使用 AWS CLI 的 Amazon Comprehend 示例

以下代码示例演示了如何通过将 AWS Command Line Interface与 Amazon Comprehend 结合使用，来执行操作和实现常见场景。

操作是大型程序的代码摘录，必须在上下文中运行。您可以通过操作了解如何调用单个服务函数，还可以通过函数相关场景的上下文查看操作。

每个示例都包含一个指向完整源代码的链接，您可以从中找到有关如何在上下文中设置和运行代码的说明。

主题

操作

操作

以下代码示例演示了如何使用 batch-detect-dominant-language。

AWS CLI

检测多个输入文本的主要语言

以下 batch-detect-dominant-language 示例分析多个输入文本并返回每个文本的主要语言。预训练模型的置信度分数也是每个预测的输出。


aws comprehend batch-detect-dominant-language \
    --text-list "Physics is the natural science that involves the study of matter and its motion and behavior through space and time, along with related concepts such as energy and force."

输出：


{
    "ResultList": [
        {
            "Index": 0,
            "Languages": [
                {
                    "LanguageCode": "en",
                    "Score": 0.9986501932144165
                }
            ]
        }
    ],
    "ErrorList": []
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的主要语言。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 BatchDetectDominantLanguage。

以下代码示例演示了如何使用 batch-detect-entities。

AWS CLI

检测来自多个输入文本的实体

以下 batch-detect-entities 示例分析多个输入文本并返回每个文本的命名实体。预训练模型的置信度分数也是每个预测的输出。


aws comprehend batch-detect-entities \
    --language-code en \
    --text-list "Dear Jane, Your AnyCompany Financial Services LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st." "Please send customer feedback to Sunshine Spa, 123 Main St, Anywhere or to Alice at AnySpa@example.com."

输出：


{
    "ResultList": [
        {
            "Index": 0,
            "Entities": [
                {
                    "Score": 0.9985517859458923,
                    "Type": "PERSON",
                    "Text": "Jane",
                    "BeginOffset": 5,
                    "EndOffset": 9
                },
                {
                    "Score": 0.9767839312553406,
                    "Type": "ORGANIZATION",
                    "Text": "AnyCompany Financial Services, LLC",
                    "BeginOffset": 16,
                    "EndOffset": 50
                },
                {
                    "Score": 0.9856694936752319,
                    "Type": "OTHER",
                    "Text": "1111-XXXX-1111-XXXX",
                    "BeginOffset": 71,
                    "EndOffset": 90
                },
                {
                    "Score": 0.9652159810066223,
                    "Type": "QUANTITY",
                    "Text": ".53",
                    "BeginOffset": 116,
                    "EndOffset": 119
                },
                {
                    "Score": 0.9986667037010193,
                    "Type": "DATE",
                    "Text": "July 31st",
                    "BeginOffset": 135,
                    "EndOffset": 144
                }
            ]
        },
        {
            "Index": 1,
            "Entities": [
                {
                    "Score": 0.720084547996521,
                    "Type": "ORGANIZATION",
                    "Text": "Sunshine Spa",
                    "BeginOffset": 33,
                    "EndOffset": 45
                },
                {
                    "Score": 0.9865870475769043,
                    "Type": "LOCATION",
                    "Text": "123 Main St",
                    "BeginOffset": 47,
                    "EndOffset": 58
                },
                {
                    "Score": 0.5895616412162781,
                    "Type": "LOCATION",
                    "Text": "Anywhere",
                    "BeginOffset": 60,
                    "EndOffset": 68
                },
                {
                    "Score": 0.6809214353561401,
                    "Type": "PERSON",
                    "Text": "Alice",
                    "BeginOffset": 75,
                    "EndOffset": 80
                },
                {
                    "Score": 0.9979087114334106,
                    "Type": "OTHER",
                    "Text": "AnySpa@example.com",
                    "BeginOffset": 84,
                    "EndOffset": 99
                }
            ]
        }
    ],
    "ErrorList": []
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的实体。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 BatchDetectEntities。

以下代码示例演示了如何使用 batch-detect-key-phrases。

AWS CLI

检测多个文本输入的关键短语

以下 batch-detect-key-phrases 示例分析多个输入文本并返回每个文本的关键名词短语。也会输出每个预测的预训练模型的置信度分数。


aws comprehend batch-detect-key-phrases \
    --language-code en \
    --text-list "Hello Zhang Wei, I am John, writing to you about the trip for next Saturday." "Dear Jane, Your AnyCompany Financial Services LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st." "Please send customer feedback to Sunshine Spa, 123 Main St, Anywhere or to Alice at AnySpa@example.com."

输出：


{
    "ResultList": [
        {
            "Index": 0,
            "KeyPhrases": [
                {
                    "Score": 0.99700927734375,
                    "Text": "Zhang Wei",
                    "BeginOffset": 6,
                    "EndOffset": 15
                },
                {
                    "Score": 0.9929308891296387,
                    "Text": "John",
                    "BeginOffset": 22,
                    "EndOffset": 26
                },
                {
                    "Score": 0.9997230172157288,
                    "Text": "the trip",
                    "BeginOffset": 49,
                    "EndOffset": 57
                },
                {
                    "Score": 0.9999470114707947,
                    "Text": "next Saturday",
                    "BeginOffset": 62,
                    "EndOffset": 75
                }
            ]
        },
        {
            "Index": 1,
            "KeyPhrases": [
                {
                    "Score": 0.8358274102210999,
                    "Text": "Dear Jane",
                    "BeginOffset": 0,
                    "EndOffset": 9
                },
                {
                    "Score": 0.989359974861145,
                    "Text": "Your AnyCompany Financial Services",
                    "BeginOffset": 11,
                    "EndOffset": 45
                },
                {
                    "Score": 0.8812323808670044,
                    "Text": "LLC credit card account 1111-XXXX-1111-XXXX",
                    "BeginOffset": 47,
                    "EndOffset": 90
                },
                {
                    "Score": 0.9999381899833679,
                    "Text": "a minimum payment",
                    "BeginOffset": 95,
                    "EndOffset": 112
                },
                {
                    "Score": 0.9997439980506897,
                    "Text": ".53",
                    "BeginOffset": 116,
                    "EndOffset": 119
                },
                {
                    "Score": 0.996875524520874,
                    "Text": "July 31st",
                    "BeginOffset": 135,
                    "EndOffset": 144
                }
            ]
        },
        {
            "Index": 2,
            "KeyPhrases": [
                {
                    "Score": 0.9990295767784119,
                    "Text": "customer feedback",
                    "BeginOffset": 12,
                    "EndOffset": 29
                },
                {
                    "Score": 0.9994127750396729,
                    "Text": "Sunshine Spa",
                    "BeginOffset": 33,
                    "EndOffset": 45
                },
                {
                    "Score": 0.9892991185188293,
                    "Text": "123 Main St",
                    "BeginOffset": 47,
                    "EndOffset": 58
                },
                {
                    "Score": 0.9969810843467712,
                    "Text": "Alice",
                    "BeginOffset": 75,
                    "EndOffset": 80
                },
                {
                    "Score": 0.9703696370124817,
                    "Text": "AnySpa@example.com",
                    "BeginOffset": 84,
                    "EndOffset": 99
                }
            ]
        }
    ],
    "ErrorList": []
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的关键词。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 BatchDetectKeyPhrases。

以下代码示例演示了如何使用 batch-detect-sentiment。

AWS CLI

检测多个输入文本的主导情绪

以下 batch-detect-sentiment 示例分析多个输入文本，并返回每个文本的主导情绪（POSITIVE、NEUTRAL、MIXED 或 NEGATIVE）。


aws comprehend batch-detect-sentiment \
    --text-list "That movie was very boring, I can't believe it was over four hours long." "It is a beautiful day for hiking today." "My meal was okay, I'm excited to try other restaurants." \
    --language-code en

输出：


{
    "ResultList": [
        {
            "Index": 0,
            "Sentiment": "NEGATIVE",
            "SentimentScore": {
                "Positive": 0.00011316669406369328,
                "Negative": 0.9995445609092712,
                "Neutral": 0.00014722718333359808,
                "Mixed": 0.00019498742767609656
            }
        },
        {
            "Index": 1,
            "Sentiment": "POSITIVE",
            "SentimentScore": {
                "Positive": 0.9981263279914856,
                "Negative": 0.00015240783977787942,
                "Neutral": 0.0013876151060685515,
                "Mixed": 0.00033366199932061136
            }
        },
        {
            "Index": 2,
            "Sentiment": "MIXED",
            "SentimentScore": {
                "Positive": 0.15930435061454773,
                "Negative": 0.11471917480230331,
                "Neutral": 0.26897063851356506,
                "Mixed": 0.45700588822364807
            }
        }
    ],
    "ErrorList": []
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的情绪。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 BatchDetectSentiment。

以下代码示例演示了如何使用 batch-detect-syntax。

AWS CLI

检查多个输入文本中单词的语法和语音部分

以下 batch-detect-syntax 示例分析多个输入文本的语法并返回语音的不同部分。预训练模型的置信度分数也是每个预测的输出。


aws comprehend batch-detect-syntax \
    --text-list "It is a beautiful day." "Can you please pass the salt?" "Please pay the bill before the 31st." \
    --language-code en

输出：


{
    "ResultList": [
        {
            "Index": 0,
            "SyntaxTokens": [
                {
                    "TokenId": 1,
                    "Text": "It",
                    "BeginOffset": 0,
                    "EndOffset": 2,
                    "PartOfSpeech": {
                        "Tag": "PRON",
                        "Score": 0.9999740719795227
                    }
                },
                {
                    "TokenId": 2,
                    "Text": "is",
                    "BeginOffset": 3,
                    "EndOffset": 5,
                    "PartOfSpeech": {
                        "Tag": "VERB",
                        "Score": 0.999937117099762
                    }
                },
                {
                    "TokenId": 3,
                    "Text": "a",
                    "BeginOffset": 6,
                    "EndOffset": 7,
                    "PartOfSpeech": {
                        "Tag": "DET",
                        "Score": 0.9999926686286926
                    }
                },
                {
                    "TokenId": 4,
                    "Text": "beautiful",
                    "BeginOffset": 8,
                    "EndOffset": 17,
                    "PartOfSpeech": {
                        "Tag": "ADJ",
                        "Score": 0.9987891912460327
                    }
                },
                {
                    "TokenId": 5,
                    "Text": "day",
                    "BeginOffset": 18,
                    "EndOffset": 21,
                    "PartOfSpeech": {
                        "Tag": "NOUN",
                        "Score": 0.9999778866767883
                    }
                },
                {
                    "TokenId": 6,
                    "Text": ".",
                    "BeginOffset": 21,
                    "EndOffset": 22,
                    "PartOfSpeech": {
                        "Tag": "PUNCT",
                        "Score": 0.9999974966049194
                    }
                }
            ]
        },
        {
            "Index": 1,
            "SyntaxTokens": [
                {
                    "TokenId": 1,
                    "Text": "Can",
                    "BeginOffset": 0,
                    "EndOffset": 3,
                    "PartOfSpeech": {
                        "Tag": "AUX",
                        "Score": 0.9999770522117615
                    }
                },
                {
                    "TokenId": 2,
                    "Text": "you",
                    "BeginOffset": 4,
                    "EndOffset": 7,
                    "PartOfSpeech": {
                        "Tag": "PRON",
                        "Score": 0.9999986886978149
                    }
                },
                {
                    "TokenId": 3,
                    "Text": "please",
                    "BeginOffset": 8,
                    "EndOffset": 14,
                    "PartOfSpeech": {
                        "Tag": "INTJ",
                        "Score": 0.9681622385978699
                    }
                },
                {
                    "TokenId": 4,
                    "Text": "pass",
                    "BeginOffset": 15,
                    "EndOffset": 19,
                    "PartOfSpeech": {
                        "Tag": "VERB",
                        "Score": 0.9999874830245972
                    }
                },
                {
                    "TokenId": 5,
                    "Text": "the",
                    "BeginOffset": 20,
                    "EndOffset": 23,
                    "PartOfSpeech": {
                        "Tag": "DET",
                        "Score": 0.9999827146530151
                    }
                },
                {
                    "TokenId": 6,
                    "Text": "salt",
                    "BeginOffset": 24,
                    "EndOffset": 28,
                    "PartOfSpeech": {
                        "Tag": "NOUN",
                        "Score": 0.9995040893554688
                    }
                },
                {
                    "TokenId": 7,
                    "Text": "?",
                    "BeginOffset": 28,
                    "EndOffset": 29,
                    "PartOfSpeech": {
                        "Tag": "PUNCT",
                        "Score": 0.999998152256012
                    }
                }
            ]
        },
        {
            "Index": 2,
            "SyntaxTokens": [
                {
                    "TokenId": 1,
                    "Text": "Please",
                    "BeginOffset": 0,
                    "EndOffset": 6,
                    "PartOfSpeech": {
                        "Tag": "INTJ",
                        "Score": 0.9997857809066772
                    }
                },
                {
                    "TokenId": 2,
                    "Text": "pay",
                    "BeginOffset": 7,
                    "EndOffset": 10,
                    "PartOfSpeech": {
                        "Tag": "VERB",
                        "Score": 0.9999252557754517
                    }
                },
                {
                    "TokenId": 3,
                    "Text": "the",
                    "BeginOffset": 11,
                    "EndOffset": 14,
                    "PartOfSpeech": {
                        "Tag": "DET",
                        "Score": 0.9999842643737793
                    }
                },
                {
                    "TokenId": 4,
                    "Text": "bill",
                    "BeginOffset": 15,
                    "EndOffset": 19,
                    "PartOfSpeech": {
                        "Tag": "NOUN",
                        "Score": 0.9999588131904602
                    }
                },
                {
                    "TokenId": 5,
                    "Text": "before",
                    "BeginOffset": 20,
                    "EndOffset": 26,
                    "PartOfSpeech": {
                        "Tag": "ADP",
                        "Score": 0.9958304762840271
                    }
                },
                {
                    "TokenId": 6,
                    "Text": "the",
                    "BeginOffset": 27,
                    "EndOffset": 30,
                    "PartOfSpeech": {
                        "Tag": "DET",
                        "Score": 0.9999947547912598
                    }
                },
                {
                    "TokenId": 7,
                    "Text": "31st",
                    "BeginOffset": 31,
                    "EndOffset": 35,
                    "PartOfSpeech": {
                        "Tag": "NOUN",
                        "Score": 0.9924124479293823
                    }
                },
                {
                    "TokenId": 8,
                    "Text": ".",
                    "BeginOffset": 35,
                    "EndOffset": 36,
                    "PartOfSpeech": {
                        "Tag": "PUNCT",
                        "Score": 0.9999955892562866
                    }
                }
            ]
        }
    ],
    "ErrorList": []
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的语法分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 BatchDetectSyntax。

以下代码示例演示了如何使用 batch-detect-targeted-sentiment。

AWS CLI

检测多个输入文本的情绪和每个命名实体

以下 batch-detect-targeted-sentiment 示例分析多个输入文本，并返回命名实体以及每个实体附带的主导情绪。预训练模型的置信度分数也是每个预测的输出。


aws comprehend batch-detect-targeted-sentiment \
    --language-code en \
    --text-list "That movie was really boring, the original was way more entertaining" "The trail is extra beautiful today." "My meal was just okay."

