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The following table provides definitions for some of the terms that are used in this document.
Term | Definition |
Fine-tuning dataset | Fine-tuning dataset refers to a specific set of data used in the process of fine-tuning a pretrained machine learning model. Fine-tuning involves taking a model that has already been trained on a large dataset for a general task (like language understanding) and further training it on a smaller, more specific dataset related to a particular task or domain. This smaller dataset is referred to as the fine-tuning dataset. |
Pretrained model | A pretrained model refers to a machine learning model that has been trained on a large dataset for a specific task or set of tasks before being made available for use. This pretraining allows the model to learn general patterns and features that are relevant to the task. Once pretrained, the model can be further fine-tuned on a smaller, specific dataset to adapt it to a particular application or domain. Pretrained models serve as a foundation for various AI applications, saving time and resources in the development process. |
Model fine-tuning | Model fine-tuning is the process of taking a pretrained machine learning model and further training it on a smaller, task-specific dataset. This helps the model adapt to a particular application or domain. Fine-tuning leverages the knowledge the model has gained during its initial training on a large dataset for a more general task. By using a fine-tuning dataset related to the specific task at hand, the model can improve its performance and accuracy for that targeted application. This process is crucial for customizing pretrained models to perform effectively in specialized tasks or domains. |
Evaluation of fine-tuned model | Evaluating a fine-tuned model involves assessing its performance on a specific task or in a particular domain after it has undergone the fine-tuning process. This typically entails using a separate dataset, distinct from the one used for fine-tuning, to measure the model's accuracy, precision, recall, F1 score, or other relevant metrics. The evaluation aims to ensure that the fine-tuned model performs well in real-world scenarios and meets the wanted level of accuracy and effectiveness for the targeted application. This process is essential for verifying the success of the fine-tuning process and validating the model's suitability for the intended task or domain. |
Model inference | Model inference is the process of using a trained machine learning model to make predictions or decisions based on new, unseen data. During inference, the model takes input data and produces an output, which could be a classification, regression, or other type of prediction depending on the nature of the model and the task it was trained for. Unlike the training phase, where the model learns from labeled data, inference involves applying the learned knowledge to make real-time or batch predictions on new data. It is a critical step in deploying machine learning models for practical applications like image recognition, language translation, and many other tasks. |