KeyError: 'eval_qwk' when used get_peft_model
When I try to use get_peft_model with aTrainerobject, it throwingKeyError: 'eval_qkw'. and when i don’t use get_peft_model , everything is ok.here is my code, and transformers version is 4.38.1def compute_metrics(p: EvalPrediction):
    y_pred = p.predictions.clip(1, 6).round()
    qwk = cohen_kappa_score(p.label_ids, y_pred, weights="quadratic")
    metrics = {'qwk': qwk}
    return metrics

class MyModel(nn.Module):
    def __init__(self, A):
        super(MyModel, self).__init__()
        
        self.bert = AutoModel.from_pretrained(modelpath)
        dim = self.bert.config.hidden_size

        self.fc = nn.Linear(dim, 1)
        self.A = A

    def forward(self, input_ids, attention_mask=None, labels=None):
        hidden = self.bert(input_ids, attention_mask=attention_mask)[0][:,0]
        y = self.fc(hidden).squeeze(1) + self.A

        if labels is not None:
            loss = F.mse_loss(y, labels)
            return loss, y
        return y#modeling_outputs.SequenceClassifierOutput(logits=y)
    

peft_config = LoraConfig(target_modules=["query_proj", "value_proj"], modules_to_save=["fc"], inference_mode=False, r=32, lora_alpha=64, lora_dropout=0.1)
model = MyModel(A=np.mean(tokenized_datasets['train']['labels']))

model = get_peft_model(model, peft_config)  # add this throwing KeyError

training_args = TrainingArguments(
    output_dir=checkpoint,
    evaluation_strategy = "epoch",
    save_strategy = "epoch",

    num_train_epochs=5,
    learning_rate=2e-5,

    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    gradient_accumulation_steps=1,

    metric_for_best_model=metric_name,
    greater_is_better=True,
    fp16=True,

    save_total_limit=1,
    load_best_model_at_end=True,
    seed = 41,
    # deepspeed='./deepspeed_config.json'
)

trainer = Trainer(
    model,
    training_args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["test"],
    tokenizer=tokenizer, 
    compute_metrics=compute_metrics,
    # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
    
)

trainer.train()