Fix indentation

app.py

@@ -7,7 +7,7 @@ tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
 import torch
 from torch.utils.data import Dataset
     
-    class ClinicalDataset(Dataset):
+class ClinicalDataset(Dataset):
          def __init__(self, texts, labels, tokenizer):
              self.texts = texts
              self.labels = labels
@@ -22,11 +22,11 @@ from torch.utils.data import Dataset
              encoding = self.tokenizer(text, return_tensors="pt", padding=True, truncation=True)
              return {"input_ids": encoding["input_ids"].squeeze(), "attention_mask": encoding["attention_mask"].squeeze(), "labels": torch.tensor(label)}
      
-     dataset = ClinicalDataset(texts=train_texts, labels=train_labels, tokenizer=tokenizer)
+dataset = ClinicalDataset(texts=train_texts, labels=train_labels, tokenizer=tokenizer)
 # Fine-tune the pre-trained model on your clinical dataset
 from transformers import Trainer, TrainingArguments
     
-    training_args = TrainingArguments(
+training_args = TrainingArguments(
          output_dir='./results',          # output directory
          num_train_epochs=3,              # total number of training epochs
          per_device_train_batch_size=16,  # batch size per device during training
@@ -36,7 +36,7 @@ from transformers import Trainer, TrainingArguments
          logging_dir='./logs',            # directory for storing logs
          logging_steps=10, )
     
-    trainer = Trainer(
+trainer = Trainer(
       model=model,
       args=training_args,
       train_dataset=dataset,
@@ -44,4 +44,4 @@ from transformers import Trainer, TrainingArguments
       data_collator=lambda data: {'input_ids': torch.stack([f['input_ids'] for f in data]),
                                 'attention_mask': torch.stack([f['attention_mask'] for f in data]),
                                  'labels': torch.stack([f['labels'] for f in data])}, )
-    trainer.train()
+trainer.train()