自定义metrics这样定义就行,基本上就是python的常规的function写法,如果要输出多个metrics,则return的部分定义多个metrics就可以,注意,输出一定要用dict的格式,因为你evaluate的时候,trainer的参数部分会让你指定用metrics的名字来进行evaluation和earlystop等等。 剩下的就没有了,trainer基本上只要定义好了dataset就可以了,...
model = AutoModelForCausalLM.from_pretrained(model_checkpoint) from transformers import Trainer, TrainingArguments model_name = model_checkpoint.split("/")[-1] training_args = TrainingArguments( f"{model_name}-finetuned-wikitext2", evaluation_strategy = "epoch", learning_rate=2e-5, weight_dec...
model = AutoModelForCausalLM.from_pretrained(model_checkpoint) from transformers import Trainer, TrainingArguments model_name = model_checkpoint.split("/")[-1] training_args = TrainingArguments( f"{model_name}-finetuned-wikitext2", evaluation_strategy = "epoch", learning_rate=2e-5, weight_dec...
model = AutoModelForCausalLM.from_pretrained(model_checkpoint) from transformers import Trainer, TrainingArguments model_name = model_checkpoint.split("/")[-1] training_args = TrainingArguments( f"{model_name}-finetuned-wikitext2", evaluation_strategy = "epoch", learning_rate=2e-5, weight_dec...
fromtransformers import AutoModelForCausalLMmodel=AutoModelForCausalLM.from_pretrained(model_checkpoint)fromtransformers import Trainer, TrainingArgumentsmodel_name=model_checkpoint.split("/")[-1]training_args=TrainingArguments(f"{model_name}-finetuned-wikitext2",evaluation_strategy="epoch",learning_rate...
您可以加载accuracy metric并使其与您的compute_metrics函数一起工作。例如,它如下所示:...
if eval_dataset is None and self.eval_dataset is None: raise ValueError("Trainer: evaluation requires an eval_dataset.") so it seems you need an eval_dataset passed in the __init__ (where you call trainer=Trainer(...) or eventually because predict() calls evaluate() which ca...
from transformers import TrainingArguments, Trainer training_args = TrainingArguments( output_dir='pegasus-samsum', num_train_epochs=1, warmup_steps=500, per_device_train_batch_size=1, per_device_eval_batch_size=1, weight_decay=0.01, logging_steps=10, push_to_hub=True, ...
它非常简单。您可以在“Seq2SeqTrainingArguments”中提到它。不需要在“Seq2SeqTrainer”函数中显式定义...
它非常简单。您可以在“Seq2SeqTrainingArguments”中提到它。不需要在“Seq2SeqTrainer”函数中显式定义...