输出：


{
    "ResultList": [
        {
            "Index": 0,
            "Entities": [
                {
                    "DescriptiveMentionIndex": [
                        0
                    ],
                    "Mentions": [
                        {
                            "Score": 0.9999009966850281,
                            "GroupScore": 1.0,
                            "Text": "movie",
                            "Type": "MOVIE",
                            "MentionSentiment": {
                                "Sentiment": "NEGATIVE",
                                "SentimentScore": {
                                    "Positive": 0.13887299597263336,
                                    "Negative": 0.8057460188865662,
                                    "Neutral": 0.05525200068950653,
                                    "Mixed": 0.00012799999967683107
                                }
                            },
                            "BeginOffset": 5,
                            "EndOffset": 10
                        }
                    ]
                },
                {
                    "DescriptiveMentionIndex": [
                        0
                    ],
                    "Mentions": [
                        {
                            "Score": 0.9921110272407532,
                            "GroupScore": 1.0,
                            "Text": "original",
                            "Type": "MOVIE",
                            "MentionSentiment": {
                                "Sentiment": "POSITIVE",
                                "SentimentScore": {
                                    "Positive": 0.9999989867210388,
                                    "Negative": 9.999999974752427e-07,
                                    "Neutral": 0.0,
                                    "Mixed": 0.0
                                }
                            },
                            "BeginOffset": 34,
                            "EndOffset": 42
                        }
                    ]
                }
            ]
        },
        {
            "Index": 1,
            "Entities": [
                {
                    "DescriptiveMentionIndex": [
                        0
                    ],
                    "Mentions": [
                        {
                            "Score": 0.7545599937438965,
                            "GroupScore": 1.0,
                            "Text": "trail",
                            "Type": "OTHER",
                            "MentionSentiment": {
                                "Sentiment": "POSITIVE",
                                "SentimentScore": {
                                    "Positive": 1.0,
                                    "Negative": 0.0,
                                    "Neutral": 0.0,
                                    "Mixed": 0.0
                                }
                            },
                            "BeginOffset": 4,
                            "EndOffset": 9
                        }
                    ]
                },
                {
                    "DescriptiveMentionIndex": [
                        0
                    ],
                    "Mentions": [
                        {
                            "Score": 0.9999960064888,
                            "GroupScore": 1.0,
                            "Text": "today",
                            "Type": "DATE",
                            "MentionSentiment": {
                                "Sentiment": "NEUTRAL",
                                "SentimentScore": {
                                    "Positive": 9.000000318337698e-06,
                                    "Negative": 1.9999999949504854e-06,
                                    "Neutral": 0.9999859929084778,
                                    "Mixed": 3.999999989900971e-06
                                }
                            },
                            "BeginOffset": 29,
                            "EndOffset": 34
                        }
                    ]
                }
            ]
        },
        {
            "Index": 2,
            "Entities": [
                {
                    "DescriptiveMentionIndex": [
                        0
                    ],
                    "Mentions": [
                        {
                            "Score": 0.9999880194664001,
                            "GroupScore": 1.0,
                            "Text": "My",
                            "Type": "PERSON",
                            "MentionSentiment": {
                                "Sentiment": "NEUTRAL",
                                "SentimentScore": {
                                    "Positive": 0.0,
                                    "Negative": 0.0,
                                    "Neutral": 1.0,
                                    "Mixed": 0.0
                                }
                            },
                            "BeginOffset": 0,
                            "EndOffset": 2
                        }
                    ]
                },
                {
                    "DescriptiveMentionIndex": [
                        0
                    ],
                    "Mentions": [
                        {
                            "Score": 0.9995260238647461,
                            "GroupScore": 1.0,
                            "Text": "meal",
                            "Type": "OTHER",
                            "MentionSentiment": {
                                "Sentiment": "NEUTRAL",
                                "SentimentScore": {
                                    "Positive": 0.04695599898695946,
                                    "Negative": 0.003226999891921878,
                                    "Neutral": 0.6091709733009338,
                                    "Mixed": 0.34064599871635437
                                }
                            },
                            "BeginOffset": 3,
                            "EndOffset": 7
                        }
                    ]
                }
            ]
        }
    ],
    "ErrorList": []
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的目标情绪。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 BatchDetectTargetedSentiment。

以下代码示例演示了如何使用 classify-document。

AWS CLI

使用指定模型端点对文档进行分类

以下 classify-document 示例对带有自定义模型端点的文档进行分类。此示例中的模型是在包含标记为垃圾邮件、非垃圾邮件或“ham”短信的数据集中训练的。


aws comprehend classify-document \
    --endpoint-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint \
    --text "CONGRATULATIONS! TXT 1235550100 to win $5000"

输出：


{
    "Classes": [
        {
            "Name": "spam",
            "Score": 0.9998599290847778
        },
        {
            "Name": "ham",
            "Score": 0.00014001205272506922
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的自定义分类。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ClassifyDocument。

以下代码示例演示了如何使用 contains-pii-entities。

AWS CLI

分析输入文本中是否存在 PII 信息

以下 contains-pii-entities 示例分析输入文本中是否存在个人身份信息（PII），并返回已识别的 PII 实体类型的标签，例如姓名、地址、银行账号或电话号码。


aws comprehend contains-pii-entities \
    --language-code en \
    --text "Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card
        account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st. Based on your autopay settings,
        we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000.
        Customer feedback for Sunshine Spa, 100 Main St, Anywhere. Send comments to Alice at AnySpa@example.com."

输出：


{
    "Labels": [
        {
            "Name": "NAME",
            "Score": 1.0
        },
        {
            "Name": "EMAIL",
            "Score": 1.0
        },
        {
            "Name": "BANK_ACCOUNT_NUMBER",
            "Score": 0.9995794296264648
        },
        {
            "Name": "BANK_ROUTING",
            "Score": 0.9173126816749573
        },
        {
            "Name": "CREDIT_DEBIT_NUMBER",
            "Score": 1.0
        }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的个人身份信息（PII）。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ContainsPiiEntities。

以下代码示例演示了如何使用 create-dataset。

AWS CLI

创建飞轮数据集

以下 create-dataset 示例创建一个飞轮数据集。该数据集将用作 --dataset-type 标签指定的其他训练数据。


aws comprehend create-dataset \
    --flywheel-arn arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity \
    --dataset-name example-dataset \
    --dataset-type "TRAIN" \
    --input-data-config file://inputConfig.json

file://inputConfig.json 的内容：


{
    "DataFormat": "COMPREHEND_CSV",
    "DocumentClassifierInputDataConfig": {
        "S3Uri": "s3://DOC-EXAMPLE-BUCKET/training-data.csv"
    }
}

输出：


{
    "DatasetArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity/dataset/example-dataset"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的飞轮概述。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 CreateDataset。

以下代码示例演示了如何使用 create-document-classifier。

AWS CLI

创建文档分类器对文档进行分类

以下 create-document-classifier 示例启动文档分类器模型的训练过程。训练数据文件 training.csv 位于 --input-data-config 标签处。training.csv 是一个两列文档，其中第一列提供标签或分类，第二列提供文档。


aws comprehend create-document-classifier \
    --document-classifier-name example-classifier \
    --data-access-arn arn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE \
    --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET/" \
    --language-code en

输出：


{
    "DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的自定义分类。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 CreateDocumentClassifier。

以下代码示例演示了如何使用 create-endpoint。

AWS CLI

为自定义模型创建端点

以下 create-endpoint 示例为之前训练的自定义模型的同步推理创建端点。


aws comprehend create-endpoint \
    --endpoint-name example-classifier-endpoint-1 \
    --model-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier \
    --desired-inference-units 1

输出：


{
    "EndpointArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint-1"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的管理 Amazon Comprehend 端点。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 CreateEndpoint。

以下代码示例演示了如何使用 create-entity-recognizer。

AWS CLI

创建自定义实体识别器

以下 create-entity-recognizer 示例启动自定义实体识别器模型的训练过程。此示例使用包含训练文档 raw_text.csv 和 CSV 实体列表 entity_list.csv 的 CSV 文件来训练模型。entity-list.csv 包含以下列：文本和类型。


aws comprehend create-entity-recognizer \
    --recognizer-name example-entity-recognizer
    --data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
    --input-data-config "EntityTypes=[{Type=DEVICE}],Documents={S3Uri=s3://DOC-EXAMPLE-BUCKET/trainingdata/raw_text.csv},EntityList={S3Uri=s3://DOC-EXAMPLE-BUCKET/trainingdata/entity_list.csv}"
    --language-code en

输出：


{
    "EntityRecognizerArn": "arn:aws:comprehend:us-west-2:111122223333:example-entity-recognizer/entityrecognizer1"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的自定义实体识别。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 CreateEntityRecognizer。

以下代码示例演示了如何使用 create-flywheel。

AWS CLI

创建飞轮

以下 create-flywheel 示例创建一个飞轮来编排文档分类或实体识别模型的持续训练。此示例中的飞轮是为了管理 --active-model-arn 标签指定的现有训练模型。创建飞轮时，会在 --input-data-lake 标签处创建一个数据湖。


aws comprehend create-flywheel \
    --flywheel-name example-flywheel \
    --active-model-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-model/version/1 \
    --data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
    --data-lake-s3-uri "s3://DOC-EXAMPLE-BUCKET"

输出：


{
    "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的飞轮概述。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 CreateFlywheel。

以下代码示例演示了如何使用 delete-document-classifier。

AWS CLI

删除自定义文档分类器

以下 delete-document-classifier 示例删除了自定义文档分类器模型。


aws comprehend delete-document-classifier \
    --document-classifier-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-1

此命令不生成任何输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的管理 Amazon Comprehend 端点。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DeleteDocumentClassifier。

以下代码示例演示了如何使用 delete-endpoint。

AWS CLI

删除自定义模型的端点

以下 delete-endpoint 示例删除指定模型的端点。必须删除所有端点才能删除模型。


aws comprehend delete-endpoint \
    --endpoint-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint-1

此命令不生成任何输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的管理 Amazon Comprehend 端点。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DeleteEndpoint。

以下代码示例演示了如何使用 delete-entity-recognizer。

AWS CLI

删除自定义实体识别器模型

以下 delete-entity-recognizer 示例删除自定义实体识别器模型。


aws comprehend delete-entity-recognizer \
    --entity-recognizer-arn arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/example-entity-recognizer-1

此命令不生成任何输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的管理 Amazon Comprehend 端点。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DeleteEntityRecognizer。

以下代码示例演示了如何使用 delete-flywheel。

AWS CLI

删除飞轮

以下 delete-flywheel 示例删除飞轮。与该飞轮关联的数据湖或模型不会删除。


aws comprehend delete-flywheel \
    --flywheel-arn arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel-1

此命令不生成任何输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的飞轮概述。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DeleteFlywheel。

以下代码示例演示了如何使用 delete-resource-policy。

AWS CLI

删除基于资源的策略

以下 delete-resource-policy 示例从 Amazon Comprehend 资源中删除基于资源的策略。


aws comprehend delete-resource-policy \
    --resource-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-1/version/1

此命令不生成任何输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的在 AWS 账户之间复制自定义模型。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DeleteResourcePolicy。

以下代码示例演示了如何使用 describe-dataset。

AWS CLI

描述飞轮数据集

以下 describe-dataset 示例获取飞轮数据集的属性。


aws comprehend describe-dataset \
    --dataset-arn arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity/dataset/example-dataset

输出：


{
    "DatasetProperties": {
        "DatasetArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity/dataset/example-dataset",
        "DatasetName": "example-dataset",
        "DatasetType": "TRAIN",
        "DatasetS3Uri": "s3://DOC-EXAMPLE-BUCKET/flywheel-entity/schemaVersion=1/12345678A123456Z/datasets/example-dataset/20230616T203710Z/",
        "Status": "CREATING",
        "CreationTime": "2023-06-16T20:37:10.400000+00:00"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的飞轮概述。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeDataset。

以下代码示例演示了如何使用 describe-document-classification-job。

AWS CLI

描述文档分类作业

以下 describe-document-classification-job 示例将获取异步文档分类作业的属性。


aws comprehend describe-document-classification-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "DocumentClassificationJobProperties": {
        "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
        "JobArn": "arn:aws:comprehend:us-west-2:111122223333:document-classification-job/123456abcdeb0e11022f22a11EXAMPLE",
        "JobName": "exampleclassificationjob",
        "JobStatus": "COMPLETED",
        "SubmitTime": "2023-06-14T17:09:51.788000+00:00",
        "EndTime": "2023-06-14T17:15:58.582000+00:00",
        "DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/mymodel/version/1",
        "InputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-BUCKET/jobdata/",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-CLN-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的自定义分类。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeDocumentClassificationJob。

