![]() ![]() ![]() Predict – Returns predictions (with metrics if labels are available) on a test set. Run_model (TensorFlow only) – Basic pass through the model.Įvaluate – Runs an evaluation loop and returns metrics. Prediction_step – Performs an evaluation/test step. Training_step – Performs a training step. ![]() Log – Logs information on the various objects watching training.Ĭreate_optimizer_and_scheduler – Setups the optimizer and learning rate scheduler if they were not passed atĬompute_loss - Computes the loss on a batch of training inputs. Get_test_dataloader/ get_test_tfdataset – Creates the test DataLoader (PyTorch) or TF Dataset. Get_eval_dataloader/ get_eval_tfdataset – Creates the evaluation DataLoader (PyTorch) or TF Dataset. Get_train_dataloader/ get_train_tfdataset – Creates the training DataLoader (PyTorch) or TF Dataset. To inject custom behavior you can subclass them and override the following methods: The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex for PyTorch and tf.keras.mixed_precision for TensorFlow.īoth Trainer and TFTrainer contain the basic training loop supporting the TrainingArguments/ TFTrainingArguments to access all the points of It’s used in most of the example scripts.īefore instantiating your Trainer/ TFTrainer, create a The Trainer and TFTrainer classes provide an API for feature-complete ![]()
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