from torch.optim import AdamW from transformers import get_scheduler optimizer = AdamW(model.parameters(), lr=5e-5) num_epochs = 3 num_training_steps = num_epochs * len(train_dataloader)# 训练步数 = 训练周期数 * 批次数量 理解这个关系非常重要,因为深度学习模型的训练通常是通过在每个批次上计算...
parameters(), lr=5e-5) from transformers import get_scheduler num_epochs = 3 num_training_steps = num_epochs * len(train_dataloader) lr_scheduler = get_scheduler( "linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps) trainer = Trainer( model, args, ...
-> [2357](file:///D:/anaconda/envs/Faseeh/Lib/site-packages/transformers/trainer.py:2357) tr_loss_step = self.training_step(model, inputs) ... [126](file:///D:/anaconda/envs/Faseeh/Lib/site-packages/accelerate/optimizer.py:126) Sets the optimizer to "train" mode. Useful for opti...
f"(lr={optimizer.param_groups[-1]['lr']}):", ) # 训练模型 ifepoch !=0: train_epoch(model, loss_func, train_loader, optimizer) accelerator.wait_for_everyone() # 在训练集和测试集上评估模型 train_loss, train_acc = eval_epoch(model, loss_func, train_eval_loader) val_loss, val_ac...
model_init: Callable[transformers.modeling_utils.PreTrainedModel] = None,compute_metrics: Optional[Callable[transformers.trainer_utils.EvalPrediction,Dict]] = None,callbacks: Optional[List[transformers.trainer_callback.TrainerCallback]] = None,optimizers: Tuple[torch.optim.optimizer.Optimizer,torch.optim....
targets = targets.to(device) outputs = model(inputs) loss = loss_function(outputs, targets) loss.backward() optimizer.step() scheduler.step() 如何添加Accelerate到代码中呢? 3.2/ 添加Accelerate from accelerate import Accelerator accelerator = Accelerator() # 首先创建实例 ...
tokenizer = AutoTokenizer.from_pretrained(model_id) print(f"Train dataset size:{len(dataset['train'])}") print(f"Test dataset size:{len(dataset['test'])}") # Train dataset size: 287113 # Test dataset size: 11490 我们在配置文件中定义了一个prompt_template,其可用于来构建指令提示,以提高...
# 训练下游任务模型 from transformers import AdamW # 训练 optimizer = AdamW(model.parameters(),lr=5e-4) criterion = torch.nn.CrossEntropyLoss() model.train() for i,(input_ids,attention_mask,token_type_ids,labels) in enumerate(loader): out = model(input_ids=input_ids, attention_mask=attent...
所以这里提示还说:"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference." 3. 使用Trainer来训练 Trainer是Huggingface transformers库的一个高级API,可以帮助我们快速搭建训练框架: 代码语言:javascript ...
When the average gradient is computed on all workers, we adjust the model weights with the optimizer and continue training our model. You can see an illustration of different tasks that are executed below.Typical machine learning tasks executed by peers in distributed training, possibly ...