以下代码示例演示了如何使用 describe-document-classifier。

AWS CLI

描述文档分类器

以下 describe-document-classifier 示例将获取自定义文档分类器模型的属性。


aws comprehend describe-document-classifier \
    --document-classifier-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-1

输出：


{
    "DocumentClassifierProperties": {
        "DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-1",
        "LanguageCode": "en",
        "Status": "TRAINED",
        "SubmitTime": "2023-06-13T19:04:15.735000+00:00",
        "EndTime": "2023-06-13T19:42:31.752000+00:00",
        "TrainingStartTime": "2023-06-13T19:08:20.114000+00:00",
        "TrainingEndTime": "2023-06-13T19:41:35.080000+00:00",
        "InputDataConfig": {
            "DataFormat": "COMPREHEND_CSV",
            "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata"
        },
        "OutputDataConfig": {},
        "ClassifierMetadata": {
            "NumberOfLabels": 3,
            "NumberOfTrainedDocuments": 5016,
            "NumberOfTestDocuments": 557,
            "EvaluationMetrics": {
                "Accuracy": 0.9856,
                "Precision": 0.9919,
                "Recall": 0.9459,
                "F1Score": 0.9673,
                "MicroPrecision": 0.9856,
                "MicroRecall": 0.9856,
                "MicroF1Score": 0.9856,
                "HammingLoss": 0.0144
            }
        },
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role",
        "Mode": "MULTI_CLASS"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的创建和管理自定义模型。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeDocumentClassifier。

以下代码示例演示了如何使用 describe-dominant-language-detection-job。

AWS CLI

描述主要语言检测作业。

以下 describe-dominant-language-detection-job 示例获取异步主要语言检测作业的属性。


aws comprehend describe-dominant-language-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "DominantLanguageDetectionJobProperties": {
        "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
        "JobArn": "arn:aws:comprehend:us-west-2:111122223333:dominant-language-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
        "JobName": "languageanalysis1",
        "JobStatus": "IN_PROGRESS",
        "SubmitTime": "2023-06-09T18:10:38.037000+00:00",
        "InputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-BUCKET",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-LANGUAGE-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeDominantLanguageDetectionJob。

以下代码示例演示了如何使用 describe-endpoint。

AWS CLI

描述指定端点

以下 describe-endpoint 示例获取指定模型的端点属性。


aws comprehend describe-endpoint \
    --endpoint-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint

输出：


{
    "EndpointProperties": {
        "EndpointArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint,
        "Status": "IN_SERVICE",
        "ModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier1",
        "DesiredModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier1",
        "DesiredInferenceUnits": 1,
        "CurrentInferenceUnits": 1,
        "CreationTime": "2023-06-13T20:32:54.526000+00:00",
        "LastModifiedTime": "2023-06-13T20:32:54.526000+00:00"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的管理 Amazon Comprehend 端点。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeEndpoint。

以下代码示例演示了如何使用 describe-entities-detection-job。

AWS CLI

描述实体检测作业

以下 describe-entities-detection-job 示例获取异步实体检测作业的属性。


aws comprehend describe-entities-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "EntitiesDetectionJobProperties": {
        "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
        "JobArn": "arn:aws:comprehend:us-west-2:111122223333:entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
        "JobName": "example-entity-detector",
        "JobStatus": "COMPLETED",
        "SubmitTime": "2023-06-08T21:30:15.323000+00:00",
        "EndTime": "2023-06-08T21:40:23.509000+00:00",
        "InputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-BUCKET/thefolder/111122223333-NER-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "LanguageCode": "en",
        "DataAccessRoleArn": "arn:aws:iam::12345678012:role/service-role/AmazonComprehendServiceRole-example-role"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeEntitiesDetectionJob。

以下代码示例演示了如何使用 describe-entity-recognizer。

AWS CLI

描述实体识别器

以下 describe-entity-recognizer 示例获取自定义实体识别器模型的属性。


aws comprehend describe-entity-recognizer \
    entity-recognizer-arn arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/business-recongizer-1/version/1

输出：


{
    "EntityRecognizerProperties": {
        "EntityRecognizerArn": "arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/business-recongizer-1/version/1",
        "LanguageCode": "en",
        "Status": "TRAINED",
        "SubmitTime": "2023-06-14T20:44:59.631000+00:00",
        "EndTime": "2023-06-14T20:59:19.532000+00:00",
        "TrainingStartTime": "2023-06-14T20:48:52.811000+00:00",
        "TrainingEndTime": "2023-06-14T20:58:11.473000+00:00",
        "InputDataConfig": {
            "DataFormat": "COMPREHEND_CSV",
            "EntityTypes": [
                {
                    "Type": "BUSINESS"
                }
            ],
            "Documents": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata/dataset/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "EntityList": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata/entity.csv"
            }
        },
        "RecognizerMetadata": {
            "NumberOfTrainedDocuments": 1814,
            "NumberOfTestDocuments": 486,
            "EvaluationMetrics": {
                "Precision": 100.0,
                "Recall": 100.0,
                "F1Score": 100.0
            },
            "EntityTypes": [
                {
                    "Type": "BUSINESS",
                    "EvaluationMetrics": {
                        "Precision": 100.0,
                        "Recall": 100.0,
                        "F1Score": 100.0
                    },
                    "NumberOfTrainMentions": 1520
                }
            ]
        },
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role",
        "VersionName": "1"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的自定义实体识别。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeEntityRecognizer。

以下代码示例演示了如何使用 describe-events-detection-job。

AWS CLI

描述事件检测作业。

以下 describe-events-detection-job 示例获取异步事件检测作业的属性。


aws comprehend describe-events-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "EventsDetectionJobProperties": {
        "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
        "JobArn": "arn:aws:comprehend:us-west-2:111122223333:events-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
        "JobName": "events_job_1",
        "JobStatus": "IN_PROGRESS",
        "SubmitTime": "2023-06-12T18:45:56.054000+00:00",
        "InputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-BUCKET/EventsData",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-EVENTS-123456abcdeb0e11022f22a11EXAMPLE/output/"
        },
        "LanguageCode": "en",
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role",
        "TargetEventTypes": [
            "BANKRUPTCY",
            "EMPLOYMENT",
            "CORPORATE_ACQUISITION",
            "CORPORATE_MERGER",
            "INVESTMENT_GENERAL"
        ]
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeEventsDetectionJob。

以下代码示例演示了如何使用 describe-flywheel-iteration。

AWS CLI

描述飞轮迭代

以下 describe-flywheel-iteration 示例获取飞轮迭代的属性。


aws comprehend describe-flywheel-iteration \
    --flywheel-arn arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel \
    --flywheel-iteration-id 20232222AEXAMPLE

输出：


{
    "FlywheelIterationProperties": {
        "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity",
        "FlywheelIterationId": "20232222AEXAMPLE",
        "CreationTime": "2023-06-16T21:10:26.385000+00:00",
        "EndTime": "2023-06-16T23:33:16.827000+00:00",
        "Status": "COMPLETED",
        "Message": "FULL_ITERATION: Flywheel iteration performed all functions successfully.",
        "EvaluatedModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1",
        "EvaluatedModelMetrics": {
            "AverageF1Score": 0.7742663922375772,
            "AveragePrecision": 0.8287636394041166,
            "AverageRecall": 0.7427084833645399,
            "AverageAccuracy": 0.8795394154118689
        },
        "TrainedModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/Comprehend-Generated-v1-bb52d585",
        "TrainedModelMetrics": {
            "AverageF1Score": 0.9767700253081214,
            "AveragePrecision": 0.9767700253081214,
            "AverageRecall": 0.9767700253081214,
            "AverageAccuracy": 0.9858281665190434
        },
        "EvaluationManifestS3Prefix": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/flywheel-entity/schemaVersion=1/20230616T200543Z/evaluation/20230616T211026Z/"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的飞轮概述。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeFlywheelIteration。

以下代码示例演示了如何使用 describe-flywheel。

AWS CLI

描述飞轮

以下 describe-flywheel 示例获取飞轮的属性。在此示例中，与飞轮关联的模型是一个自定义分类器模型，该模型经过训练，可以将文档分类为垃圾邮件、非垃圾邮件或“ham”。


aws comprehend describe-flywheel \
    --flywheel-arn arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel

输出：


{
    "FlywheelProperties": {
        "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel",
        "ActiveModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-model/version/1",
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role",
        "TaskConfig": {
            "LanguageCode": "en",
            "DocumentClassificationConfig": {
                "Mode": "MULTI_CLASS",
                "Labels": [
                    "ham",
                    "spam"
                ]
            }
        },
        "DataLakeS3Uri": "s3://DOC-EXAMPLE-BUCKET/example-flywheel/schemaVersion=1/20230616T200543Z/",
        "DataSecurityConfig": {},
        "Status": "ACTIVE",
        "ModelType": "DOCUMENT_CLASSIFIER",
        "CreationTime": "2023-06-16T20:05:43.242000+00:00",
        "LastModifiedTime": "2023-06-16T20:21:43.567000+00:00"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的飞轮概述。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeFlywheel。

以下代码示例演示了如何使用 describe-key-phrases-detection-job。

AWS CLI

描述关键短语检测作业

以下 describe-key-phrases-detection-job 示例获取异步关键短语检测作业的属性。


aws comprehend describe-key-phrases-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "KeyPhrasesDetectionJobProperties": {
        "JobId": "69aa080c00fc68934a6a98f10EXAMPLE",
        "JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/69aa080c00fc68934a6a98f10EXAMPLE",
        "JobName": "example-key-phrases-detection-job",
        "JobStatus": "COMPLETED",
        "SubmitTime": 1686606439.177,
        "EndTime": 1686606806.157,
        "InputDataConfig": {
            "S3Uri": "s3://dereksbucket1001/EventsData/",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://dereksbucket1002/testfolder/111122223333-KP-69aa080c00fc68934a6a98f10EXAMPLE/output/output.tar.gz"
        },
        "LanguageCode": "en",
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-testrole"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeKeyPhrasesDetectionJob。

以下代码示例演示了如何使用 describe-pii-entities-detection-job。

AWS CLI

描述 PII 实体检测作业

以下 describe-pii-entities-detection-job 示例获取异步 PII 实体检测作业的属性。


aws comprehend describe-pii-entities-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "PiiEntitiesDetectionJobProperties": {
        "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
        "JobArn": "arn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
        "JobName": "example-pii-entities-job",
        "JobStatus": "IN_PROGRESS",
        "SubmitTime": "2023-06-08T21:30:15.323000+00:00",
        "EndTime": "2023-06-08T21:40:23.509000+00:00",
        "InputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-BUCKET/thefolder/111122223333-NER-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "LanguageCode": "en",
        "DataAccessRoleArn": "arn:aws:iam::12345678012:role/service-role/AmazonComprehendServiceRole-example-role"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribePiiEntitiesDetectionJob。

以下代码示例演示了如何使用 describe-resource-policy。

AWS CLI

描述附加到模型的资源策略

以下 describe-resource-policy 示例获取附加到模型的基于资源的策略属性。


aws comprehend describe-resource-policy \
    --resource-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1

输出：


{
    "ResourcePolicy": "{\"Version\":\"2012-10-17\",\"Statement\":[{\"Effect\":\"Allow\",\"Principal\":{\"AWS\":\"arn:aws:iam::444455556666:root\"},\"Action\":\"comprehend:ImportModel\",\"Resource\":\"*\"}]}",
    "CreationTime": "2023-06-19T18:44:26.028000+00:00",
    "LastModifiedTime": "2023-06-19T18:53:02.002000+00:00",
    "PolicyRevisionId": "baa675d069d07afaa2aa3106ae280f61"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的在 AWS 账户之间复制自定义模型。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeResourcePolicy。

以下代码示例演示了如何使用 describe-sentiment-detection-job。

AWS CLI

描述情绪检测作业

以下 describe-sentiment-detection-job 示例获取异步情绪检测作业的属性。


aws comprehend describe-sentiment-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "SentimentDetectionJobProperties": {
        "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
        "JobArn": "arn:aws:comprehend:us-west-2:111122223333:sentiment-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
        "JobName": "movie_review_analysis",
        "JobStatus": "IN_PROGRESS",
        "SubmitTime": "2023-06-09T23:16:15.956000+00:00",
        "InputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-BUCKET/MovieData",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "LanguageCode": "en",
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeSentimentDetectionJob。

以下代码示例演示了如何使用 describe-targeted-sentiment-detection-job。

AWS CLI

描述目标情绪检测作业

以下 describe-targeted-sentiment-detection-job 示例获取异步目标情绪检测作业的属性。


aws comprehend describe-targeted-sentiment-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "TargetedSentimentDetectionJobProperties": {
        "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
        "JobArn": "arn:aws:comprehend:us-west-2:111122223333:targeted-sentiment-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
        "JobName": "movie_review_analysis",
        "JobStatus": "IN_PROGRESS",
        "SubmitTime": "2023-06-09T23:16:15.956000+00:00",
        "InputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-BUCKET/MovieData",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "LanguageCode": "en",
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeTargetedSentimentDetectionJob。

以下代码示例演示了如何使用 describe-topics-detection-job。

AWS CLI

描述主题检测作业

以下 describe-topics-detection-job 示例获取异步主题检测作业的属性。


aws comprehend describe-topics-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "TopicsDetectionJobProperties": {
        "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
        "JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
        "JobName": "example_topics_detection",
        "JobStatus": "IN_PROGRESS",
        "SubmitTime": "2023-06-09T18:44:43.414000+00:00",
        "InputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-BUCKET",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TOPICS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "NumberOfTopics": 10,
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-examplerole"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DescribeTopicsDetectionJob。

以下代码示例演示了如何使用 detect-dominant-language。

AWS CLI

检测输入文本的主要语言

以下 detect-dominant-language 分析输入文本并识别主要语言。预训练模型的置信度分数也是输出。


aws comprehend detect-dominant-language \
    --text "It is a beautiful day in Seattle."

