Sure, to add more layers to the BERT model, you can use the `BertForSequenceClassification` model and modify its classifier head. Here's an example of how to add one additional linear layer to the classifier head:
```scss
import torch
import pandas as pd
from transformers import BertTokenizer, BertForSequenceClassification, AdamW
from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from torch.nn.utils.rnn import pad_sequence
from torch.nn.functional import cross_entropy

# Check if a GPU is available, else use CPU
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load the pre-trained BERT model and tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=8).to(device)

# Modify the classifier head to add an additional linear layer
model.classifier = torch.nn.Sequential(
    torch.nn.Dropout(p=0.1),
    torch.nn.Linear(in_features=model.classifier.in_features, out_features=256),
    torch.nn.ReLU(),
    torch.nn.Dropout(p=0.1),
    torch.nn.Linear(in_features=256, out_features=model.config.num_labels)
).to(device)

# Load your training data
train_df = pd.read_csv('train.csv')

# Split the train data into train and validation sets
train_texts, val_texts, train_labels, val_labels = train_test_split(train_df['text'], train_df['label'], test_size=.2, random_state=42)

# Encode your text data into numerical representations using the tokenizer
max_length = 256
train_encodings = tokenizer(train_texts.tolist(), truncation=True, padding=True, max_length=max_length)
val_encodings = tokenizer(val_texts.tolist(), truncation=True, padding=True, max_length=max_length)

# Convert the encoded data into PyTorch tensors and move them to the GPU
train_inputs = torch.tensor(train_encodings['input_ids']).to(device)
train_masks = torch.tensor(train_encodings['attention_mask']).to(device)
train_labels = torch.tensor(train_labels.tolist()).to(device)
train_dataset = TensorDataset(train_inputs, train_masks, train_labels)

val_inputs = torch.tensor(val_encodings['input_ids']).to(device)
val_masks = torch.tensor(val_encodings['attention_mask']).to(device)
val_labels = torch.tensor(val_labels.tolist()).to(device)
val_dataset = TensorDataset(val_inputs, val_masks, val_labels)

# Define your optimizer and learning rate schedule
optimizer = AdamW(model.parameters(), lr=5e-5)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)

# Define the data loaders for training and validation
batch_size = 4
train_sampler = RandomSampler(train_dataset)
train_loader = DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size)
val_sampler = SequentialSampler(val_dataset)
val_loader = DataLoader(val_dataset, sampler=val_sampler, batch_size=batch_size)

# Define early stopping variables
patience = 5
best_val_loss = float('inf')
epochs_since_improvement = 0

# Fine-tune the pre-trained BERT model on your training data
num_epochs = 50
for epoch in range(num_epochs):
    train_loss = 0
    val_loss = 0
    model.train()
    for i, batch in enumerate(train_loader):
        inputs, masks, labels = batch

        optimizer.zero_grad()
        outputs = model(inputs.to(device), attention_mask=masks.to(device), labels=labels.to(device))
        loss = outputs.loss
        loss.backward()
        optimizer.step()
```
즉, BERT 모델에 더 많은 레이어를 추가하려면 `BertForSequenceClassification` 모델을 사용하고 해당 분류기 헤드를 수정하면 됩니다. 다음은 분류기 헤드에 선형 레이어를 하나 더 추가하는 방법의 예입니다:```scssimport torchimport pandas as pdtransformers에서 BertTokenizer, BertForSequenceClassification, AdamW를 가져옵니다.sklearn.model_selection에서 train_test_split을 가져옵니다.torch.utils.data에서 텐서 데이터셋, 데이터 로더, 랜덤 샘플러, 시퀀셜 샘플러를 임포트합니다.torch.nn.utils.rnn에서 pad_sequence 임포트torch.nn.functional에서 cross_entropy를 가져옵니다.# GPU를 사용할 수 있는지 확인, 그렇지 않으면 CPU 사용device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')# 사전 학습된 BERT 모델과 토큰화기를 로드합니다.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=8).to(device)# 분류기 헤드를 수정하여 선형 레이어를 추가합니다.model.classifier = torch.nn.Sequential(    torch.nn.Dropout(p=0.1),    torch.nn.Linear(in_features=model.classifier.in_features, out_features=256),    torch.nn.ReLU(),    torch.nn.Dropout(p=0.1),    torch.nn.Linear(in_features=256, out_features=model.config.num_labels)).to(device)# 훈련 데이터 로드train_df = pd.read_csv('train.csv')# 훈련 데이터를 훈련 및 검증 세트로 분할합니다.train_texts, val_texts, train_labels, val_labels = train_test_split(train_df['text'], train_df['label'], test_size=.2, random_state=42)# 토큰화기를 사용하여 텍스트 데이터를 숫자 표현으로 인코딩합니다.max_length = 256train_encodings = tokenizer(train_texts.tolist(), truncation=True, padding=True, max_length=max_length)val_encodings = tokenizer(val_texts.tolist(), truncation=True, padding=True, max_length=max_length)# 인코딩된 데이터를 PyTorch 텐서로 변환하여 GPU로 이동합니다.train_inputs = torch.tensor(train_encodings['input_ids']).to(device)train_masks = torch.tensor(train_encodings['attention_mask']).to(device)train_labels = torch.tensor(train_labels.tolist()).to(device)train_dataset = TensorDataset(train_inputs, train_masks, train_labels)val_inputs = torch.tensor(val_encodings['input_ids']).to(device)val_masks = torch.tensor(val_encodings['attention_mask']).to(device)val_labels = torch.tensor(val_labels.tolist()).to(device)val_dataset = TensorDataset(val_inputs, val_masks, val_labels)# 옵티마이저와 학습 속도 일정을 정의합니다.optimizer = AdamW(model.parameters(), lr=5e-5)스케줄러 = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)# 훈련 및 검증을 위한 데이터 로더를 정의합니다.batch_size = 4train_sampler = 랜덤샘플러(train_dataset)train_loader = DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size)val_sampler = SequentialSampler(val_dataset)val_loader = DataLoader(val_dataset, sampler=val_sampler, batch_size=batch_size)# 조기 중지 변수 정의patience = 5best_val_loss = float('inf')epochs_since_improvement = 0# 훈련 데이터에 대해 사전 훈련된 BERT 모델을 미세 조정합니다.num_epochs = 50for epoch in range(num_epochs):    train_loss = 0    val_loss = 0    model.train()    for i, batch in enumerate(train_loader):        입력, 마스크, 레이블 = 배치        optimizer.zero_grad()        output = model(inputs.to(device), attention_mask=masks.to(device), labels=labels.to(device))        손실 = 출력.손실        loss.backward()        optimizer.step()```