Artificial Intelligence & Machine Learning
Pre-trained Model
Definition
A pre-trained model is a model that was trained on a large benchmark dataset to solve a problem similar to the one that we want to solve. This model, with its saved weights and parameters, can then be used as a starting point for a new task.
Why It Matters
Pre-trained models are the foundation of transfer learning. They allow developers to leverage the knowledge gained from massive training runs (that cost millions of dollars) and apply it to their own problems with much less data and compute.
Contextual Example
BERT is a powerful language model pre-trained by Google on a massive text corpus. A developer can take the pre-trained BERT model and fine-tune it for a specific NLP task, like sentiment analysis, achieving high accuracy without having to train a model from scratch.
Common Misunderstandings
- Model hubs like Hugging Face provide access to thousands of pre-trained models for various tasks.
- Using a pre-trained model is almost always a better approach than training from scratch, unless you have a very large, specialized dataset.