-specific features and improve its performance on your task.
Efficient Training: Fine-tuning a model is generally faster and requires less computational resources compared to training a model from scratch. Since the initial layers of the model have already learned low-level features, fine-tuning focuses on updating the higher-level layers that are more task-specific. This reduces the training time and allows you to achieve good performance with fewer training iterations.
Better Performance: Fine-tuning allows the model to learn task-specific representations, which can lead to improved performance on your specific task compared to using a generic, pre-trained model without fine-tuning.
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