Sure, here's some updated code that splits the train data into training and validation sets, trains the BERT-based model on the training data, and evaluates the model on the test data:
```python
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from transformers import BertForSequenceClassification, BertTokenizer
from transformers import AdamW, get_linear_schedule_with_warmup
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import pandas as pd

# Load the training data and split into train and validation sets
train_df = pd.read_csv('related_groups.csv')
train_texts, val_texts, train_labels, val_labels = train_test_split(train_df['text'], train_df['label'], test_size=0.2, random_state=42)

# Load the pre-trained BERT model and tokenizer
model_name = 'bert-base-uncased'
model = BertForSequenceClassification.from_pretrained(model_name, num_labels=4)
tokenizer = BertTokenizer.from_pretrained(model_name)

# Tokenize the text data and create input tensors
train_encodings = tokenizer(train_texts.tolist(), truncation=True, padding=True)
val_encodings = tokenizer(val_texts.tolist(), truncation=True, padding=True)
train_dataset = torch.utils.data.TensorDataset(torch.tensor(train_encodings['input_ids']), torch.tensor(train_encodings['attention_mask']), torch.tensor(train_labels.tolist()))
val_dataset = torch.utils.data.TensorDataset(torch.tensor(val_encodings['input_ids']), torch.tensor(val_encodings['attention_mask']), torch.tensor(val_labels.tolist()))

# Create a DataLoader for the training and validation data
batch_size = 32
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size)
val_sampler = SequentialSampler(val_dataset)
val_dataloader = DataLoader(val_dataset, sampler=val_sampler, batch_size=batch_size)

# Set up the optimizer and scheduler
epochs = 5
learning_rate = 2e-5
optimizer = AdamW(model.parameters(), lr=learning_rate, eps=1e-8)
total_steps = len(train_dataloader) * epochs
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps)

# Train the model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
best_accuracy = 0
for epoch in range(epochs):
    model.train()
    for batch in train_dataloader:
        input_ids = batch[0].to(device)
        attention_mask = batch[1].to(device)
        labels = batch[2].to(device)
        optimizer.zero_grad()
        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
        loss = outputs.loss
        loss.backward()
        optimizer.step()
        scheduler.step()
    model.eval()
    with torch.no_grad():
        predictions = []
        true_labels = []
        for batch in val_dataloader:
            input_ids = batch[0].to(device)
            attention_mask = batch[1].to(device)
            labels = batch[2]
            outputs = model(input_ids, attention_mask=attention_mask)
            logits = outputs.logits
            logits = logits.detach().cpu().numpy()
            label_ids = labels.numpy()
            predictions.extend(logits.argmax(axis=1))
            true_labels.extend(label_ids)
        accuracy = accuracy_score(true_labels, predictions)
        print(f'Epoch {epoch + 1} - Validation Accuracy: {accuracy}')
        if accuracy > best_accuracy:
            best_accuracy = accuracy
            torch.save(model.state_dict(), 'best_model.pt')

# Evaluate the model on the test data
test_df = pd.read_csv('test.csv
```
훈련 데이터를 훈련 및 검증 세트로 분할하고, 훈련 데이터에 대해 BERT 기반 모델을 훈련하고, 테스트 데이터에 대해 모델을 평가하는 몇 가지 업데이트된 코드가 있습니다:'''pythonimport torchtorch.utils.data에서 DataLoader, RandomSampler, SequentialSampler를 가져옵니다.transformers에서 BertForSequenceClassification, BertTokenizer를 임포트합니다.transformers에서 AdamW, get_linear_schedule_with_warmup을 임포트합니다.sklearn.metrics에서 accuracy_score를 가져옵니다.sklearn.model_selection에서 train_test_split을 가져옵니다.팬더를 PD로 가져오기# 훈련 데이터를 로드하고 훈련 및 검증 세트로 분할합니다.train_df = pd.read_csv('related_groups.csv')train_texts, val_texts, train_labels, val_labels = train_test_split(train_df['text'], train_df['label'], test_size=0.2, random_state=42)# 사전 학습된 BERT 모델과 토큰화기를 로드합니다.model_name = 'bert-base-uncased'model = BertForSequenceClassification.from_pretrained(model_name, num_labels=4)토큰화기 = BertTokenizer.from_pretrained(model_name)# 텍스트 데이터를 토큰화하고 입력 텐서를 생성합니다.train_encodings = tokenizer(train_texts.tolist(), truncation=True, padding=True)val_encodings = 토큰화기(val_texts.tolist(), 잘라내기=True, 패딩=True)train_dataset = torch.utils.data.TensorDataset(torch.tensor(train_encodings['input_ids']), torch.tensor(train_encodings['attention_mask']), torch.tensor(train_labels.tolist()))val_dataset = torch.utils.data.TensorDataset(torch.tensor(val_encodings['input_ids']), torch.tensor(val_encodings['attention_mask']), torch.tensor(val_labels.tolist()))# 훈련 및 검증 데이터를 위한 데이터 로더를 생성합니다.batch_size = 32train_sampler = 랜덤샘플러(train_dataset)train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size)val_sampler = 시퀀셜샘플러(val_dataset)val_dataloader = 데이터로더(val_dataset, 샘플러=val_sampler, 배치_크기=배치_크기)# 옵티마이저 및 스케줄러 설정epochs = 5learning_rate = 2e-5optimizer = AdamW(model.parameters(), lr=learning_rate, eps=1e-8)총_스텝 = len(train_dataloader) * epochsscheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps)# 모델 훈련device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')model.to(device)best_accuracy = 0for epoch in range(epochs):    model.train()    for batch in train_dataloader:        input_ids = batch[0].to(device)        attention_mask = batch[1].to(device)        labels = batch[2].to(device)        optimizer.zero_grad()        output = model(input_ids, attention_mask=attention_mask, labels=labels)        손실 = 출력.손실        loss.backward()        optimizer.step()        scheduler.step()    model.eval()    와 함께 torch.no_grad():        예측 = []        true_labels = []        val_dataloader의 배치에 대해:            input_ids = batch[0].to(device)            attention_mask = batch[1].to(device)            labels = batch[2]            output = model(input_ids, attention_mask=attention_mask)            logits = outputs.logits            logits = logits.detach().cpu().numpy()            label_ids = labels.numpy()            예측.확장(logits.argmax(axis=1))            true_labels.extend(label_ids)        정확도 = 정확도_점수(true_labels, 예측)        print(f'에포크 {에포크 + 1} - 유효성 검사 정확도: {accuracy}')        정확도 > 최고 정확도:            best_accuracy = 정확도            torch.save(model.state_dict(), 'best_model.pt')# 테스트 데이터에서 모델 평가test_df = pd.read_csv('test.csv'```