evaluation_strategy="epoch", per_device_train_batch_size=32, per_device_eval_batch_size=32, learning_rate=5e-5, num_train_epochs=3, warmup_ratio=0.2, logging_dir='./imdb_train_logs', logging_strategy="epoch", save_strategy="epoch", report_to="tensorboard") trainer = Trainer( model,...
# 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_strategy="steps", # save_steps (default: 500):...
# 2. 如果想配置保存策略,可以设置 save_strategy="epoch"。这很重要,因为理想状态是模型保存一个最好的checkpoint就可以了。但是突发状况非常多,比如中途断电了,别人把你程序kill了,过几天之后硬盘空间没了等等,真实状况下,没隔一段时间保存一份更稳妥 training_args = TrainingArguments("test-trainer", evaluatio...
# 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_strategy="steps", # save_steps (default: 500):...
strategy: transformers.trainer_utils.IntervalStrategy = 'steps',save_steps: int = 500,save_total_limit: Optional[int] = None,no_cuda: bool = False,seed: int = 42,fp16: bool = False,fp16_opt_level: str = 'O1',fp16_backend: str = 'auto',fp16_full_eval: bool = False,local_...
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_size 分别表示在训练和验证期间使用的批大小。
max_steps=128, # delete for full training overwrite_output_dir=True, save_total_limit=3, fp16=False, # True if GPU)trainer = Seq2SeqTrainer( model=model, args=training_args, train_dataset=train_tokenized, eval_dataset=validation_tokenized,)trainer.train()过程也非常简单...
save_strategy="epoch", learning_rate=2e-4, bf16=True, tf32=True, max_grad_norm=0.3, warmup_ratio=0.03, lr_scheduler_type="constant", disable_tqdm=True# 当配置的参数都正确后可以关闭tqdm ) 我们现在有了用来训练模型SFTTrainer所需要准备的每一个模块。
save_steps=500, eval_steps=500, logging_steps=25, report_to=["tensorboard"], load_best_model_at_end=True, metric_for_best_model="wer", greater_is_better=False, push_to_hub=True, ) 开始训练: from transformers import Seq2SeqTrainer ...
logging_strategy="steps", # logging_steps (default 500): Number of update steps between two logs if # logging_strategy="steps". logging_steps=50, # save_strategy (default "steps"): # The checkpoint save strategy to adopt during training. Possible values are: ...