preprocess_logits_for_metrics:一个函数,用于在每次评估步骤后预处理logits。它必须接受两个张量,即logits和labels,并返回处理后的logits。此函数的修改将在compute_metrics中反映在接收到的预测值上。 Trainer类简化了训练流程,让用户可以更加专注于模型的设计和训练策略,而不必担心底层的训练细节。通过提供这些参数和功...
preprocess_logits_for_metrics (Callable[[torch.Tensor, torch.Tensor], torch.Tensor], 可选):用于指定一个函数,这个函数在每次评估步骤(evaluation step)前,其实就是在进入compute_metrics函数前对模型的输出 logits 进行预处理。接受两个张量(tensors)作为参数,一个是模型的输出 logits,另一个是真实标签(labels...
preprocess_logits_for_metrics: typing.Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = None ) 参数: model:一个 PreTrainedModel 或torch.nn.Module 对象,指定用于训练、评估、或预测的模型。如果未提供,则必须传入 model_init 参数。 Trainer 被优化为与 PreTrainedModel 一起工作。但是你仍然可以使用...
preprocess_logits_for_metrics (`Callable[[paddle.Tensor, paddle.Tensor], paddle.Tensor]`, 可选)): 一个函数, 在每次评估之前对logits进行预处理。 (`Callable[[paddle.Tensor, paddle.Tensor], paddle.Tensor]`, *optional*) A function that preprocess the logits right before caching them at each ...
self.args._n_gpu = 1 # later use `self.model is self.model_wrapped` to check if it's wrapped or not self.model_wrapped = model self.model = model self.compute_metrics = compute_metrics self.preprocess_logits_for_metrics = preprocess_logits_for_metrics self.optimizer, self.lr_scheduler...
epoch =-1foriinrange(epochs): top1, _ = trainer.train(train_loader, model, criterion, optimizer, i, opt) print('cls weights: {}, aa weights: {}'.format( model.mA.parameters().next().norm().data[0], model.mAAa.parameters().next().norm().data[0])) ...
for easy upload metrics = train_result.metrics max_train_samples = (data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)) metrics["train_samples"] = min(max_train_samples, len(train_dataset)) trainer.log_metrics(...
epoch =-1foriinrange(epochs): top1, _ = trainer.train(train_loader, model, criterion, optimizer, i, opt) print('cls weights: {}, aa weights: {}'.format( model.mA.parameters().next().norm().data[0], model.mAAa.parameters().next().norm().data[0])) ...
# Data collator will default to DataCollatorWithPadding, so we change it.# data_collator=default_data_collator,data_collator=data_collator,preprocess_logits_for_metrics=(preprocess_logits_for_metricsiftraining_args.do_evalandnotis_torch_tpu_available()elseNone),callbacks=callbacks_ls,# 这里传入)·...
[typing.List[transformers.trainer_callback.TrainerCallback]] = None optimizers: typing.Tuple[torch.optim.optimizer.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None) preprocess_logits_for_metrics: typing.Union[typing.Callable[[torch.Tensor, torch.Tensor], torch.Tensor], NoneType] = ...