dataset

- **Splitting**: Split your dataset into training, validation, and test sets. A common split ratio is 80% training, 10% validation, and 10% test. ```python from datasets import load_dataset, DatasetDict # Load your dataset dataset = load_…

#### 1. Data Preprocessing ```python from transformers import LlamaTokenizer import torch # Load tokenizer tokenizer = LlamaTokenizer.from_pretrained("llama-2-13b") # Tokenize dataset def tokenize_function(examples): return tokenizer…

6. **Ensemble Methods**: Combine multiple models to improve overall accuracy. ### Enhanced Code Example Here's an enhanced version of your code that incorporates these strategies: ```python import torch from transformers import AutoModel…

return len(self.contexts) # Create dataset and data loader dataset = ContextDataset(contexts, labels) data_loader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True) ``` Can someone help me fine-tune this model for …

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = RerankingModel().to(device) dataset = ... # Your dataset loader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True) optimizer…

# Implement secure tuning logic here return np.random.rand(len(dataset)) # Apply secure tuning to datasets tuned_datasets = [secure_tuning(dataset) for dataset in datasets] # Calculate compliance rate compliance_rate = np.mean([np…