输出：


{
    "Languages": [
        {
            "LanguageCode": "en",
            "Score": 0.9877256155014038
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的主要语言。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DetectDominantLanguage。

以下代码示例演示了如何使用 detect-entities。

AWS CLI

检测输入文本中的命名实体

以下 detect-entities 示例分析输入文本并返回命名实体。预训练模型的置信度分数也是每个预测的输出。


aws comprehend detect-entities \
    --language-code en \
    --text "Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card \
    account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st. Based on your autopay settings, \
    we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. \
    Customer feedback for Sunshine Spa, 123 Main St, Anywhere. Send comments to Alice at AnySpa@example.com."

输出：


{
    "Entities": [
        {
            "Score": 0.9994556307792664,
            "Type": "PERSON",
            "Text": "Zhang Wei",
            "BeginOffset": 6,
            "EndOffset": 15
        },
        {
            "Score": 0.9981022477149963,
            "Type": "PERSON",
            "Text": "John",
            "BeginOffset": 22,
            "EndOffset": 26
        },
        {
            "Score": 0.9986887574195862,
            "Type": "ORGANIZATION",
            "Text": "AnyCompany Financial Services, LLC",
            "BeginOffset": 33,
            "EndOffset": 67
        },
        {
            "Score": 0.9959119558334351,
            "Type": "OTHER",
            "Text": "1111-XXXX-1111-XXXX",
            "BeginOffset": 88,
            "EndOffset": 107
        },
        {
            "Score": 0.9708039164543152,
            "Type": "QUANTITY",
            "Text": ".53",
            "BeginOffset": 133,
            "EndOffset": 136
        },
        {
            "Score": 0.9987268447875977,
            "Type": "DATE",
            "Text": "July 31st",
            "BeginOffset": 152,
            "EndOffset": 161
        },
        {
            "Score": 0.9858865737915039,
            "Type": "OTHER",
            "Text": "XXXXXX1111",
            "BeginOffset": 271,
            "EndOffset": 281
        },
        {
            "Score": 0.9700471758842468,
            "Type": "OTHER",
            "Text": "XXXXX0000",
            "BeginOffset": 306,
            "EndOffset": 315
        },
        {
            "Score": 0.9591118693351746,
            "Type": "ORGANIZATION",
            "Text": "Sunshine Spa",
            "BeginOffset": 340,
            "EndOffset": 352
        },
        {
            "Score": 0.9797496795654297,
            "Type": "LOCATION",
            "Text": "123 Main St",
            "BeginOffset": 354,
            "EndOffset": 365
        },
        {
            "Score": 0.994929313659668,
            "Type": "PERSON",
            "Text": "Alice",
            "BeginOffset": 394,
            "EndOffset": 399
        },
        {
            "Score": 0.9949769377708435,
            "Type": "OTHER",
            "Text": "AnySpa@example.com",
            "BeginOffset": 403,
            "EndOffset": 418
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的实体。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DetectEntities。

以下代码示例演示了如何使用 detect-key-phrases。

AWS CLI

检测输入文本中的关键词

以下 detect-key-phrases 示例分析输入文本并识别关键名词短语。预训练模型的置信度分数也是每个预测的输出。


aws comprehend detect-key-phrases \
    --language-code en \
    --text "Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card \
        account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st. Based on your autopay settings, \
        we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. \
        Customer feedback for Sunshine Spa, 123 Main St, Anywhere. Send comments to Alice at AnySpa@example.com."

输出：


{
    "KeyPhrases": [
        {
            "Score": 0.8996376395225525,
            "Text": "Zhang Wei",
            "BeginOffset": 6,
            "EndOffset": 15
        },
        {
            "Score": 0.9992469549179077,
            "Text": "John",
            "BeginOffset": 22,
            "EndOffset": 26
        },
        {
            "Score": 0.988385021686554,
            "Text": "Your AnyCompany Financial Services",
            "BeginOffset": 28,
            "EndOffset": 62
        },
        {
            "Score": 0.8740853071212769,
            "Text": "LLC credit card account 1111-XXXX-1111-XXXX",
            "BeginOffset": 64,
            "EndOffset": 107
        },
        {
            "Score": 0.9999437928199768,
            "Text": "a minimum payment",
            "BeginOffset": 112,
            "EndOffset": 129
        },
        {
            "Score": 0.9998900890350342,
            "Text": ".53",
            "BeginOffset": 133,
            "EndOffset": 136
        },
        {
            "Score": 0.9979453086853027,
            "Text": "July 31st",
            "BeginOffset": 152,
            "EndOffset": 161
        },
        {
            "Score": 0.9983011484146118,
            "Text": "your autopay settings",
            "BeginOffset": 172,
            "EndOffset": 193
        },
        {
            "Score": 0.9996572136878967,
            "Text": "your payment",
            "BeginOffset": 211,
            "EndOffset": 223
        },
        {
            "Score": 0.9995037317276001,
            "Text": "the due date",
            "BeginOffset": 227,
            "EndOffset": 239
        },
        {
            "Score": 0.9702621698379517,
            "Text": "your bank account number XXXXXX1111",
            "BeginOffset": 245,
            "EndOffset": 280
        },
        {
            "Score": 0.9179925918579102,
            "Text": "the routing number XXXXX0000.Customer feedback",
            "BeginOffset": 286,
            "EndOffset": 332
        },
        {
            "Score": 0.9978160858154297,
            "Text": "Sunshine Spa",
            "BeginOffset": 337,
            "EndOffset": 349
        },
        {
            "Score": 0.9706913232803345,
            "Text": "123 Main St",
            "BeginOffset": 351,
            "EndOffset": 362
        },
        {
            "Score": 0.9941995143890381,
            "Text": "comments",
            "BeginOffset": 379,
            "EndOffset": 387
        },
        {
            "Score": 0.9759287238121033,
            "Text": "Alice",
            "BeginOffset": 391,
            "EndOffset": 396
        },
        {
            "Score": 0.8376792669296265,
            "Text": "AnySpa@example.com",
            "BeginOffset": 400,
            "EndOffset": 415
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的关键词。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DetectKeyPhrases。

以下代码示例演示了如何使用 detect-pii-entities。

AWS CLI

检测输入文本中的 PII 实体

以下 detect-pii-entities 示例分析输入文本，并识别包含个人身份信息（PII）的实体。预训练模型的置信度分数也是每个预测的输出。


aws comprehend detect-pii-entities \
    --language-code en \
    --text "Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card \
        account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st. Based on your autopay settings, \
        we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. \
        Customer feedback for Sunshine Spa, 123 Main St, Anywhere. Send comments to Alice at AnySpa@example.com."

输出：


{
    "Entities": [
        {
            "Score": 0.9998322129249573,
            "Type": "NAME",
            "BeginOffset": 6,
            "EndOffset": 15
        },
        {
            "Score": 0.9998878240585327,
            "Type": "NAME",
            "BeginOffset": 22,
            "EndOffset": 26
        },
        {
            "Score": 0.9994089603424072,
            "Type": "CREDIT_DEBIT_NUMBER",
            "BeginOffset": 88,
            "EndOffset": 107
        },
        {
            "Score": 0.9999760985374451,
            "Type": "DATE_TIME",
            "BeginOffset": 152,
            "EndOffset": 161
        },
        {
            "Score": 0.9999449253082275,
            "Type": "BANK_ACCOUNT_NUMBER",
            "BeginOffset": 271,
            "EndOffset": 281
        },
        {
            "Score": 0.9999847412109375,
            "Type": "BANK_ROUTING",
            "BeginOffset": 306,
            "EndOffset": 315
        },
        {
            "Score": 0.999925434589386,
            "Type": "ADDRESS",
            "BeginOffset": 354,
            "EndOffset": 365
        },
        {
            "Score": 0.9989161491394043,
            "Type": "NAME",
            "BeginOffset": 394,
            "EndOffset": 399
        },
        {
            "Score": 0.9994171857833862,
            "Type": "EMAIL",
            "BeginOffset": 403,
            "EndOffset": 418
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的个人身份信息（PII）。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DetectPiiEntities。

以下代码示例演示了如何使用 detect-sentiment。

AWS CLI

检测输入文本的情绪

以下 detect-sentiment 示例分析输入文本，并返回占主导地位的情绪（POSITIVE、NEUTRAL、MIXED 或NEGATIVE）的推断。


aws comprehend detect-sentiment \
    --language-code en \
    --text "It is a beautiful day in Seattle"

输出：


{
    "Sentiment": "POSITIVE",
    "SentimentScore": {
        "Positive": 0.9976957440376282,
        "Negative": 9.653854067437351e-05,
        "Neutral": 0.002169104292988777,
        "Mixed": 3.857641786453314e-05
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的情绪。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DetectSentiment。

以下代码示例演示了如何使用 detect-syntax。

AWS CLI

检测输入文本中的语音部分

以下 detect-syntax 示例分析输入文本的语法并返回语音的不同部分。预训练模型的置信度分数也是每个预测的输出。


aws comprehend detect-syntax \
    --language-code en \
    --text "It is a beautiful day in Seattle."

输出：


{
    "SyntaxTokens": [
        {
            "TokenId": 1,
            "Text": "It",
            "BeginOffset": 0,
            "EndOffset": 2,
            "PartOfSpeech": {
                "Tag": "PRON",
                "Score": 0.9999740719795227
            }
        },
        {
            "TokenId": 2,
            "Text": "is",
            "BeginOffset": 3,
            "EndOffset": 5,
            "PartOfSpeech": {
                "Tag": "VERB",
                "Score": 0.999901294708252
            }
        },
        {
            "TokenId": 3,
            "Text": "a",
            "BeginOffset": 6,
            "EndOffset": 7,
            "PartOfSpeech": {
                "Tag": "DET",
                "Score": 0.9999938607215881
            }
        },
        {
            "TokenId": 4,
            "Text": "beautiful",
            "BeginOffset": 8,
            "EndOffset": 17,
            "PartOfSpeech": {
                "Tag": "ADJ",
                "Score": 0.9987351894378662
            }
        },
        {
            "TokenId": 5,
            "Text": "day",
            "BeginOffset": 18,
            "EndOffset": 21,
            "PartOfSpeech": {
                "Tag": "NOUN",
                "Score": 0.9999796748161316
            }
        },
        {
            "TokenId": 6,
            "Text": "in",
            "BeginOffset": 22,
            "EndOffset": 24,
            "PartOfSpeech": {
                "Tag": "ADP",
                "Score": 0.9998047947883606
            }
        },
        {
            "TokenId": 7,
            "Text": "Seattle",
            "BeginOffset": 25,
            "EndOffset": 32,
            "PartOfSpeech": {
                "Tag": "PROPN",
                "Score": 0.9940530061721802
            }
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的语法分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DetectSyntax。

以下代码示例演示了如何使用 detect-targeted-sentiment。

AWS CLI

检测输入文本中命名实体的目标情绪

以下 detect-targeted-sentiment 示例分析输入文本，并返回命名实体以及与每个实体关联的目标情绪。也会输出每个预测的预训练模型的置信度分数。


aws comprehend detect-targeted-sentiment \
    --language-code en \
    --text "I do not enjoy January because it is too cold but August is the perfect temperature"

输出：


{
    "Entities": [
        {
            "DescriptiveMentionIndex": [
                0
            ],
            "Mentions": [
                {
                    "Score": 0.9999979734420776,
                    "GroupScore": 1.0,
                    "Text": "I",
                    "Type": "PERSON",
                    "MentionSentiment": {
                        "Sentiment": "NEUTRAL",
                        "SentimentScore": {
                            "Positive": 0.0,
                            "Negative": 0.0,
                            "Neutral": 1.0,
                            "Mixed": 0.0
                        }
                    },
                    "BeginOffset": 0,
                    "EndOffset": 1
                }
            ]
        },
        {
            "DescriptiveMentionIndex": [
                0
            ],
            "Mentions": [
                {
                    "Score": 0.9638869762420654,
                    "GroupScore": 1.0,
                    "Text": "January",
                    "Type": "DATE",
                    "MentionSentiment": {
                        "Sentiment": "NEGATIVE",
                        "SentimentScore": {
                            "Positive": 0.0031610000878572464,
                            "Negative": 0.9967250227928162,
                            "Neutral": 0.00011100000119768083,
                            "Mixed": 1.9999999949504854e-06
                        }
                    },
                    "BeginOffset": 15,
                    "EndOffset": 22
                }
            ]
        },
        {
            "DescriptiveMentionIndex": [
                0
            ],
            "Mentions": [
                {
                {
                    "Score": 0.9664419889450073,
                    "GroupScore": 1.0,
                    "Text": "August",
                    "Type": "DATE",
                    "MentionSentiment": {
                        "Sentiment": "POSITIVE",
                        "SentimentScore": {
                            "Positive": 0.9999549984931946,
                            "Negative": 3.999999989900971e-06,
                            "Neutral": 4.099999932805076e-05,
                            "Mixed": 0.0
                        }
                    },
                    "BeginOffset": 50,
                    "EndOffset": 56
                }
            ]
        },
        {
            "DescriptiveMentionIndex": [
                0
            ],
            "Mentions": [
                {
                    "Score": 0.9803199768066406,
                    "GroupScore": 1.0,
                    "Text": "temperature",
                    "Type": "ATTRIBUTE",
                    "MentionSentiment": {
                        "Sentiment": "POSITIVE",
                        "SentimentScore": {
                            "Positive": 1.0,
                            "Negative": 0.0,
                            "Neutral": 0.0,
                            "Mixed": 0.0
                        }
                    },
                    "BeginOffset": 77,
                    "EndOffset": 88
                }
            ]
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的目标情绪。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 DetectTargetedSentiment。

