tokenized data

- Define training arguments for the `Trainer` to control the training process. 5. **Trainer**: - Use the `Trainer` from the `transformers` library to fine-tune the model. 6. **Fine-Tuning and Evaluation**: - Fine-tune the model o…

# Define training arguments training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_steps=500, weight_decay=0.01, loggi…

- Load and split the dataset into training and testing sets. - Tokenize the data using the tokenizer. 2. **Model Fine-Tuning**: - Define a custom dataset class to handle the tokenized data. - Set up training arguments and defin…

- Set an appropriate expiration time based on how frequently the data changes. - Use `setex` to set the key with an expiration time. By implementing these strategies, you can effectively use Redis to cache query results, reducing the l…

- **Authentication**: - Ensure that users authenticate and obtain a valid token before accessing the data. - Use the `KeycloakOpenID` client to handle authentication and token validation. - **Data Filtering**: - Implement the data fi…

keycloak_admin = KeycloakAdmin(server_url="https://my-keycloak-server.com", username="my-username", password="my-password", realm_name="my-realm") …

- Write unit tests to cover various scenarios, including valid and invalid input data. This helps ensure that your tokenization logic works as expected and catches edge cases. By incorporating these improvements, you can handle invalid i…

sample_size = int(len(all_data) * 0.20) return random.sample(all_data, sample_size) elif "10-percent-access" in user_roles: sample_size = int(len(all_data) * 0.10) return random.sample(all_data, sample_si…