It's important to note that there are numerous other models and variations developed by researchers and organizations worldwide, tailored to specific tasks and domains. The choice of model depends on the specific problem you are trying to solve and the data available.
Encoders1. Choose an appropriate encoding technique
1. Importing transformers modulesfrom transformers import module_name2. Loading pre-trained models
1. Importing scikit-learn modulesfrom sklearn import module_name2. Splitting data into training and testing sets
1. Data PreprocessingI. Resize ImagesResize all input images to a consistent size to ensure uniformity.
1. Sequence PaddingEnsure that input sequences have the same length by padding or truncating them to a fixed size.
1. Text PreprocessingI. TokenizationSplit the input text into individual tokens or words to prepare it for further processing.
Text PreprocessingI. TokenizationSplit the input text into individual tokens or words to prepare it for model input.
Data PreparationI. Image ResizingResize input images to a consistent size to ensure uniformity across the dataset.
Data PreparationI. Data NormalizationNormalize the input data to improve convergence and prevent numerical instability.