以下代码示例演示了如何使用 import-model。

AWS CLI

导入模型

以下 import-model 示例从不同的 AWS 账户导入模型。账户 444455556666 中的文档分类器模型具有基于资源的策略，允许账户 111122223333 导入模型。


aws comprehend import-model \
    --source-model-arn arn:aws:comprehend:us-west-2:444455556666:document-classifier/example-classifier

输出：


{
    "ModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的在 AWS 账户之间复制自定义模型。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ImportModel。

以下代码示例演示了如何使用 list-datasets。

AWS CLI

列出所有飞轮数据集

以下 list-datasets 示例列出与飞轮关联的所有数据集。


aws comprehend list-datasets \
    --flywheel-arn arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity

输出：


{
    "DatasetPropertiesList": [
        {
            "DatasetArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity/dataset/example-dataset-1",
            "DatasetName": "example-dataset-1",
            "DatasetType": "TRAIN",
            "DatasetS3Uri": "s3://DOC-EXAMPLE-BUCKET/flywheel-entity/schemaVersion=1/20230616T200543Z/datasets/example-dataset-1/20230616T203710Z/",
            "Status": "CREATING",
            "CreationTime": "2023-06-16T20:37:10.400000+00:00"
        },
        {
            "DatasetArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity/dataset/example-dataset-2",
            "DatasetName": "example-dataset-2",
            "DatasetType": "TRAIN",
            "DatasetS3Uri": "s3://DOC-EXAMPLE-BUCKET/flywheel-entity/schemaVersion=1/20230616T200543Z/datasets/example-dataset-2/20230616T200607Z/",
            "Description": "TRAIN Dataset created by Flywheel creation.",
            "Status": "COMPLETED",
            "NumberOfDocuments": 5572,
            "CreationTime": "2023-06-16T20:06:07.722000+00:00"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的飞轮概述。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListDatasets。

以下代码示例演示了如何使用 list-document-classification-jobs。

AWS CLI

列出所有文档分类作业

以下 list-document-classification-jobs 示例列出所有文档分类作业。


aws comprehend list-document-classification-jobs

输出：


{
    "DocumentClassificationJobPropertiesList": [
        {
            "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classification-job/123456abcdeb0e11022f22a11EXAMPLE",
            "JobName": "exampleclassificationjob",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-14T17:09:51.788000+00:00",
            "EndTime": "2023-06-14T17:15:58.582000+00:00",
            "DocumentClassifierArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classifier/mymodel/version/12",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/jobdata/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/1234567890101-CLN-e758dd56b824aa717ceab551f11749fb/output/output.tar.gz"
            },
            "DataAccessRoleArn": "arn:aws:iam::1234567890101:role/service-role/AmazonComprehendServiceRole-example-role"
        },
        {
            "JobId": "123456abcdeb0e11022f22a1EXAMPLE2",
            "JobArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classification-job/123456abcdeb0e11022f22a1EXAMPLE2",
            "JobName": "exampleclassificationjob2",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-14T17:22:39.829000+00:00",
            "EndTime": "2023-06-14T17:28:46.107000+00:00",
            "DocumentClassifierArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classifier/mymodel/version/12",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/jobdata/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/1234567890101-CLN-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz"
            },
            "DataAccessRoleArn": "arn:aws:iam::1234567890101:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的自定义分类。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListDocumentClassificationJobs。

以下代码示例演示了如何使用 list-document-classifier-summaries。

AWS CLI

列出所有已创建文档分类器的摘要

以下 list-document-classifier-summaries 示例列出所有已创建文档分类器的摘要。


aws comprehend list-document-classifier-summaries

输出：


{
    "DocumentClassifierSummariesList": [
        {
            "DocumentClassifierName": "example-classifier-1",
            "NumberOfVersions": 1,
            "LatestVersionCreatedAt": "2023-06-13T22:07:59.825000+00:00",
            "LatestVersionName": "1",
            "LatestVersionStatus": "TRAINED"
        },
        {
            "DocumentClassifierName": "example-classifier-2",
            "NumberOfVersions": 2,
            "LatestVersionCreatedAt": "2023-06-13T21:54:59.589000+00:00",
            "LatestVersionName": "2",
            "LatestVersionStatus": "TRAINED"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的创建和管理自定义模型。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListDocumentClassifierSummaries。

以下代码示例演示了如何使用 list-document-classifiers。

AWS CLI

列出所有文档分类器

以下 list-document-classifiers 示例列出所有经过训练和正在训练的文档分类器模型。


aws comprehend list-document-classifiers

输出：


{
    "DocumentClassifierPropertiesList": [
        {
            "DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier1",
            "LanguageCode": "en",
            "Status": "TRAINED",
            "SubmitTime": "2023-06-13T19:04:15.735000+00:00",
            "EndTime": "2023-06-13T19:42:31.752000+00:00",
            "TrainingStartTime": "2023-06-13T19:08:20.114000+00:00",
            "TrainingEndTime": "2023-06-13T19:41:35.080000+00:00",
            "InputDataConfig": {
                "DataFormat": "COMPREHEND_CSV",
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata"
            },
            "OutputDataConfig": {},
            "ClassifierMetadata": {
                "NumberOfLabels": 3,
                "NumberOfTrainedDocuments": 5016,
                "NumberOfTestDocuments": 557,
                "EvaluationMetrics": {
                    "Accuracy": 0.9856,
                    "Precision": 0.9919,
                    "Recall": 0.9459,
                    "F1Score": 0.9673,
                    "MicroPrecision": 0.9856,
                    "MicroRecall": 0.9856,
                    "MicroF1Score": 0.9856,
                    "HammingLoss": 0.0144
                }
            },
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-testorle",
            "Mode": "MULTI_CLASS"
        },
        {
            "DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier2",
            "LanguageCode": "en",
            "Status": "TRAINING",
            "SubmitTime": "2023-06-13T21:20:28.690000+00:00",
            "InputDataConfig": {
                "DataFormat": "COMPREHEND_CSV",
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata"
            },
            "OutputDataConfig": {},
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-testorle",
            "Mode": "MULTI_CLASS"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的创建和管理自定义模型。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListDocumentClassifiers。

以下代码示例演示了如何使用 list-dominant-language-detection-jobs。

AWS CLI

列出所有主要语言检测作业

以下 list-dominant-language-detection-jobs 示例列出所有正在进行和已完成的异步主要语言检测作业。


aws comprehend list-dominant-language-detection-jobs

输出：


{
    "DominantLanguageDetectionJobPropertiesList": [
        {
            "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:dominant-language-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
            "JobName": "languageanalysis1",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-09T18:10:38.037000+00:00",
            "EndTime": "2023-06-09T18:18:45.498000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-LANGUAGE-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
            },
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        },
        {
            "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:dominant-language-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
            "JobName": "languageanalysis2",
            "JobStatus": "STOPPED",
            "SubmitTime": "2023-06-09T18:16:33.690000+00:00",
            "EndTime": "2023-06-09T18:24:40.608000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-LANGUAGE-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
            },
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListDominantLanguageDetectionJobs。

以下代码示例演示了如何使用 list-endpoints。

AWS CLI

列出所有端点

以下 list-endpoints 示例列出所有活动的指定模型的端点。


aws comprehend list-endpoints

输出：


{
    "EndpointPropertiesList": [
        {
            "EndpointArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/ExampleClassifierEndpoint",
            "Status": "IN_SERVICE",
            "ModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier1",
            "DesiredModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier1",
            "DesiredInferenceUnits": 1,
            "CurrentInferenceUnits": 1,
            "CreationTime": "2023-06-13T20:32:54.526000+00:00",
            "LastModifiedTime": "2023-06-13T20:32:54.526000+00:00"
        },
        {
            "EndpointArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/ExampleClassifierEndpoint2",
            "Status": "IN_SERVICE",
            "ModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier2",
            "DesiredModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier2",
            "DesiredInferenceUnits": 1,
            "CurrentInferenceUnits": 1,
            "CreationTime": "2023-06-13T20:32:54.526000+00:00",
            "LastModifiedTime": "2023-06-13T20:32:54.526000+00:00"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的管理 Amazon Comprehend 端点。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListEndpoints。

以下代码示例演示了如何使用 list-entities-detection-jobs。

AWS CLI

列出所有实体检测作业

以下 list-entities-detection-jobs 示例列出所有异步实体检测作业。


aws comprehend list-entities-detection-jobs

输出：


{
    "EntitiesDetectionJobPropertiesList": [
        {
            "JobId": "468af39c28ab45b83eb0c4ab9EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:entities-detection-job/468af39c28ab45b83eb0c4ab9EXAMPLE",
            "JobName": "example-entities-detection",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-08T20:57:46.476000+00:00",
            "EndTime": "2023-06-08T21:05:53.718000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-NER-468af39c28ab45b83eb0c4ab9EXAMPLE/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        },
        {
            "JobId": "809691caeaab0e71406f80a28EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:entities-detection-job/809691caeaab0e71406f80a28EXAMPLE",
            "JobName": "example-entities-detection-2",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-08T21:30:15.323000+00:00",
            "EndTime": "2023-06-08T21:40:23.509000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-NER-809691caeaab0e71406f80a28EXAMPLE/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        },
        {
            "JobId": "e00597c36b448b91d70dea165EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:entities-detection-job/e00597c36b448b91d70dea165EXAMPLE",
            "JobName": "example-entities-detection-3",
            "JobStatus": "STOPPED",
            "SubmitTime": "2023-06-08T22:19:28.528000+00:00",
            "EndTime": "2023-06-08T22:27:33.991000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-NER-e00597c36b448b91d70dea165EXAMPLE/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的实体。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListEntitiesDetectionJobs。

以下代码示例演示了如何使用 list-entity-recognizer-summaries。

AWS CLI

查看所有已创建实体识别器的摘要列表

以下 list-entity-recognizer-summaries 示例列出所有实体识别器摘要。


aws comprehend list-entity-recognizer-summaries

输出：


{
    "EntityRecognizerSummariesList": [
        {
            "RecognizerName": "entity-recognizer-3",
            "NumberOfVersions": 2,
            "LatestVersionCreatedAt": "2023-06-15T23:15:07.621000+00:00",
            "LatestVersionName": "2",
            "LatestVersionStatus": "STOP_REQUESTED"
        },
        {
            "RecognizerName": "entity-recognizer-2",
            "NumberOfVersions": 1,
            "LatestVersionCreatedAt": "2023-06-14T22:55:27.805000+00:00",
            "LatestVersionName": "2"
            "LatestVersionStatus": "TRAINED"
        },
        {
            "RecognizerName": "entity-recognizer-1",
            "NumberOfVersions": 1,
            "LatestVersionCreatedAt": "2023-06-14T20:44:59.631000+00:00",
            "LatestVersionName": "1",
            "LatestVersionStatus": "TRAINED"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的自定义实体识别。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListEntityRecognizerSummaries。

以下代码示例演示了如何使用 list-entity-recognizers。

AWS CLI

列出所有自定义实体识别器

以下 list-entity-recognizers 示例列出所有已创建自定义实体识别器。


aws comprehend list-entity-recognizers

输出：


{
    "EntityRecognizerPropertiesList": [
        {
            "EntityRecognizerArn": "arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/EntityRecognizer/version/1",
            "LanguageCode": "en",
            "Status": "TRAINED",
            "SubmitTime": "2023-06-14T20:44:59.631000+00:00",
            "EndTime": "2023-06-14T20:59:19.532000+00:00",
            "TrainingStartTime": "2023-06-14T20:48:52.811000+00:00",
            "TrainingEndTime": "2023-06-14T20:58:11.473000+00:00",
            "InputDataConfig": {
                "DataFormat": "COMPREHEND_CSV",
                "EntityTypes": [
                    {
                        "Type": "BUSINESS"
                    }
                ],
                "Documents": {
                    "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata/dataset/",
                    "InputFormat": "ONE_DOC_PER_LINE"
                },
                "EntityList": {
                    "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata/entity.csv"
                }
            },
            "RecognizerMetadata": {
                "NumberOfTrainedDocuments": 1814,
                "NumberOfTestDocuments": 486,
                "EvaluationMetrics": {
                    "Precision": 100.0,
                    "Recall": 100.0,
                    "F1Score": 100.0
                },
                "EntityTypes": [
                    {
                        "Type": "BUSINESS",
                        "EvaluationMetrics": {
                            "Precision": 100.0,
                            "Recall": 100.0,
                            "F1Score": 100.0
                        },
                        "NumberOfTrainMentions": 1520
                    }
                ]
            },
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole",
            "VersionName": "1"
        },
        {
            "EntityRecognizerArn": "arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/entityrecognizer3",
            "LanguageCode": "en",
            "Status": "TRAINED",
            "SubmitTime": "2023-06-14T22:57:51.056000+00:00",
            "EndTime": "2023-06-14T23:14:13.894000+00:00",
            "TrainingStartTime": "2023-06-14T23:01:33.984000+00:00",
            "TrainingEndTime": "2023-06-14T23:13:02.984000+00:00",
            "InputDataConfig": {
                "DataFormat": "COMPREHEND_CSV",
                "EntityTypes": [
                    {
                        "Type": "DEVICE"
                    }
                ],
                "Documents": {
                    "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata/raw_txt.csv",
                    "InputFormat": "ONE_DOC_PER_LINE"
                },
                "EntityList": {
                    "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata/entity_list.csv"
                }
            },
            "RecognizerMetadata": {
                "NumberOfTrainedDocuments": 4616,
                "NumberOfTestDocuments": 3489,
                "EvaluationMetrics": {
                    "Precision": 98.54227405247813,
                    "Recall": 100.0,
                    "F1Score": 99.26578560939794
                },
                "EntityTypes": [
                    {
                        "Type": "DEVICE",
                        "EvaluationMetrics": {
                            "Precision": 98.54227405247813,
                            "Recall": 100.0,
                            "F1Score": 99.26578560939794
                        },
                        "NumberOfTrainMentions": 2764
                    }
                ]
            },
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的自定义实体识别。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListEntityRecognizers。

