随后将其作为输入字典传递给forward()模型方法。这是由Trainer类完成的,例如这里的第 573-576 行: def_training_step( self, model: nn.Module, inputs: Dict[str, torch.Tensor], optimizer: torch.optim.Optimizer )-> float:model.train()fork, vininputs.items(): inputs[k] = v.to(self.args.devi...
我相信你是在正确的轨道上;你的逻辑检查和更新最佳损失（在on_evaluate方法中）是有意义的。但是，请确...
Trainer( model=model, args=training_args, train_dataset=dsd["train"], eval_dataset=dsd["test"], tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics, ) 最后一句话即可开始训练过程,数据后处理 在模型训练中,直接根据模型输出的 logit 矩阵就可以计算 loss,不需要转换成面向...
# save_total_limit=3, # whether you don't have much space so you # let only 3 model weights saved in the disk ) trainer = Trainer( model=model, args=training_args, data_collator=data_collator, train_dataset=train_dataset, eval_dataset=test_dataset, ) # train the modeltrainer.train()...
revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None ) return model args:超参数的定义,这部分也是trainer的重要功能,大部分训练相关的参数都是这里设置的,非常的方便: Trainerhuggingface.co classtransformers.TrainingArguments(output_dir: str,overwrite_output_...
save_strategy="epoch", load_best_model_at_end=True, push_to_hub=True, ) trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_imdb["train"], eval_dataset=tokenized_imdb["test"], tokenizer=tokenizer, data_collator=data_collator, ...
model_path="pretrained-bert"# make the directoryifnot already thereifnot os.path.isdir(model_path): os.mkdir(model_path) # save the tokenizer tokenizer.save_model(model_path) # dumping some of the tokenizer config to config file,
accelerator.wait_for_everyone()unwrapped_model=accelerator.unwrap_model(model)unwrapped_model.save_pretrained(save_dir,save_function=accelerator.save,state_dict=accelerator.get_state_dict(model)) Note: DeepSpeed support is experimental for now. In case you get into some problem, please open an issue...
the best model (in terms of loss)# at the end of training# save_total_limit=3, # whether you don't have much space so you# let only 3 model weights saved in the disk)trainer=Trainer(model=model,args=training_args,data_collator=data_collator,train_dataset=train_dataset,eval_dataset=...
peft_model_path = os.path.join(checkpoint_folder, "adapter_model") kwargs["model"].save_pretrained(peft_model_path) pytorch_model_path = os.path.join(checkpoint_folder, "pytorch_model.bin") if os.path.exists(pytorch_model_path): os.remove(pytorch_model_path) return control trainer = Seq...