logging_steps=50, # save_strategy (default "steps"): # The checkpoint save strategy to adopt during training. Possible values are: # "no": No save is done during training. # "epoch": Save is done at the end of each epoch. # "steps": Save is done every save_steps (default 500)....
下面就是训练的过程:from transformers import BartForConditionalGenerationfrom transformers import Seq2SeqTrainingArguments, Seq2SeqTrainermodel = BartForConditionalGeneration.from_pretrained( "facebook/bart-base" )training_args = Seq2SeqTrainingArguments( output_dir="./", evaluation_strategy="steps...
logging_steps=2, # set to 1000 for full training save_steps=64, # set to 500 for full training eval_steps=64, # set to 8000 for full training warmup_steps=1, # set to 2000 for full training max_steps=128, # delete for full training overwrite_output_dir=True, save_total_limit=3...
evaluation_strategy和eval_steps每50个训练step在验证集上验证训练模型。 logging_strategy 和 logging_steps 每 50 个训练step保存日志(将由 TensorBoard 可视化)。 save_strategy 和 save_steps 表示每 200 个训练step保存训练模型。 learning_rate 学习率。per_device_train_batch_size 和 per_device_eval_batch_...
save_strategy为模型保存策略,同样有no, steps, epoch三种,意义同上 report_to为模型训练、评估中的重要指标(如loss, accurace)输出之处,可选择azure_ml, clearml, codecarbon, comet_ml, dagshub, flyte, mlflow, neptune, tensorboard, wandb,使用all会输出到所有的地方,使用no则不会输出。 下面我们使用Trainer...
# save_strategy (default "steps"): # The checkpoint save strategy to adopt during training. Possible values are: # "no": No save is done during training. # "epoch": Save is done at the end of each epoch. # "steps": Save is done every save_steps (default 500). ...
save_steps: 训练期间,每 save_steps 步保存一次中间 checkpoint 并异步上传到 Hub。 eval_steps: 训练期间,每 eval_steps 步对中间 checkpoint 进行一次评估。 report_to: 训练日志的保存位置,支持 azure_ml、comet_ml、mlflow、neptune、tensorboard 以及wandb 这些平台。你可以按照自己的偏好进行选择,也可以直接使...
save_steps=1000, # load_best_model_at_end=True, # whether to load the best model (interms 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 ...
training_args=TrainingArguments(output_dir="./lunyuAlbert",overwrite_output_dir=True,num_train_epochs=20,per_gpu_train_batch_size=16,save_steps=2000,save_total_limit=2,)trainer=Trainer(model=model,args=training_args,data_collator=data_collator,train_dataset=dataset,prediction_loss_only=True,) ...
# Setting number of steps in scheduler scheduler.set_timesteps(steps) # Convert the seed image to latent init_latents = pil_to_latents(init_img) # Figuring initial time step based on strength init_timestep = int(steps * strength)