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doc:beam/97caa0eb-3854-43dd-83e5-f2b56dd19262To ensure strict enforcement of the 1% limit, you can implement additional checks and validation in your data fetching functions. Here's an enhanced version of the `fetch_limited_tuning_data` function: ```python def fetch_limited_tuning_da…
doc:beam/e90baac4-24b6-4abb-89e2-a81f7d246e29accuracy = 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…
doc:beam/3119929b-7715-416d-a7a4-963c7bfbf2f0issue.update(fields={'assignee': {'name': assignee}}) print(f"Task {task_key} assigned to {assignee}") except Exception as e: print(f"Failed to assign task {task_key}: {str(e)}") # Initialize Jira client jira_cl…
doc:beam/ce0f38e5-9f9e-428f-abc8-fc9a177d0e20Gradually re-encrypt existing data with the new key: ```python # Fetch the new encryption key from Vault new_key = get_encryption_key(vault_client) # Re-encrypt existing data reencrypted_data = encrypt_data(decrypted_data, new_key) print…
doc:beam/4efeeb64-8572-49af-812f-e5accd46c4adquery_vector = np.random.rand(1, 128).astype("float32") # Search for nearest neighbors k = 10 # number of nearest neighbors to retrieve D, I = index.search(query_vector, k) # Print the results print("Distances:", D) print("Indices:", I) …
doc:beam/b5573ddd-8b6e-4548-a117-b6f5f7970ed3bleu_score = sentence_bleu([original.split()], reformulated.split()) bleu_scores.append(bleu_score) return sum(bleu_scores) / len(bleu_scores) # Example usage original_queries = ['What is the meaning of life?', 'How do …
doc:beam/2ab0a1fa-1edb-4fa9-bdf6-d24eb14c3996- Define a function `update_model_with_feedback` to update the model with new ratings. - Convert new ratings to the Surprise format and update the model using the `update` method. 5. **Collect New Feedback**: - Define a function `…
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