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)....
output_dir 存储最终模型的位置。 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_...
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...
我们没有通过将evaluation_strategy设置为“steps”(在每次更新参数的时候评估)或“epoch”(在每个epoch结束时评估)来告诉Trainer在训练期间进行评估。 我们没有为Trainer提供一个compute_metrics()函数来直接计算模型的好坏(否则评估将只输出loss,这不是一个非常直观的数字)。 4. 评估相关的模型: 首先,想要评估模型的...
batch_size=2, predict_with_generate=True, 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, # dele...
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,) ...
predict_with_generate=True,logging_steps=2, #setto1000forfull trainingsave_steps=64, #setto500forfull trainingeval_steps=64, #setto8000forfull trainingwarmup_steps=1, #setto2000forfull trainingmax_steps=128, # deleteforfull trainingoverwrite_output_dir=True,save_total_limit=3,fp16=False, ...
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...
( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=8, per_device_eval_batch_size=8, warmup_steps=500, weight_decay=0.01, logging_dir='./logs', ) # 创建Trainer trainer = Trainer( model=model, args=training_args, train_dataset=dataset, eval_dataset=dataset ) ...
# Use new PyTorch optimizer eval_steps = 1000, # New logging_steps = 1000, save_steps = 1000, learning_rate = 2e-5, per_device_train_batch_size = batch_size, per_device_eval_batch_size = batch_size, weight_decay = 0.01, save_total_limit = 3, num_train_epochs = ...