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...
save_total_limit=save_total_limit,#最多保存模型个数 metric_for_best_model='eval_cider',#修改衡量指标 greater_is_better=True, learning_rate=lr, warmup_ratio=0.03, seed=userSeed,overwrite_output_dir=True, per_device_eval_batch_size=batchsize, per_device_train_batch_size=batchsize, output_d...
metric_for_best_model="f1", # 确定最佳模型的评估指标 load_best_model_at_end=True # 训练结束时加载表现最佳的模型 ) 模型在transformers库源码中的trainer.py文件中的_save()方法执行保存操作。 该方法的源码如下: def _save(self, output_dir: Optional[str] = None, state_dict=None): ...
from transformers import AutoModelForSequenceClassification # Model id to load the tokenizer model_id = "bert-base-uncased" # Prepare model labels - useful for inference labels = tokenized_dataset["train"].features["labels"].names num_labels = len(labels) ...
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) ...