pyabsa.framework.instructor_class.instructor_template
Module Contents
Classes
Functions
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- class pyabsa.framework.instructor_class.instructor_template.BaseTrainingInstructor(config)[source]
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- _reload_model_state_dict(ckpt='./init_state_dict.bin')[source]
Reload the model state dictionary from a checkpoint file. :param ckpt: The path to the checkpoint file.
- load_cache_dataset(**kwargs)[source]
Load the dataset from cache if it exists and not set to overwrite the cache. Otherwise, return None. :param kwargs: Additional keyword arguments. :return: The path to the cache file if it exists. Otherwise, return None.
- save_cache_dataset(cache_path=None, **kwargs)[source]
Save the dataset to cache for faster loading in the future. :param kwargs: Additional arguments for saving the dataset cache. :param cache_path: The path to the cache file. :return: The path to the saved cache file.
- _prepare_env()[source]
Prepares the environment for training, including setting the tokenizer and embedding matrix, removing the initial state dictionary file if it exists, and setting up the model on the appropriate device.
- _train(criterion)[source]
Train the model on a given criterion.
- Parameters:
criterion – The loss function used to train the model.
- Returns:
If there is only one validation dataloader, return the training results. If there are more than one validation dataloaders, perform k-fold cross-validation and return the results.
- abstract _init_misc()[source]
Initialize miscellaneous settings specific to the subclass implementation. This method should be implemented in a subclass.
- abstract _cache_or_load_dataset()[source]
Cache or load the dataset. This method should be implemented in a subclass.
- abstract _train_and_evaluate(criterion)[source]
Train and evaluate the model. This method should be implemented in a subclass.
- abstract _k_fold_train_and_evaluate(criterion)[source]
Train and evaluate the model using k-fold cross validation. This method should be implemented in a subclass.
- abstract _evaluate_acc_f1(test_dataloader)[source]
Evaluate the accuracy and F1 score of the model. This method should be implemented in a subclass.