training_args = TrainingArguments( output_dir="my_awesome_model", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, num_train_epochs=2, weight_decay=0.01, evaluation_strategy="epoch", save_strategy="epoch", load_best_model_at_end=True, push_to_hub=Tru...
load_best_model_at_end=True, local_rank=0, log_level=passive, log_level_replica=warning, log_on_each_node=True, logging_dir=./cross_model/runs/Nov27_07-11-23_66feef283143, logging_first_step=False, logging_nan_inf_filter=True, logging_steps=10, logging_strategy=steps, lr_scheduler_...
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...
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...
load_best_model_at_end=True, ... greater_is_better=False, ... label_names=["labels"], ... push_to_hub=True, ... ) 实例化Trainer对象,并将模型、数据集和数据整理器传递给它。 代码语言:javascript 复制 >>> from transformers import Seq2SeqTrainer >>> trainer = Seq2SeqTrainer( ... ...
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) ...
metric_for_best_model (str, 可选) :与 load_best_model_at_end 结合使用以指定用于比较两个不同模型的metric 。必须是评估返回的metric 的名称,带或不带前缀“eval_”。 num_train_epochs (float, 可选,默认是3) – 要训练的epoch数 load_best_model_at_end (bool, 可选, 默认为 False) :是否在训...
load_best_model_at_end=True, report_to="wandb" ) for name, param in model.named_parameters(): param.requires_grad_(False) if name =='model.embed_tokens.weight': param.requires_grad_(True) print(name, "requires_grad:", param.requires_grad) ...
$MODEL_PATH/$MODEL_NAME \ --num_train_epochs 5 \ --weight_decay 0.01 \ --learning_rate 1e-5 \ --warmup_steps 8000 \ --save_strategy steps \ --save_steps 4000 \ --save_total_limit 10 \ --evaluation_strategy steps \ --eval_steps 4000 \ --load_best_model_at_end \ --...
(The answer to the second, “What is the best show currently on TV?” can be found at the end of this post.)This week’s question: Who is your favorite voice actor for animated characters on TV? Why?Dave Trumbore (@DrClawMD), ColliderMy knee-jerk reaction was either Kevin Conroy ...