以下代码示例演示了如何使用 list-events-detection-jobs。

AWS CLI

列出所有事件检测作业

以下 list-events-detection-jobs 示例列出所有异步事件检测作业。


aws comprehend list-events-detection-jobs

输出：


{
    "EventsDetectionJobPropertiesList": [
        {
            "JobId": "aa9593f9203e84f3ef032ce18EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:1111222233333:events-detection-job/aa9593f9203e84f3ef032ce18EXAMPLE",
            "JobName": "events_job_1",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-12T19:14:57.751000+00:00",
            "EndTime": "2023-06-12T19:21:04.962000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-SOURCE-BUCKET/EventsData/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/1111222233333-EVENTS-aa9593f9203e84f3ef032ce18EXAMPLE/output/"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::1111222233333:role/service-role/AmazonComprehendServiceRole-example-role",
            "TargetEventTypes": [
                "BANKRUPTCY",
                "EMPLOYMENT",
                "CORPORATE_ACQUISITION",
                "CORPORATE_MERGER",
                "INVESTMENT_GENERAL"
            ]
        },
        {
            "JobId": "4a990a2f7e82adfca6e171135EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:1111222233333:events-detection-job/4a990a2f7e82adfca6e171135EXAMPLE",
            "JobName": "events_job_2",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-12T19:55:43.702000+00:00",
            "EndTime": "2023-06-12T20:03:49.893000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-SOURCE-BUCKET/EventsData/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/1111222233333-EVENTS-4a990a2f7e82adfca6e171135EXAMPLE/output/"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::1111222233333:role/service-role/AmazonComprehendServiceRole-example-role",
            "TargetEventTypes": [
                "BANKRUPTCY",
                "EMPLOYMENT",
                "CORPORATE_ACQUISITION",
                "CORPORATE_MERGER",
                "INVESTMENT_GENERAL"
            ]
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListEventsDetectionJobs。

以下代码示例演示了如何使用 list-flywheel-iteration-history。

AWS CLI

列出所有飞轮迭代历史记录

以下 list-flywheel-iteration-history 示例列出飞轮的所有迭代。


aws comprehend list-flywheel-iteration-history
    --flywheel-arn arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel

输出：


{
    "FlywheelIterationPropertiesList": [
        {
            "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel",
            "FlywheelIterationId": "20230619TEXAMPLE",
            "CreationTime": "2023-06-19T04:00:32.594000+00:00",
            "EndTime": "2023-06-19T04:00:49.248000+00:00",
            "Status": "COMPLETED",
            "Message": "FULL_ITERATION: Flywheel iteration performed all functions successfully.",
            "EvaluatedModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1",
            "EvaluatedModelMetrics": {
                "AverageF1Score": 0.7742663922375772,
                "AverageF1Score": 0.9876464664646313,
                "AveragePrecision": 0.9800000253081214,
                "AverageRecall": 0.9445600253081214,
                "AverageAccuracy": 0.9997281665190434
            },
            "EvaluationManifestS3Prefix": "s3://DOC-EXAMPLE-BUCKET/example-flywheel/schemaVersion=1/20230619TEXAMPLE/evaluation/20230619TEXAMPLE/"
        },
        {
            "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel-2",
            "FlywheelIterationId": "20230616TEXAMPLE",
            "CreationTime": "2023-06-16T21:10:26.385000+00:00",
            "EndTime": "2023-06-16T23:33:16.827000+00:00",
            "Status": "COMPLETED",
            "Message": "FULL_ITERATION: Flywheel iteration performed all functions successfully.",
            "EvaluatedModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/spamvshamclassify/version/1",
            "EvaluatedModelMetrics": {
                "AverageF1Score": 0.7742663922375772,
                "AverageF1Score": 0.9767700253081214,
                "AveragePrecision": 0.9767700253081214,
                "AverageRecall": 0.9767700253081214,
                "AverageAccuracy": 0.9858281665190434
            },
            "EvaluationManifestS3Prefix": "s3://DOC-EXAMPLE-BUCKET/example-flywheel-2/schemaVersion=1/20230616TEXAMPLE/evaluation/20230616TEXAMPLE/"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的飞轮概述。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListFlywheelIterationHistory。

以下代码示例演示了如何使用 list-flywheels。

AWS CLI

列出所有飞轮

以下 list-flywheels 示例列出所有已创建的飞轮。


aws comprehend list-flywheels

输出：


{
    "FlywheelSummaryList": [
        {
            "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel-1",
            "ActiveModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier/version/1",
            "DataLakeS3Uri": "s3://DOC-EXAMPLE-BUCKET/example-flywheel-1/schemaVersion=1/20230616T200543Z/",
            "Status": "ACTIVE",
            "ModelType": "DOCUMENT_CLASSIFIER",
            "CreationTime": "2023-06-16T20:05:43.242000+00:00",
            "LastModifiedTime": "2023-06-19T04:00:43.027000+00:00",
            "LatestFlywheelIteration": "20230619T040032Z"
        },
        {
            "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel-2",
            "ActiveModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier2/version/1",
            "DataLakeS3Uri": "s3://DOC-EXAMPLE-BUCKET/example-flywheel-2/schemaVersion=1/20220616T200543Z/",
            "Status": "ACTIVE",
            "ModelType": "DOCUMENT_CLASSIFIER",
            "CreationTime": "2022-06-16T20:05:43.242000+00:00",
            "LastModifiedTime": "2022-06-19T04:00:43.027000+00:00",
            "LatestFlywheelIteration": "20220619T040032Z"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的飞轮概述。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListFlywheels。

以下代码示例演示了如何使用 list-key-phrases-detection-jobs。

AWS CLI

列出所有关键短语检测作业

以下 list-key-phrases-detection-jobs 示例列出所有正在进行和已完成的异步关键短语检测作业。


aws comprehend list-key-phrases-detection-jobs

输出：


{
    "KeyPhrasesDetectionJobPropertiesList": [
        {
            "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
            "JobName": "keyphrasesanalysis1",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-08T22:31:43.767000+00:00",
            "EndTime": "2023-06-08T22:39:52.565000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-SOURCE-BUCKET/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-KP-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        },
        {
            "JobId": "123456abcdeb0e11022f22a33EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/123456abcdeb0e11022f22a33EXAMPLE",
            "JobName": "keyphrasesanalysis2",
            "JobStatus": "STOPPED",
            "SubmitTime": "2023-06-08T22:57:52.154000+00:00",
            "EndTime": "2023-06-08T23:05:48.385000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-KP-123456abcdeb0e11022f22a33EXAMPLE/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        },
        {
            "JobId": "123456abcdeb0e11022f22a44EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/123456abcdeb0e11022f22a44EXAMPLE",
            "JobName": "keyphrasesanalysis3",
            "JobStatus": "FAILED",
            "Message": "NO_READ_ACCESS_TO_INPUT: The provided data access role does not have proper access to the input data.",
            "SubmitTime": "2023-06-09T16:47:04.029000+00:00",
            "EndTime": "2023-06-09T16:47:18.413000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-KP-123456abcdeb0e11022f22a44EXAMPLE/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListKeyPhrasesDetectionJobs。

以下代码示例演示了如何使用 list-pii-entities-detection-jobs。

AWS CLI

列出所有 PII 实体检测作业

以下 list-pii-entities-detection-jobs 示例列出所有正在进行和已完成的异步 PII 检测作业。


aws comprehend list-pii-entities-detection-jobs

输出：


{
    "PiiEntitiesDetectionJobPropertiesList": [
        {
            "JobId": "6f9db0c42d0c810e814670ee4EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/6f9db0c42d0c810e814670ee4EXAMPLE",
            "JobName": "example-pii-detection-job",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-09T21:02:46.241000+00:00",
            "EndTime": "2023-06-09T21:12:52.602000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-SOURCE-BUCKET/111122223333-PII-6f9db0c42d0c810e814670ee4EXAMPLE/output/"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role",
            "Mode": "ONLY_OFFSETS"
        },
        {
            "JobId": "d927562638cfa739331a99b3cEXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/d927562638cfa739331a99b3cEXAMPLE",
            "JobName": "example-pii-detection-job-2",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-09T21:20:58.211000+00:00",
            "EndTime": "2023-06-09T21:31:06.027000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-PII-d927562638cfa739331a99b3cEXAMPLE/output/"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role",
            "Mode": "ONLY_OFFSETS"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListPiiEntitiesDetectionJobs。

以下代码示例演示了如何使用 list-sentiment-detection-jobs。

AWS CLI

列出所有情绪检测作业

以下 list-sentiment-detection-jobs 示例列出所有正在进行和已完成的异步情绪检测作业。


aws comprehend list-sentiment-detection-jobs

输出：


{
    "SentimentDetectionJobPropertiesList": [
        {
            "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:sentiment-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
            "JobName": "example-sentiment-detection-job",
            "JobStatus": "IN_PROGRESS",
            "SubmitTime": "2023-06-09T22:42:20.545000+00:00",
            "EndTime": "2023-06-09T22:52:27.416000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/MovieData",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        },
        {
            "JobId": "123456abcdeb0e11022f22a1EXAMPLE2",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:sentiment-detection-job/123456abcdeb0e11022f22a1EXAMPLE2",
            "JobName": "example-sentiment-detection-job-2",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-09T23:16:15.956000+00:00",
            "EndTime": "2023-06-09T23:26:00.168000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/MovieData2",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TS-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListSentimentDetectionJobs。

以下代码示例演示了如何使用 list-tags-for-resource。

AWS CLI

列出资源标签

以下 list-tags-for-resource 示例列出 Amazon Comprehend 资源的标签。


aws comprehend list-tags-for-resource \
    --resource-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1

输出：


{
    "ResourceArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1",
    "Tags": [
        {
            "Key": "Department",
            "Value": "Finance"
        },
        {
            "Key": "location",
            "Value": "Seattle"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的标记资源。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListTagsForResource。

以下代码示例演示了如何使用 list-targeted-sentiment-detection-jobs。

AWS CLI

列出所有目标情绪检测作业

以下 list-targeted-sentiment-detection-jobs 示例列出所有正在进行和已完成的异步目标情绪检测作业。


aws comprehend list-targeted-sentiment-detection-jobs

输出：


{
    "TargetedSentimentDetectionJobPropertiesList": [
        {
            "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:targeted-sentiment-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
            "JobName": "example-targeted-sentiment-detection-job",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-09T22:42:20.545000+00:00",
            "EndTime": "2023-06-09T22:52:27.416000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/MovieData",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-IOrole"
        },
        {
            "JobId": "123456abcdeb0e11022f22a1EXAMPLE2",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:targeted-sentiment-detection-job/123456abcdeb0e11022f22a1EXAMPLE2",
            "JobName": "example-targeted-sentiment-detection-job-2",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-09T23:16:15.956000+00:00",
            "EndTime": "2023-06-09T23:26:00.168000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET/MovieData2",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TS-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListTargetedSentimentDetectionJobs。

以下代码示例演示了如何使用 list-topics-detection-jobs。

AWS CLI

列出所有主题检测作业

以下 list-topics-detection-jobs 示例列出所有正在进行和已完成的异步主题检测作业。


aws comprehend list-topics-detection-jobs

输出：


{
    "TopicsDetectionJobPropertiesList": [
        {
            "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
            "JobName" "topic-analysis-1"
            "JobStatus": "IN_PROGRESS",
            "SubmitTime": "2023-06-09T18:40:35.384000+00:00",
            "EndTime": "2023-06-09T18:46:41.936000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
            },
            "NumberOfTopics": 10,
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        },
        {
            "JobId": "123456abcdeb0e11022f22a1EXAMPLE2",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a1EXAMPLE2",
            "JobName": "topic-analysis-2",
            "JobStatus": "COMPLETED",
            "SubmitTime": "2023-06-09T18:44:43.414000+00:00",
            "EndTime": "2023-06-09T18:50:50.872000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz"
            },
            "NumberOfTopics": 10,
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        },
        {
            "JobId": "123456abcdeb0e11022f22a1EXAMPLE3",
            "JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a1EXAMPLE3",
            "JobName": "topic-analysis-2",
            "JobStatus": "IN_PROGRESS",
            "SubmitTime": "2023-06-09T18:50:56.737000+00:00",
            "InputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-BUCKET",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a1EXAMPLE3/output/output.tar.gz"
            },
            "NumberOfTopics": 10,
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 ListTopicsDetectionJobs。

以下代码示例演示了如何使用 put-resource-policy。

AWS CLI

附加基于资源的策略

以下 put-resource-policy 示例将基于资源的策略附加到模型，以便其他 AWS 账户导入。该策略附加到账户 111122223333 中的模型，并允许账户 444455556666 导入模型。


aws comprehend put-resource-policy \
    --resource-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1 \
    --resource-policy '{"Version":"2012-10-17","Statement":[{"Effect":"Allow","Action":"comprehend:ImportModel","Resource":"*","Principal":{"AWS":["arn:aws:iam::444455556666:root"]}}]}'

输出：


{
    "PolicyRevisionId": "aaa111d069d07afaa2aa3106aEXAMPLE"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的在 AWS 账户之间复制自定义模型。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 PutResourcePolicy。

以下代码示例演示了如何使用 start-document-classification-job。

AWS CLI

列出文档分类作业

以下 start-document-classification-job 示例以自定义模型启动文档分类作业，该作业对 --input-data-config 标签所指定地址处的所有文件都使用自定义模型。在此示例中，输入 S3 存储桶包含 SampleSMStext1.txt、SampleSMStext2.txt、和 SampleSMStext3.txt。该模型之前曾接受过关于垃圾邮件和非垃圾邮件，或“ham”、短信的文档分类训练。作业完成后，output.tar.gz 将放置在 --output-data-config 标签指定的位置。output.tar.gz 包含 predictions.jsonl，其中列出了每个文档的分类。每个文件的 Json 输出打印在一行上，但是为了便于阅读，此处设置了格式。


aws comprehend start-document-classification-job \
    --job-name exampleclassificationjob \
    --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET-INPUT/jobdata/" \
    --output-data-config "S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/" \
    --data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
    --document-classifier-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/mymodel/version/12

SampleSMStext1.txt 的内容：


"CONGRATULATIONS! TXT 2155550100 to win $5000"

SampleSMStext2.txt 的内容：


"Hi, when do you want me to pick you up from practice?"

