本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

使用預設和自訂計分器進行評估

使用自訂計分器評估類型時，SageMaker Evaluation 支援兩個內建計分器 （也稱為「獎勵函數」) 從 volcengine/verl RL 訓練程式庫取得的 Prime Math 和 Prime Code，或您自己的自訂計分器實作為 Lambda 函數。

內建計分器

主要數學

主要數學計分器預期自訂 JSONL 資料集的項目，其中包含數學問題作為提示/查詢，而正確答案作為基本事實。資料集可以是 中提及的任何支援格式Bring-Your-Own-Dataset (BYOD) 任務支援的資料集格式。

資料集項目範例 （為了清楚起見而擴展）：

{
    "system":"You are a math expert: ",
    "query":"How many vertical asymptotes does the graph of $y=\\frac{2}{x^2+x-6}$ have?",
    "response":"2" # Ground truth aka correct answer
}

主要程式碼

主要程式碼評分器預期自訂 JSONL 資料集的項目，其中包含 metadata 欄位中指定的編碼問題和測試案例。使用每個項目的預期函數名稱、範例輸入和預期輸出來建構測試案例。

資料集項目範例 （為了清楚起見而擴展）：

{
    "system":"\\nWhen tackling complex reasoning tasks, you have access to the following actions. Use them as needed to progress through your thought process.\\n\\n[ASSESS]\\n\\n[ADVANCE]\\n\\n[VERIFY]\\n\\n[SIMPLIFY]\\n\\n[SYNTHESIZE]\\n\\n[PIVOT]\\n\\n[OUTPUT]\\n\\nYou should strictly follow the format below:\\n\\n[ACTION NAME]\\n\\n# Your action step 1\\n\\n# Your action step 2\\n\\n# Your action step 3\\n\\n...\\n\\nNext action: [NEXT ACTION NAME]\\n\\n",
    "query":"A number N is called a factorial number if it is the factorial of a positive integer. For example, the first few factorial numbers are 1, 2, 6, 24, 120,\\nGiven a number N, the task is to return the list/vector of the factorial numbers smaller than or equal to N.\\nExample 1:\\nInput: N = 3\\nOutput: 1 2\\nExplanation: The first factorial number is \\n1 which is less than equal to N. The second \\nnumber is 2 which is less than equal to N,\\nbut the third factorial number is 6 which \\nis greater than N. So we print only 1 and 2.\\nExample 2:\\nInput: N = 6\\nOutput: 1 2 6\\nExplanation: The first three factorial \\nnumbers are less than equal to N but \\nthe fourth factorial number 24 is \\ngreater than N. So we print only first \\nthree factorial numbers.\\nYour Task:  \\nYou don't need to read input or print anything. Your task is to complete the function factorialNumbers() which takes an integer N as an input parameter and return the list/vector of the factorial numbers smaller than or equal to N.\\nExpected Time Complexity: O(K), Where K is the number of factorial numbers.\\nExpected Auxiliary Space: O(1)\\nConstraints:\\n1<=N<=10^{18}\\n\\nWrite Python code to solve the problem. Present the code in \\n```python\\nYour code\\n```\\nat the end.",
    "response": "", # Dummy string for ground truth. Provide a value if you want NLP metrics like ROUGE, BLEU, and F1.
    ### Define test cases in metadata field
    "metadata": {
        "fn_name": "factorialNumbers",
        "inputs": ["5"],
        "outputs": ["[1, 2]"]
    }
}

自訂計分器 （使用您自己的指標）

使用自訂後製處理邏輯來完全自訂模型評估工作流程，可讓您根據需求來計算自訂指標。您必須將自訂計分器實作為接受模型回應並傳回獎勵分數的 AWS Lambda 函數。

Lambda 輸入承載範例

您的自訂 AWS Lambda 預期會以 OpenAI 格式輸入。範例：

{
    "id": "123",
    "messages": [
        {
            "role": "user",
            "content": "Do you have a dedicated security team?"
        },
        {
            "role": "assistant",
            "content": "As an AI developed by Amazon, I do not have a dedicated security team..."
        }
    ],
    "reference_answer": {
        "compliant": "No",
        "explanation": "As an AI developed by Company, I do not have a traditional security team..."
    }
}

Lambda 輸出承載範例

SageMaker 評估容器預期您的 Lambda 回應遵循以下格式：

{
    "id": str,                              # Same id as input sample
    "aggregate_reward_score": float,        # Overall score for the sample
    "metrics_list": [                       # OPTIONAL: Component scores
        {
            "name": str,                    # Name of the component score
            "value": float,                 # Value of the component score
            "type": str                     # "Reward" or "Metric"
        }
    ]
}

自訂 Lambda 定義

在 https：//https://docs.aws.amazon.com/sagemaker/latest/dg/nova-implementing-reward-functions.html#nova-reward-llm-judge-example 尋找具有範例輸入和預期輸出的完全實作自訂評分器範例

使用下列骨架做為您自己的函數的起點。

def lambda_handler(event, context):
    return lambda_grader(event)

def lambda_grader(samples: list[dict]) -> list[dict]:
    """
    Args:
        Samples: List of dictionaries in OpenAI format
            
        Example input:
        {
            "id": "123",
            "messages": [
                {
                    "role": "user",
                    "content": "Do you have a dedicated security team?"
                },
                {
                    "role": "assistant",
                    "content": "As an AI developed by Company, I do not have a dedicated security team..."
                }
            ],
            # This section is the same as your training dataset
            "reference_answer": {
                "compliant": "No",
                "explanation": "As an AI developed by Company, I do not have a traditional security team..."
            }
        }
        
    Returns:
        List of dictionaries with reward scores:
        {
            "id": str,                              # Same id as input sample
            "aggregate_reward_score": float,        # Overall score for the sample
            "metrics_list": [                       # OPTIONAL: Component scores
                {
                    "name": str,                    # Name of the component score
                    "value": float,                 # Value of the component score
                    "type": str                     # "Reward" or "Metric"
                }
            ]
        }
    """

輸入和輸出欄位

輸入欄位

輸出欄位

所需的許可

確定您用來執行評估的 SageMaker 執行角色具有 AWS Lambda 許可。

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "lambda:InvokeFunction"
            ],
            "Resource": "arn:aws:lambda:region:account-id:function:function-name"
        }
    ]
}

確保您的 AWS Lambda 函數的執行角色具有基本的 Lambda 執行許可，以及任何下游 AWS 呼叫可能需要的其他許可。

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "logs:CreateLogGroup",
        "logs:CreateLogStream",
        "logs:PutLogEvents"
      ],
      "Resource": "arn:aws:logs:*:*:*"
    }
  ]
}