Trainer

errors.append(doc) return errors errors = analyze_tokenization_errors(documents, tokenizer) print(f"Tokenization Errors: {errors}") # Fine-tune the model on your specific dataset # This involves preparing a labeled dataset…

test_encodings = tokenize_data(tokenizer, test_df['query']) # Create datasets train_dataset = QueryDataset(train_encodings, train_df['label'].tolist()) test_dataset = QueryDataset(test_encodings, test_df['label'].tolist()) …

logging_steps=10, evaluation_strategy='epoch', save_total_limit=2, ) # Define the trainer trainer = Trainer( model=model, args=training_args, train_dataset=dataset['train'], eval_dataset=dataset['test'], dat…

labels = tokenizer(examples['reformulated'], max_length=512, padding='max_length', truncation=True, return_tensors='pt')['input_ids'] model_inputs['labels'] = labels return model_inputs tokenized_datasets = dataset.map(preproce…

- Ensure the new model integrates seamlessly with the rest of the retrieval pipeline. ### Example Implementation #### Step 1: Data Preparation Prepare your dataset for training and validation: ```python from transformers import AutoT…

2. **Model Selection**: Use a more sophisticated model that handles multiple languages effectively. 3. **Hyperparameter Tuning**: Fine-tune hyperparameters to improve model performance. 4. **Evaluation Metrics**: Use additional evaluation m…

output_dir='./results', num_train_epochs=3, per_device_train_batch_size=8, per_device_eval_batch_size=8, warmup_steps=500, weight_decay=0.01, logging_dir='./logs', logging_steps=10, evaluation_strategy="s…

tokenized_texts = [tokenize_text(text) for text in texts] # Evaluate accuracy def evaluate_accuracy(tokenized_texts, ground_truth): correct = 0 total = 0 for tokenized, truth in zip(tokenized_texts, ground_truth): for t…

accuracy = accuracy_score(test_df['label'], predicted_labels) print(f"Accuracy for {model_name}: {accuracy:.2f}") return accuracy # List of models to experiment with models_to_test = [ "bert-base-uncased", "roberta-bas…

### Step 3: Data Augmentation 1. **Back-Translation**: Translate your queries to another language and then back to the original language. 2. **Paraphrasing**: Use paraphrasing techniques to generate new variations of your queries. 3. **Syn…

3. **Data Augmentation**: Apply data augmentation techniques to further improve the model's performance. 4. **Evaluate and Monitor**: Continuously evaluate and monitor the model's performance. Would you like to proceed with these steps or …

"distilbert-base-uncased" ] # Experiment with different models best_accuracy = 0 best_model = None for model_name in models_to_test: accuracy = train_and_evaluate_model(model_name, train_df, test_df) if accuracy > best_accuracy…

- Utilize efficient libraries and frameworks that are optimized for CPU usage, such as TensorFlow or PyTorch. ### Example Implementation Here's an example of how you can fine-tune Llama 2 13B on a CPU with these strategies: #### 1. Lo…

model = T5ForConditionalGeneration.from_pretrained('./fine_tuned_model') def reformulate_query(query): inputs = tokenizer(f"reformulate: {query}", return_tensors="pt", max_length=512, truncation=True) outputs = model.generate(input…

# Define training arguments training_args = TrainingArguments( output_dir=f'./results/{model_name}', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_s…

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…