# training (used to log training loss for example). Possible values are: # "no": No logging is done during training. # "epoch": Logging is done at the end of each epoch. # "steps": Logging is done every logging_steps. logging_strategy="steps", # logging_steps (default 500): Numb...
Trainer rather than Trainertrainer=HfMultiTaskTrainer(model,args,train_dataset=ds)trainer.train()if__name__=='__main__':main() 按照标准HF的方式启动训练,便可在日志中记录各项指标,例如: {'loss': 55.509,'grad_norm': 1.0,'learning_rate': 4.8387096774193554e-05,'tensor': 0.9841784507036209,'np...
classtransformers.TrainingArguments(output_dir: str,overwrite_output_dir: bool = False,do_train: bool = False,do_eval: bool = None,do_predict: bool = False,evaluation_strategy: transformers.trainer_utils.IntervalStrategy = 'no',prediction_loss_only: bool = False,per_device_train_batch_size: ...
tokenized_dataset_train = dataset_train.map(preprocess_function, batched=True) tokenized_dataset_test = dataset_test.map(preprocess_function, batched=True)fromtransformersimportAutoModelForSequenceClassification, TrainingArguments, TrainerfromtransformersimportDataCollatorWithPadding###num_label...
# training (used to log training loss for example). Possible values are: # "no": No logging is done during training. # "epoch": Logging is done at the end of each epoch. # "steps": Logging is done every logging_steps. logging_strategy="steps", ...
compute_loss方法:这个方法是最重要的,很多时候,就是直接继承Trainer,然后写一个自己的Trainer,大家都...
metric_for_best_model="eval_loss", greater_is_better=False ) trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=val_dataset, callbacks=[early_stopping] ) trainer.train() When trainer.train() is called, I get the error below, which ...
1- main_process_ip: None - main_process_port: None - main_training_function: main ...
args=training_args, train_dataset=lm_datasets["train"], eval_dataset=lm_datasets["validation"], ) trainer.train() 训练完成后,评估以如下方式进行: import math eval_results = trainer.evaluate() print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")监督微调 ...
# TrainingArguments参数指定了训练的设置:输出目录、总的epochs、 # 训练的batch_size、预测的batch_size、warmup的step数、weight_decay和log目录。 training_args = TrainingArguments("test-trainer") model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2) trainer = Trainer( model...