[models] - refactor: sequence classification - #92
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- Introduced structured training, validation, and test steps for better handling of classification tasks. - Improved loss computation and logging mechanisms to facilitate easier tracking of model performance during training.
- Consolidate training, validation, and test steps into reusable classification output and loss methods for better code organization and maintainability. - Remove redundant code and improve clarity by utilizing a consistent approach for handling model inputs and outputs across different model classes.
… loss - Introduced new data classes for sequence classification outputs and loss handling to improve type safety and clarity. - Updated existing classes to accommodate additional outputs and properties, facilitating better model output management.
- Loss tensors are now explicitly moved to CPU after detaching to prevent potential memory issues on GPU.
- Introduce a new lifecycle module to validate training, validation, and test steps for sequence classification models. - Add tests to ensure proper gradient computation and output shapes during training and inference stages.
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Moves sequence-classification training, evaluation and prediction into the shared base module, with small hooks for model-specific losses and early exits.