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 ...
args = training_args, max_seq_length=2048, packing=False, # num_of_sequences=1024, formatting_func=formatting_prompts_func, data_collator=data_collator, compute_metrics=compute_metrics, preprocess_logits_for_metrics = preprocess_logits_for_metrics )...
trainer.validate(train_loader, model, criterion,-1, metrics, args)forepochinrange(args.start_epoch, args.epochs):ifargs.distributed: trainer.train_sampler.set_epoch(epoch) scores = {} scores.update(trainer.train(train_loader, model, criterion, optimizer, epoch, metrics, args)) ...
这些trainer为了吸引更多人使用,肯定要加尽可能多的功能,比如基本的日志、tensorboard、断点重训、训练时...
processing"): predict_dataset = predict_dataset.map( preprocess_function, batched=True, load_from_cache_file=not data_args.overwrite_cache, desc="Running tokenizer on prediction dataset", ) # Get the metric function metric = load_metric("xnli") # You can def...
trainer.validate(train_loader, model, criterion,-1, metrics, args)forepochinrange(args.start_epoch, args.epochs):ifargs.distributed: trainer.train_sampler.set_epoch(epoch) scores = {} scores.update(trainer.train(train_loader, model, criterion, optimizer, epoch, metrics, args)) ...
# 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)·...