SampleSMStext3.txt 的内容：


"Plz send bank account # to 2155550100 to claim prize!!"

输出：


{
    "JobId": "e758dd56b824aa717ceab551fEXAMPLE",
    "JobArn": "arn:aws:comprehend:us-west-2:111122223333:document-classification-job/e758dd56b824aa717ceab551fEXAMPLE",
    "JobStatus": "SUBMITTED"
}

predictions.jsonl 的内容：


{"File": "SampleSMSText1.txt", "Line": "0", "Classes": [{"Name": "spam", "Score": 0.9999}, {"Name": "ham", "Score": 0.0001}]}
{"File": "SampleSMStext2.txt", "Line": "0", "Classes": [{"Name": "ham", "Score": 0.9994}, {"Name": "spam", "Score": 0.0006}]}
{"File": "SampleSMSText3.txt", "Line": "0", "Classes": [{"Name": "spam", "Score": 0.9999}, {"Name": "ham", "Score": 0.0001}]}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的自定义分类。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StartDocumentClassificationJob。

以下代码示例演示了如何使用 start-dominant-language-detection-job。

AWS CLI

启动异步语言检测作业

以下 start-dominant-language-detection-job 示例为位于 --input-data-config 标签指定地址的所有文件启动异步语言检测作业。此示例中的 S3 存储桶包含 Sampletext1.txt。作业完成后，文件夹 output 将放置在 --output-data-config 标签指定的位置。该文件夹包含 output.txt，其中包含每个文本文件的主要语言以及每个预测的预训练模型的置信度分数。


aws comprehend start-dominant-language-detection-job \
    --job-name example_language_analysis_job \
    --language-code en \
    --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET/" \
    --output-data-config "S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/" \
    --data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
    --language-code en

Sampletext1.txt 的内容：


"Physics is the natural science that involves the study of matter and its motion and behavior through space and time, along with related concepts such as energy and force."

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
    "JobArn": "arn:aws:comprehend:us-west-2:111122223333:dominant-language-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
    "JobStatus": "SUBMITTED"
}

output.txt 的内容：


{"File": "Sampletext1.txt", "Languages": [{"LanguageCode": "en", "Score": 0.9913753867149353}], "Line": 0}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StartDominantLanguageDetectionJob。

以下代码示例演示了如何使用 start-entities-detection-job。

AWS CLI

示例 1：使用预训练模型启动标准实体检测作业

以下 start-entities-detection-job 示例为位于 --input-data-config 标签指定地址的所有文件启动异步实体检测作业。此示例中的 S3 存储桶包含 Sampletext1.txt、Sampletext2.txt 和 Sampletext3.txt。作业完成后，文件夹 output 将放置在 --output-data-config 标签指定的位置。该文件夹包含 output.txt，其中列出了在每个文本文件中检测到的所有命名实体，以及预训练模型对每个预测的置信度分数。每个输入文件的 Json 输出打印在一行上，但是为了便于阅读，此处设置了格式。


aws comprehend start-entities-detection-job \
    --job-name entitiestest \
    --language-code en \
    --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET/" \
    --output-data-config "S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/" \
    --data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
    --language-code en

Sampletext1.txt 的内容：


"Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st."

Sampletext2.txt 的内容：


"Dear Max, based on your autopay settings for your account example1.org account, we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. "

Sampletext3.txt 的内容：


"Jane, please submit any customer feedback from this weekend to AnySpa, 123 Main St, Anywhere and send comments to Alice at AnySpa@example.com."

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
    "JobArn": "arn:aws:comprehend:us-west-2:111122223333:entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
    "JobStatus": "SUBMITTED"
}

output.txt 的内容，为便于阅读，采用了行间缩进：


{
"Entities": [
    {
    "BeginOffset": 6,
    "EndOffset": 15,
    "Score": 0.9994006636420306,
    "Text": "Zhang Wei",
    "Type": "PERSON"
    },
    {
    "BeginOffset": 22,
    "EndOffset": 26,
    "Score": 0.9976647915128143,
    "Text": "John",
    "Type": "PERSON"
    },
    {
    "BeginOffset": 33,
    "EndOffset": 67,
    "Score": 0.9984608700836206,
    "Text": "AnyCompany Financial Services, LLC",
    "Type": "ORGANIZATION"
    },
    {
    "BeginOffset": 88,
    "EndOffset": 107,
    "Score": 0.9868521019555556,
    "Text": "1111-XXXX-1111-XXXX",
    "Type": "OTHER"
    },
    {
    "BeginOffset": 133,
    "EndOffset": 139,
    "Score": 0.998242565709204,
    "Text": "$24.53",
    "Type": "QUANTITY"
    },
    {
    "BeginOffset": 155,
    "EndOffset": 164,
    "Score": 0.9993039263159287,
    "Text": "July 31st",
    "Type": "DATE"
    }
],
"File": "SampleText1.txt",
"Line": 0
}
{
"Entities": [
    {
    "BeginOffset": 5,
    "EndOffset": 8,
    "Score": 0.9866232147545232,
    "Text": "Max",
    "Type": "PERSON"
    },
    {
    "BeginOffset": 156,
    "EndOffset": 166,
    "Score": 0.9797723450933329,
    "Text": "XXXXXX1111",
    "Type": "OTHER"
    },
    {
    "BeginOffset": 191,
    "EndOffset": 200,
    "Score": 0.9247838572396843,
    "Text": "XXXXX0000",
    "Type": "OTHER"
    }
],
"File": "SampleText2.txt",
"Line": 0
}
{
 "Entities": [
    {
    "Score": 0.9990532994270325,
    "Type": "PERSON",
    "Text": "Jane",
    "BeginOffset": 0,
    "EndOffset": 4
    },
    {
    "Score": 0.9519651532173157,
    "Type": "DATE",
    "Text": "this weekend",
    "BeginOffset": 47,
    "EndOffset": 59
    },
    {
    "Score": 0.5566426515579224,
    "Type": "ORGANIZATION",
    "Text": "AnySpa",
    "BeginOffset": 63,
    "EndOffset": 69
    },
    {
    "Score": 0.8059805631637573,
    "Type": "LOCATION",
    "Text": "123 Main St, Anywhere",
    "BeginOffset": 71,
    "EndOffset": 92
    },
    {
    "Score": 0.998830258846283,
    "Type": "PERSON",
    "Text": "Alice",
    "BeginOffset": 114,
    "EndOffset": 119
    },
    {
    "Score": 0.997818112373352,
    "Type": "OTHER",
    "Text": "AnySpa@example.com",
    "BeginOffset": 123,
    "EndOffset": 138
    }
    ],
    "File": "SampleText3.txt",
    "Line": 0
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

示例 2：启动自定义实体检测作业

以下 start-entities-detection-job 示例为位于 --input-data-config 标签指定地址的所有文件启动异步自定义实体检测作业。在此示例中，S3 存储桶包含 SampleFeedback1.txt、SampleFeedback2.txt 和 SampleFeedback3.txt。实体识别器模型经过客户支持反馈的训练，可以识别设备名称。作业完成后，文件夹 output 将放置在 --output-data-config 标签指定的位置。该文件夹包含 output.txt，其中列出了在每个文本文件中检测到的所有命名实体，以及预训练模型对每个预测的置信度分数。每个文件的 Json 输出打印在一行上，但是为了便于阅读，此处设置了格式。


aws comprehend start-entities-detection-job \
    --job-name customentitiestest \
    --entity-recognizer-arn "arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/entityrecognizer" \
    --language-code en \
    --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET/jobdata/" \
    --output-data-config "S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/" \
    --data-access-role-arn "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-IOrole"

SampleFeedback1.txt 的内容：


"I've been on the AnyPhone app have had issues for 24 hours when trying to pay bill. Cannot make payment. Sigh. | Oh man! Lets get that app up and running. DM me, and we can get to work!"

SampleFeedback2.txt 的内容：


"Hi, I have a discrepancy with my new bill. Could we get it sorted out? A rep added stuff I didnt sign up for when I did my AnyPhone 10 upgrade. | We can absolutely get this sorted!"

SampleFeedback3.txt 的内容：


"Is the by 1 get 1 free AnySmartPhone promo still going on? | Hi Christian! It ended yesterday, send us a DM if you have any questions and we can take a look at your options!"

输出：


{
    "JobId": "019ea9edac758806850fa8a79ff83021",
    "JobArn": "arn:aws:comprehend:us-west-2:111122223333:entities-detection-job/019ea9edac758806850fa8a79ff83021",
    "JobStatus": "SUBMITTED"
}

output.txt 的内容，为便于阅读，采用了行间缩进：


{
"Entities": [
    {
    "BeginOffset": 17,
    "EndOffset": 25,
    "Score": 0.9999728210205924,
    "Text": "AnyPhone",
    "Type": "DEVICE"
    }
],
"File": "SampleFeedback1.txt",
"Line": 0
}
{
"Entities": [
    {
    "BeginOffset": 123,
    "EndOffset": 133,
    "Score": 0.9999892116761524,
    "Text": "AnyPhone 10",
    "Type": "DEVICE"
    }
],
"File": "SampleFeedback2.txt",
"Line": 0
}
{
"Entities": [
    {
    "BeginOffset": 23,
    "EndOffset": 35,
    "Score": 0.9999971389852362,
    "Text": "AnySmartPhone",
    "Type": "DEVICE"
    }
],
"File": "SampleFeedback3.txt",
"Line": 0
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的自定义实体识别。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StartEntitiesDetectionJob。

以下代码示例演示了如何使用 start-events-detection-job。

AWS CLI

启动异步事件检测作业

以下 start-events-detection-job 示例为位于 --input-data-config 标签指定地址的所有文件启动异步事件检测作业。可能的目标事件类型包括 BANKRUPCTY、EMPLOYMENT、CORPORATE_ACQUISITION、INVESTMENT_GENERAL、CORPORATE_MERGER、IPO、RIGHTS_ISSUE、SECONDARY_OFFERING、SHELF_OFFERING、TENDER_OFFERING 和 STOCK_SPLIT。此示例中的 S3 存储桶包含 SampleText1.txt、SampleText2.txt 和 SampleText3.txt。作业完成后，文件夹 output 将放置在 --output-data-config 标签指定的位置。该文件夹包含 SampleText1.txt.out、SampleText2.txt.out 和 SampleText3.txt.out。每个文件的 JSON 输出打印在一行上，但是为了便于阅读，此处设置了格式。


aws comprehend start-events-detection-job \
    --job-name events-detection-1 \
    --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET/EventsData" \
    --output-data-config "S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/" \
    --data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole \
    --language-code en \
    --target-event-types "BANKRUPTCY" "EMPLOYMENT" "CORPORATE_ACQUISITION" "CORPORATE_MERGER" "INVESTMENT_GENERAL"

SampleText1.txt 的内容：


"Company AnyCompany grew by increasing sales and through acquisitions. After purchasing competing firms in 2020, AnyBusiness, a part of the AnyBusinessGroup, gave Jane Does firm a going rate of one cent a gallon or forty-two cents a barrel."

SampleText2.txt 的内容：


"In 2021, AnyCompany officially purchased AnyBusiness for 100 billion dollars, surprising and exciting the shareholders."

SampleText3.txt 的内容：


"In 2022, AnyCompany stock crashed 50. Eventually later that year they filed for bankruptcy."

