可以使用 Trainer 对象的 predict 方法生成预测。 #Tokenize test set dataset_test_encoded = dataset["test"].map(preprocess_function_batch, batched=True) # Use the model to get predictions test_predictions = trainer.predict(dataset_test_encoded) # For each prediction, create the label with argmax...
I'm encountering aCUDA out of memoryerror when using thecompute_metricsfunction with the Hugging Face Trainer during model evaluation. My GPU is running out of memory while trying to compute the ROUGE scores. Below is a summary of my setup and the error message: I have a val_...
If you're going to train a model to evaluate on the SQUAD dataset, then the QuestionAnsweringTrainer is the most appropriate Trainer object to use. [Suggestion]: Most probably HuggingFace devs and dev-advocate should add some notes on the object in QuestionAnsweringTrainer http...
This is the code to subclass Trainer class CustomTrainer(Trainer): def compute_loss(self, model, inputs, return_outputs=False): labels = inputs.get("labels") # forward pass outputs = model(**inputs) logits = outputs.get("logits") # compute custom loss (suppose one has 3 labels with...
Both the ppov2 and the rloo trainers use the following to compute rewards . trl/trl/trainer/ppov2_trainer.py Lines 322 to 324 in 3c0a10b _, score, _ = get_reward( reward_model, postprocessed_query_response, tokenizer.pad_token_id, contex...
I’m trying to log training and validation accuracy and using a compute_metrics function. However, when I set the compute_metrics argument to the function and run Trainer, the Cuda OutOfMemoryError pops up. I’m able to train smoothly when I comment out compute_metrics....
How the loss is computed by Trainer. By default, all models return the loss in the first element. Subclass and override for custom behavior. """ifself.label_smootherisnotNoneand"labels"ininputs: labels = inputs.pop("labels")else: