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# 评估类型和 Job 提交
<a name="model-customize-evaluation-types"></a>

## 使用标准化数据集进行基准测试
<a name="model-customize-evaluation-benchmarking"></a>

使用基准评估类型在标准化基准数据集（包括 MMLU 和 BBH 等热门数据集）中评估模型的质量。


| 基准 | 支持自定义数据集 | 模式 | 说明 | 指标 | Strategy | 可用子任务 | 
| --- | --- | --- | --- | --- | --- | --- | 
| mmlu | 否 | 文本 | 多任务语言理解：考核 57 个科目的知识。 | 准确性 | zs\$1cot | 是 | 
| mmlu\$1pro | 否 | 文本 | MMLU（专业子集），专注于法律、医学、会计和工程等专业领域。 | 准确性 | zs\$1cot | 否 | 
| bbh | 否 | 文本 | 高级推理任务：一系列具有挑战性的问题，用于考核更高级别的认知和解决问题的能力。 | 准确性 | fs\$1cot | 是 | 
| gpqa | 否 | 文本 | 一般物理问题解答：评测对物理概念和相关问题解决能力的理解情况。 | 准确性 | zs\$1cot | 否 | 
| math | 否 | 文本 | 数学问题解决：衡量在代数、微积分及应用题等领域的数学推理能力。 | exact\$1match | zs\$1cot | 是 | 
| strong\$1reject | 否 | 文本 | 质量控制任务-测试模型检测和拒绝不当、有害或不正确内容的能力。 | deflection | zs | 是 | 
| ifeval | 否 | 文本 | 指令跟随评估：衡量模型遵循给定指令并按照规范完成任务的准确程度。 | 准确性 | zs | 否 | 

有关 BYOD 格式的更多信息，请参阅[Bring-Your-Own-Dataset(BYOD) 任务支持的数据集格式](model-customize-evaluation-dataset-formats.md)。

### 可用子任务
<a name="model-customize-evaluation-benchmarking-subtasks"></a>

以下列出了跨多个领域进行模型评估的可用子任务，包括 MMLU（大规模多任务语言理解）、BBH（Big Bench Hard）和 MATH。 StrongReject这些子任务让您能够评测模型在特定功能和知识领域的表现。

**MMLU 子任务**

```
MMLU_SUBTASKS = [
    "abstract_algebra",
    "anatomy",
    "astronomy",
    "business_ethics",
    "clinical_knowledge",
    "college_biology",
    "college_chemistry",
    "college_computer_science",
    "college_mathematics",
    "college_medicine",
    "college_physics",
    "computer_security",
    "conceptual_physics",
    "econometrics",
    "electrical_engineering",
    "elementary_mathematics",
    "formal_logic",
    "global_facts",
    "high_school_biology",
    "high_school_chemistry",
    "high_school_computer_science",
    "high_school_european_history",
    "high_school_geography",
    "high_school_government_and_politics",
    "high_school_macroeconomics",
    "high_school_mathematics",
    "high_school_microeconomics",
    "high_school_physics",
    "high_school_psychology",
    "high_school_statistics",
    "high_school_us_history",
    "high_school_world_history",
    "human_aging",
    "human_sexuality",
    "international_law",
    "jurisprudence",
    "logical_fallacies",
    "machine_learning",
    "management",
    "marketing",
    "medical_genetics",
    "miscellaneous",
    "moral_disputes",
    "moral_scenarios",
    "nutrition",
    "philosophy",
    "prehistory",
    "professional_accounting",
    "professional_law",
    "professional_medicine",
    "professional_psychology",
    "public_relations",
    "security_studies",
    "sociology",
    "us_foreign_policy",
    "virology",
    "world_religions"
]
```

**BBH 子任务**

```
BBH_SUBTASKS = [
    "boolean_expressions",
    "causal_judgement",
    "date_understanding",
    "disambiguation_qa",
    "dyck_languages",
    "formal_fallacies",
    "geometric_shapes",
    "hyperbaton",
    "logical_deduction_five_objects",
    "logical_deduction_seven_objects",
    "logical_deduction_three_objects",
    "movie_recommendation",
    "multistep_arithmetic_two",
    "navigate",
    "object_counting",
    "penguins_in_a_table",
    "reasoning_about_colored_objects",
    "ruin_names",
    "salient_translation_error_detection",
    "snarks",
    "sports_understanding",
    "temporal_sequences",
    "tracking_shuffled_objects_five_objects",
    "tracking_shuffled_objects_seven_objects",
    "tracking_shuffled_objects_three_objects",
    "web_of_lies",
    "word_sorting"
]
```

**数学子任务**

```
MATH_SUBTASKS = [
    "algebra", 
    "counting_and_probability", 
    "geometry",
    "intermediate_algebra", 
    "number_theory", 
    "prealgebra", 
    "precalculus"
]
```

**StrongReject 子任务**

```
STRONG_REJECT_SUBTASKS = [
    "gcg_transfer_harmbench", 
    "gcg_transfer_universal_attacks",
    "combination_3", 
    "combination_2", 
    "few_shot_json", 
    "dev_mode_v2",
    "dev_mode_with_rant",
    "wikipedia_with_title", 
    "distractors",
    "wikipedia",
     "style_injection_json", 
    "style_injection_short",
    "refusal_suppression", 
    "prefix_injection", 
    "distractors_negated",
    "poems", 
    "base64", 
    "base64_raw", "
    base64_input_only",
    "base64_output_only", 
    "evil_confidant", 
    "aim", 
    "rot_13",
    "disemvowel", 
    "auto_obfuscation", 
    "auto_payload_splitting", 
    "pair",
    "pap_authority_endorsement", 
    "pap_evidence_based_persuasion",
    "pap_expert_endorsement", 
    "pap_logical_appeal", 
    "pap_misrepresentation"
]
```

### 提交您的基准测试作业
<a name="model-customize-evaluation-benchmarking-submit"></a>

------
#### [ SageMaker Studio ]

![\[通过 SageMaker Studio 进行基准测试的最低配置\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/benchmark-submission-sagemaker-studio.png)


------
#### [ SageMaker Python SDK ]

```
from sagemaker.train.evaluate import get_benchmarks
from sagemaker.train.evaluate import BenchMarkEvaluator

Benchmark = get_benchmarks()

# Create evaluator with MMLU benchmark
evaluator = BenchMarkEvaluator(
benchmark=Benchmark.MMLU,
model="arn:aws:sagemaker:<region>:<account-id>:model-package/<model-package-name>/<version>",
s3_output_path="s3://<bucket-name>/<prefix>/",
evaluate_base_model=False
)

execution = evaluator.evaluate()
```

有关通过 SageMaker Python SDK 提交评估作业的更多信息，请参阅：[https://sagemaker.readthedocs.io/en/stable/model\$1customization/evaluation.html](https://sagemaker.readthedocs.io/en/stable/model_customization/evaluation.html)

------

## 大型语言模型作为评判 (LLMAJ) 评估
<a name="model-customize-evaluation-llmaj"></a>

使用 LLM-as-a-judge (LLMAJ) 评估来利用另一个前沿模型对目标模型的响应进行评分。你可以通过调用 `create_evaluation_job` API 启动评估作业，使用 B AWS edrock 模型作为评委。

有关支持的评判模型的更多信息，请参阅：[https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html)

您可以使用两种不同的指标格式来定义评估：
+ **内置指标：**利用 B AWS edrock 内置指标来分析模型推理响应的质量。欲了解更多信息，请参阅：[https://docs.aws.amazon.com/bedrock/latest/userguide/model-evaluation-type-judge-prompt .html](https://docs.aws.amazon.com/bedrock/latest/userguide/model-evaluation-type-judge-prompt.html)
+ **自定义指标：**使用 Bedrock 评估自定义指标格式定义自己的自定义指标，使用您自己的指令分析模型推理响应的质量。欲了解更多信息，请参阅：[https://docs.aws.amazon.com/bedrock/latest/userguide/model-evaluation-custom-metrics-prompt-formats.html](https://docs.aws.amazon.com/bedrock/latest/userguide/model-evaluation-custom-metrics-prompt-formats.html)

### 提交内置指标 LLMAJ 作业
<a name="model-customize-evaluation-llmaj-builtin"></a>

------
#### [ SageMaker Studio ]

![\[通过 Studio 进行 LLMAJ 基准测试的最低配置 SageMaker\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/llmaj-as-judge-submission-sagemaker-studio.png)


------
#### [ SageMaker Python SDK ]

```
from sagemaker.train.evaluate import LLMAsJudgeEvaluator

evaluator = LLMAsJudgeEvaluator(
    model="arn:aws:sagemaker:<region>:<account-id>:model-package/<model-package-name>/<version>",
    evaluator_model="<bedrock-judge-model-id>",
    dataset="s3://<bucket-name>/<prefix>/<dataset-file>.jsonl",
    builtin_metrics=["<builtin-metric-1>", "<builtin-metric-2>"],
    s3_output_path="s3://<bucket-name>/<prefix>/",
    evaluate_base_model=False
)

execution = evaluator.evaluate()
```

有关通过 SageMaker Python SDK 提交评估作业的更多信息，请参阅：[https://sagemaker.readthedocs.io/en/stable/model\$1customization/evaluation.html](https://sagemaker.readthedocs.io/en/stable/model_customization/evaluation.html)

------

### 提交自定义指标 LLMAJ 作业
<a name="model-customize-evaluation-llmaj-custom"></a>

定义您的自定义指标：

```
{
    "customMetricDefinition": {
        "name": "PositiveSentiment",
        "instructions": (
            "You are an expert evaluator. Your task is to assess if the sentiment of the response is positive. "
            "Rate the response based on whether it conveys positive sentiment, helpfulness, and constructive tone.\n\n"
            "Consider the following:\n"
            "- Does the response have a positive, encouraging tone?\n"
            "- Is the response helpful and constructive?\n"
            "- Does it avoid negative language or criticism?\n\n"
            "Rate on this scale:\n"
            "- Good: Response has positive sentiment\n"
            "- Poor: Response lacks positive sentiment\n\n"
            "Here is the actual task:\n"
            "Prompt: {{prompt}}\n"
            "Response: {{prediction}}"
        ),
        "ratingScale": [
            {"definition": "Good", "value": {"floatValue": 1}},
            {"definition": "Poor", "value": {"floatValue": 0}}
        ]
    }
}
```

欲了解更多信息，请参阅：[https://docs.aws.amazon.com/bedrock/latest/userguide/model-evaluation-custom-metrics-prompt-formats.html](https://docs.aws.amazon.com/bedrock/latest/userguide/model-evaluation-custom-metrics-prompt-formats.html)

------
#### [ SageMaker Studio ]

![\[通过 “自定义指标” > “添加自定义指标” 上传自定义指标\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/custom-llmaj-metrics-submission-sagemaker-studio.png)


------
#### [ SageMaker Python SDK ]

```
evaluator = LLMAsJudgeEvaluator(
    model="arn:aws:sagemaker:<region>:<account-id>:model-package/<model-package-name>/<version>",
    evaluator_model="<bedrock-judge-model-id>",
    dataset="s3://<bucket-name>/<prefix>/<dataset-file>.jsonl",
    custom_metrics=custom_metric_dict = {
        "customMetricDefinition": {
            "name": "PositiveSentiment",
            "instructions": (
                "You are an expert evaluator. Your task is to assess if the sentiment of the response is positive. "
                "Rate the response based on whether it conveys positive sentiment, helpfulness, and constructive tone.\n\n"
                "Consider the following:\n"
                "- Does the response have a positive, encouraging tone?\n"
                "- Is the response helpful and constructive?\n"
                "- Does it avoid negative language or criticism?\n\n"
                "Rate on this scale:\n"
                "- Good: Response has positive sentiment\n"
                "- Poor: Response lacks positive sentiment\n\n"
                "Here is the actual task:\n"
                "Prompt: {{prompt}}\n"
                "Response: {{prediction}}"
            ),
            "ratingScale": [
                {"definition": "Good", "value": {"floatValue": 1}},
                {"definition": "Poor", "value": {"floatValue": 0}}
            ]
        }
    },
    s3_output_path="s3://<bucket-name>/<prefix>/",
    evaluate_base_model=False
)
```

------

## 自定义记分器
<a name="model-customize-evaluation-custom-scorers"></a>

定义您自己的自定义记分器函数以启动评估作业。该系统提供了两个内置得分器：素数和素码。你也可以自带记分器功能。您可以直接复制您的记分器函数代码，也可以使用关联的 ARN 自带自己的 Lambda 函数定义。默认情况下，两种得分手类型都会生成评估结果，其中包括标准指标，例如 F1 分数、ROUGE 和 BLEU。

有关内置和自定义评分器及其各自要求/合同的更多信息，请参阅。[使用预设和自定义评分器进行评估](model-customize-evaluation-preset-custom-scorers.md)

### 注册您的数据集
<a name="model-customize-evaluation-custom-scorers-register-dataset"></a>

通过将自己的数据集注册为 SageMaker Hub 内容数据集，为自定义评分者带来自己的数据集。

------
#### [ SageMaker Studio ]

在 Studio 中，使用专用的数据集页面上传您的数据集。

![\[SageMaker Studio 中注册的评估数据集\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/dataset-registration-sagemaker-studio.png)


------
#### [ SageMaker Python SDK ]

在 SageMaker Python SDK 中，使用专用的数据集页面上传您的数据集。

```
from sagemaker.ai_registry.dataset import DataSet

dataset = DataSet.create(
    name="your-bring-your-own-dataset",
    source="s3://<bucket-name>/<prefix>/<dataset-file>.jsonl"
)
dataset.refresh()
```

------

### 提交内置记分员作业
<a name="model-customize-evaluation-custom-scorers-builtin"></a>

------
#### [ SageMaker Studio ]

![\[从代码执行或数学答案中选择内置自定义评分\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/builtin-scorer-submission-sagemaker-studio.png)


------
#### [ SageMaker Python SDK ]

```
from sagemaker.train.evaluate import CustomScorerEvaluator
from sagemaker.train.evaluate import get_builtin_metrics

BuiltInMetric = get_builtin_metrics()

evaluator_builtin = CustomScorerEvaluator(
    evaluator=BuiltInMetric.PRIME_MATH,
    dataset="arn:aws:sagemaker:<region>:<account-id>:hub-content/<hub-content-id>/DataSet/your-bring-your-own-dataset/<version>",
    model="arn:aws:sagemaker:<region>:<account-id>:model-package/<model-package-name>/<version>",
    s3_output_path="s3://<bucket-name>/<prefix>/",
    evaluate_base_model=False
)

execution = evaluator.evaluate()
```

从 “内置评分” 中选择`BuiltInMetric.PRIME_MATH`或`BuiltInMetric.PRIME_CODE`。

------

### 提交自定义记分员作业
<a name="model-customize-evaluation-custom-scorers-custom"></a>

定义自定义奖励函数。有关更多信息，请参阅 [自定义评分器（自带指标）](model-customize-evaluation-preset-custom-scorers.md#model-customize-evaluation-custom-scorers-byom)。

**注册自定义奖励功能**

------
#### [ SageMaker Studio ]

![\[导航到 SageMaker Studio > 资产 > 评估器 > 创建评估器 > 创建奖励函数\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/custom-scorer-submission-sagemaker-studio.png)


![\[在 Custom Scorer > Custom Metrics 中提交 Custom Scorer 评估作业，引用注册的预设\]](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/images/custom-scorer-benchmark-submission-sagemaker-studio.png)


------
#### [ SageMaker Python SDK ]

```
from sagemaker.ai_registry.evaluator import Evaluator
from sagemaker.ai_registry.air_constants import REWARD_FUNCTION

evaluator = Evaluator.create(
    name = "your-reward-function-name",
    source="/path_to_local/custom_lambda_function.py",
    type = REWARD_FUNCTION
)
```

```
evaluator = CustomScorerEvaluator(
    evaluator=evaluator,
    dataset="s3://<bucket-name>/<prefix>/<dataset-file>.jsonl",
    model="arn:aws:sagemaker:<region>:<account-id>:model-package/<model-package-name>/<version>",
    s3_output_path="s3://<bucket-name>/<prefix>/",
    evaluate_base_model=False
)

execution = evaluator.evaluate()
```

------