metric_for_best_model="f1", # 设定评估指标 load_best_model_at_end=True) # 训练完成后加载最优模型 train_args ''' TrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=False, bf16_full_eval=False, d...
metric_for_best_model="f1Sample", # The metric name to evaluate a model load_best_model_at_end=True # Whether load the best model at the end of training ) trainer = transformers.Trainer( model=model, # Function to get a fresh model args=training_args, # Training arguments created above...
metric_for_best_model="f1", # push to hub parameters report_to="tensorboard", push_to_hub=True, hub_strategy="every_save", hub_model_id=repository_id, hub_token=HfFolder.get_token(), ) # Create a Trainer instance trainer = Trainer( model=model, args=training_args, train_dataset=toke...
load_best_model_at_end=True, metric_for_best_model=metric_name, ) 上面evaluation_strategy = "epoch"参数告诉训练代码:我们每个epcoh会做一次验证评估。 上面batch_size在这个notebook之前定义好了。 最后,由于不同的任务需要不同的评测指标,我们定一个函数来根据任务名字得到评价方法: def compute_metrics(ev...
load_best_model_at_end=True, metric_for_best_model="wer", greater_is_better=False, push_to_hub=True, ) 注意: 如果不想将模型 checkpoint 上传到 Hub,你需要设置 push_to_hub=False。 我们可以将训练参数以及模型、数据集、数据整理器和 compute_metrics 函数一起传给 🤗 Trainer: from transformers...
对Transformer模型的研究中,会出现一些术语:架构Architecture和检查点checkpoint以及Model。 这些术语的含义略有不同: Architecture:定义了模型的基本结构和基本运算 checkpoint:模型的某个训练状态,加载此checkpoint会加载此时的权重。(训练时可以选择自动保存checkpoint) ...
metric_for_best_model=f1, mp_parameters=, neftune_noise_alpha=None, no_cuda=False, num_train_epochs=3.0, optim=adamw_torch, optim_args=None, output_dir=./checkpoints, overwrite_output_dir=False, past_index=-1, per_device_eval_batch_size=128, per_device_train_batch_size=64, prediction...
[str]] = None,load_best_model_at_end: Optional[bool] = False,metric_for_best_model: Optional[str] = None,greater_is_better: Optional[bool] = None,ignore_data_skip: bool = False,sharded_ddp: str = '',deepspeed: Optional[str] = None,label_smoothing_factor: float = 0.0,adafactor: ...
args=TrainingArguments(output_dir="models_for_ner",per_device_train_batch_size=32,per_device_eval_batch_size=64,evaluation_strategy="epoch",save_strategy="epoch",metric_for_best_model="f1",load_best_model_at_end=True,logging_steps=50,num_train_epochs=1) ...
metric_for_best_model = 'bleu', # New or "f1" load_best_model_at_end = True # New ) trainer = Seq2SeqTrainer( model = model, args = training_args, train_dataset = train_ds, eval_dataset = eval_ds, tokenizer = tokenizer, data_collator = data_collator, compute_...