如何在DistilBERT中进行交叉验证

from sklearn.model_selection import KFold
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments
from datasets import Dataset

# 假设我们有一个数据集dataset
# dataset = ...

# 初始化tokenizer和模型
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')

# 数据预处理
def preprocess_function(examples):
    return tokenizer(examples['text'], truncation=True, padding='max_length')

encoded_dataset = dataset.map(preprocess_function, batched=True)

# K折交叉验证
k = 5
kf = KFold(n_splits=k, shuffle=True)

results = []

for fold, (train_index, val_index) in enumerate(kf.split(encoded_dataset['train'])):
    train_dataset = encoded_dataset['train'].select(train_index)
    val_dataset = encoded_dataset['train'].select(val_index)
    
    training_args = TrainingArguments(
        output_dir=f'./results_{fold}',
        evaluation_strategy='epoch',
        learning_rate=2e-5,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=64,
        num_train_epochs=3,
        weight_decay=0.01,
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=val_dataset,
    )

    trainer.train()
    
    # 评估模型
    results.append(trainer.evaluate())

# 输出平均性能
print(f'Average results: {sum(results) / k}')