Breakdown of Generative || Pre-trained || Transformer
Generative models are a type of statistical model which are used to create new data by understanding the relationships between different variables.
Pre-trained models are models that have already been trained on a large dataset. This allows them to be used for tasks where it would be difficult to train a model from scratch. A pre-trained model may not be 100% accurate, but it can save you time and improve performance.
Transformer model is a deep learning model that is used for tasks such as machine translation and text classification. It is designed to handle sequential data, such as text.
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Having spent weeks attempting to understand what GPT-3 is, I have finally come up with my own simple explanation. Hope this makes sense to you.
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Similar ideas to Breakdown of Generative || Pre-trained || Transformer
Transfer learning and fine tuning
Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem. For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis.
- Take layers from...
The Salesforce team has created and open-sourced a new identifier-aware unified pre-trained encoder-decoder model called CodeT5 . So far, they have demonstrated state-of-the-art results in multiple code-related downstream tasks ...
Transfer learning concept
The biggest problem, thoug h, is that models like this one are performed only on a single task. Future tasks require a new set of data points as well as equal or more amount of resources.
Transfer learning is an approach in deep learning (and machine learning) where knowledge is ...
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