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
    "JobArn": "arn:aws:comprehend:us-west-2:111122223333:events-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
    "JobStatus": "SUBMITTED"
}

SampleText1.txt.out 的内容，为便于阅读，采用了行间缩进：


{
    "Entities": [
        {
        "Mentions": [
            {
            "BeginOffset": 8,
            "EndOffset": 18,
            "Score": 0.99977,
            "Text": "AnyCompany",
            "Type": "ORGANIZATION",
            "GroupScore": 1
            },
            {
            "BeginOffset": 112,
            "EndOffset": 123,
            "Score": 0.999747,
            "Text": "AnyBusiness",
            "Type": "ORGANIZATION",
            "GroupScore": 0.979826
            },
            {
            "BeginOffset": 171,
            "EndOffset": 175,
            "Score": 0.999615,
            "Text": "firm",
            "Type": "ORGANIZATION",
            "GroupScore": 0.871647
            }
        ]
        },
        {
        "Mentions": [
            {
            "BeginOffset": 97,
            "EndOffset": 102,
            "Score": 0.987687,
            "Text": "firms",
            "Type": "ORGANIZATION",
            "GroupScore": 1
            }
        ]
        },
        {
        "Mentions": [
            {
            "BeginOffset": 103,
            "EndOffset": 110,
            "Score": 0.999458,
            "Text": "in 2020",
            "Type": "DATE",
            "GroupScore": 1
            }
        ]
        },
        {
        "Mentions": [
            {
            "BeginOffset": 160,
            "EndOffset": 168,
            "Score": 0.999649,
            "Text": "John Doe",
            "Type": "PERSON",
            "GroupScore": 1
            }
        ]
        }
    ],
    "Events": [
        {
        "Type": "CORPORATE_ACQUISITION",
        "Arguments": [
            {
            "EntityIndex": 0,
            "Role": "INVESTOR",
            "Score": 0.99977
            }
        ],
        "Triggers": [
            {
            "BeginOffset": 56,
            "EndOffset": 68,
            "Score": 0.999967,
            "Text": "acquisitions",
            "Type": "CORPORATE_ACQUISITION",
            "GroupScore": 1
            }
        ]
        },
        {
        "Type": "CORPORATE_ACQUISITION",
        "Arguments": [
            {
            "EntityIndex": 1,
            "Role": "INVESTEE",
            "Score": 0.987687
            },
            {
            "EntityIndex": 2,
            "Role": "DATE",
            "Score": 0.999458
            },
            {
            "EntityIndex": 3,
            "Role": "INVESTOR",
            "Score": 0.999649
            }
        ],
        "Triggers": [
            {
            "BeginOffset": 76,
            "EndOffset": 86,
            "Score": 0.999973,
            "Text": "purchasing",
            "Type": "CORPORATE_ACQUISITION",
            "GroupScore": 1
            }
        ]
        }
    ],
    "File": "SampleText1.txt",
    "Line": 0
}

SampleText2.txt.out 的内容：


{
    "Entities": [
        {
        "Mentions": [
            {
            "BeginOffset": 0,
            "EndOffset": 7,
            "Score": 0.999473,
            "Text": "In 2021",
            "Type": "DATE",
            "GroupScore": 1
            }
        ]
        },
        {
        "Mentions": [
            {
            "BeginOffset": 9,
            "EndOffset": 19,
            "Score": 0.999636,
            "Text": "AnyCompany",
            "Type": "ORGANIZATION",
            "GroupScore": 1
            }
        ]
        },
        {
        "Mentions": [
            {
            "BeginOffset": 45,
            "EndOffset": 56,
            "Score": 0.999712,
            "Text": "AnyBusiness",
            "Type": "ORGANIZATION",
            "GroupScore": 1
            }
        ]
        },
        {
        "Mentions": [
            {
            "BeginOffset": 61,
            "EndOffset": 80,
            "Score": 0.998886,
            "Text": "100 billion dollars",
            "Type": "MONETARY_VALUE",
            "GroupScore": 1
            }
        ]
        }
    ],
    "Events": [
        {
        "Type": "CORPORATE_ACQUISITION",
        "Arguments": [
            {
            "EntityIndex": 3,
            "Role": "AMOUNT",
            "Score": 0.998886
            },
            {
            "EntityIndex": 2,
            "Role": "INVESTEE",
            "Score": 0.999712
            },
            {
            "EntityIndex": 0,
            "Role": "DATE",
            "Score": 0.999473
            },
            {
            "EntityIndex": 1,
            "Role": "INVESTOR",
            "Score": 0.999636
            }
        ],
        "Triggers": [
            {
            "BeginOffset": 31,
            "EndOffset": 40,
            "Score": 0.99995,
            "Text": "purchased",
            "Type": "CORPORATE_ACQUISITION",
            "GroupScore": 1
            }
        ]
        }
    ],
    "File": "SampleText2.txt",
    "Line": 0
}

SampleText3.txt.out 的内容：


{
    "Entities": [
        {
        "Mentions": [
            {
            "BeginOffset": 9,
            "EndOffset": 19,
            "Score": 0.999774,
            "Text": "AnyCompany",
            "Type": "ORGANIZATION",
            "GroupScore": 1
            },
            {
            "BeginOffset": 66,
            "EndOffset": 70,
            "Score": 0.995717,
            "Text": "they",
            "Type": "ORGANIZATION",
            "GroupScore": 0.997626
            }
        ]
        },
        {
        "Mentions": [
            {
            "BeginOffset": 50,
            "EndOffset": 65,
            "Score": 0.999656,
            "Text": "later that year",
            "Type": "DATE",
            "GroupScore": 1
            }
        ]
        }
    ],
    "Events": [
        {
        "Type": "BANKRUPTCY",
        "Arguments": [
            {
            "EntityIndex": 1,
            "Role": "DATE",
            "Score": 0.999656
            },
            {
            "EntityIndex": 0,
            "Role": "FILER",
            "Score": 0.995717
            }
        ],
        "Triggers": [
            {
            "BeginOffset": 81,
            "EndOffset": 91,
            "Score": 0.999936,
            "Text": "bankruptcy",
            "Type": "BANKRUPTCY",
            "GroupScore": 1
            }
        ]
        }
    ],
    "File": "SampleText3.txt",
    "Line": 0
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StartEventsDetectionJob。

以下代码示例演示了如何使用 start-flywheel-iteration。

AWS CLI

启动飞轮迭代

以下 start-flywheel-iteration 示例启动飞轮迭代。此操作使用飞轮中的任何新数据集来训练新的模型版本。


aws comprehend start-flywheel-iteration \
    --flywheel-arn arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel

输出：


{
    "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel",
    "FlywheelIterationId": "12345123TEXAMPLE"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的飞轮概述。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StartFlywheelIteration。

以下代码示例演示了如何使用 start-key-phrases-detection-job。

AWS CLI

启动关键短语检测作业

以下 start-key-phrases-detection-job 示例为位于 --input-data-config 标签指定地址的所有文件启动异步关键短语检测作业。此示例中的 S3 存储桶包含 Sampletext1.txt、Sampletext2.txt 和 Sampletext3.txt。作业完成后，文件夹 output 将放置在 --output-data-config 标签指定的位置。该文件夹包含 output.txt，其中包含了在每个文本文件中检测到的所有关键短语，以及预训练模型对每个预测的置信度分数。每个文件的 Json 输出打印在一行上，但是为了便于阅读，此处设置了格式。


aws comprehend start-key-phrases-detection-job \
    --job-name keyphrasesanalysistest1 \
    --language-code en \
    --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET/" \
    --output-data-config "S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/" \
    --data-access-role-arn "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" \
    --language-code en

Sampletext1.txt 的内容：


"Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st."

Sampletext2.txt 的内容：


"Dear Max, based on your autopay settings for your account Internet.org account, we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. "

Sampletext3.txt 的内容：


"Jane, please submit any customer feedback from this weekend to Sunshine Spa, 123 Main St, Anywhere and send comments to Alice at AnySpa@example.com."

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
    "JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
    "JobStatus": "SUBMITTED"
}

output.txt 的内容，为便于阅读，采用了行间缩进：


{
    "File": "SampleText1.txt",
    "KeyPhrases": [
        {
        "BeginOffset": 6,
        "EndOffset": 15,
        "Score": 0.9748965572679326,
        "Text": "Zhang Wei"
        },
        {
        "BeginOffset": 22,
        "EndOffset": 26,
        "Score": 0.9997344722354619,
        "Text": "John"
        },
        {
        "BeginOffset": 28,
        "EndOffset": 62,
        "Score": 0.9843791074032948,
        "Text": "Your AnyCompany Financial Services"
        },
        {
        "BeginOffset": 64,
        "EndOffset": 107,
        "Score": 0.8976122401721824,
        "Text": "LLC credit card account 1111-XXXX-1111-XXXX"
        },
        {
        "BeginOffset": 112,
        "EndOffset": 129,
        "Score": 0.9999612982629748,
        "Text": "a minimum payment"
        },
        {
        "BeginOffset": 133,
        "EndOffset": 139,
        "Score": 0.99975728947036,
        "Text": "$24.53"
        },
        {
        "BeginOffset": 155,
        "EndOffset": 164,
        "Score": 0.9940866241449973,
        "Text": "July 31st"
        }
    ],
    "Line": 0
    }
    {
    "File": "SampleText2.txt",
    "KeyPhrases": [
        {
        "BeginOffset": 0,
        "EndOffset": 8,
        "Score": 0.9974021100118472,
        "Text": "Dear Max"
        },
        {
        "BeginOffset": 19,
        "EndOffset": 40,
        "Score": 0.9961120519515884,
        "Text": "your autopay settings"
        },
        {
        "BeginOffset": 45,
        "EndOffset": 78,
        "Score": 0.9980620070116009,
        "Text": "your account Internet.org account"
        },
        {
        "BeginOffset": 97,
        "EndOffset": 109,
        "Score": 0.999919660140754,
        "Text": "your payment"
        },
        {
        "BeginOffset": 113,
        "EndOffset": 125,
        "Score": 0.9998370719754205,
        "Text": "the due date"
        },
        {
        "BeginOffset": 131,
        "EndOffset": 166,
        "Score": 0.9955068678502509,
        "Text": "your bank account number XXXXXX1111"
        },
        {
        "BeginOffset": 172,
        "EndOffset": 200,
        "Score": 0.8653433315829526,
        "Text": "the routing number XXXXX0000"
        }
    ],
    "Line": 0
    }
    {
    "File": "SampleText3.txt",
    "KeyPhrases": [
        {
        "BeginOffset": 0,
        "EndOffset": 4,
        "Score": 0.9142947833681668,
        "Text": "Jane"
        },
        {
        "BeginOffset": 20,
        "EndOffset": 41,
        "Score": 0.9984325676596763,
        "Text": "any customer feedback"
        },
        {
        "BeginOffset": 47,
        "EndOffset": 59,
        "Score": 0.9998782448150636,
        "Text": "this weekend"
        },
        {
        "BeginOffset": 63,
        "EndOffset": 75,
        "Score": 0.99866741830757,
        "Text": "Sunshine Spa"
        },
        {
        "BeginOffset": 77,
        "EndOffset": 88,
        "Score": 0.9695803485466054,
        "Text": "123 Main St"
        },
        {
        "BeginOffset": 108,
        "EndOffset": 116,
        "Score": 0.9997065928550928,
        "Text": "comments"
        },
        {
        "BeginOffset": 120,
        "EndOffset": 125,
        "Score": 0.9993466833825161,
        "Text": "Alice"
        },
        {
        "BeginOffset": 129,
        "EndOffset": 144,
        "Score": 0.9654563612885667,
        "Text": "AnySpa@example.com"
        }
    ],
    "Line": 0
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StartKeyPhrasesDetectionJob。

以下代码示例演示了如何使用 start-pii-entities-detection-job。

AWS CLI

启动异步 PII 检测作业

以下 start-pii-entities-detection-job 示例为位于 --input-data-config 标签指定地址的所有文件启动异步个人身份信息（PII）实体检测作业。此示例中的 S3 存储桶包含 Sampletext1.txt、Sampletext2.txt 和 Sampletext3.txt。作业完成后，文件夹 output 将放置在 --output-data-config 标签指定的位置。该文件夹包含 SampleText1.txt.out、SampleText2.txt.out 和 SampleText3.txt.out，列出了每个文本文件中的命名实体。每个文件的 Json 输出打印在一行上，但是为了便于阅读，此处设置了格式。


aws comprehend start-pii-entities-detection-job \
    --job-name entities_test \
    --language-code en \
    --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET/" \
    --output-data-config "S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/" \
    --data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
    --language-code en \
    --mode ONLY_OFFSETS

Sampletext1.txt 的内容：


"Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st."

Sampletext2.txt 的内容：


"Dear Max, based on your autopay settings for your account Internet.org account, we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. "

Sampletext3.txt 的内容：


"Jane, please submit any customer feedback from this weekend to Sunshine Spa, 123 Main St, Anywhere and send comments to Alice at AnySpa@example.com."

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
    "JobArn": "arn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
    "JobStatus": "SUBMITTED"
}

SampleText1.txt.out 的内容，为便于阅读，采用了行间缩进：


{
    "Entities": [
        {
        "BeginOffset": 6,
        "EndOffset": 15,
        "Type": "NAME",
        "Score": 0.9998490510222595
        },
        {
        "BeginOffset": 22,
        "EndOffset": 26,
        "Type": "NAME",
        "Score": 0.9998937958019426
        },
        {
        "BeginOffset": 88,
        "EndOffset": 107,
        "Type": "CREDIT_DEBIT_NUMBER",
        "Score": 0.9554297245278491
        },
        {
        "BeginOffset": 155,
        "EndOffset": 164,
        "Type": "DATE_TIME",
        "Score": 0.9999720462925257
        }
    ],
    "File": "SampleText1.txt",
    "Line": 0
}

SampleText2.txt.out 的内容，为便于阅读，采用了行间缩进：


{
    "Entities": [
        {
        "BeginOffset": 5,
        "EndOffset": 8,
        "Type": "NAME",
        "Score": 0.9994390774924007
        },
        {
        "BeginOffset": 58,
        "EndOffset": 70,
        "Type": "URL",
        "Score": 0.9999958276922101
        },
        {
        "BeginOffset": 156,
        "EndOffset": 166,
        "Type": "BANK_ACCOUNT_NUMBER",
        "Score": 0.9999721058045592
        },
        {
        "BeginOffset": 191,
        "EndOffset": 200,
        "Type": "BANK_ROUTING",
        "Score": 0.9998968945989909
        }
    ],
    "File": "SampleText2.txt",
    "Line": 0
}

SampleText3.txt.out 的内容，为便于阅读，采用了行间缩进：


{
    "Entities": [
        {
        "BeginOffset": 0,
        "EndOffset": 4,
        "Type": "NAME",
        "Score": 0.999949934606805
        },
        {
        "BeginOffset": 77,
        "EndOffset": 88,
        "Type": "ADDRESS",
        "Score": 0.9999035300466904
        },
        {
        "BeginOffset": 120,
        "EndOffset": 125,
        "Type": "NAME",
        "Score": 0.9998203838716296
        },
        {
        "BeginOffset": 129,
        "EndOffset": 144,
        "Type": "EMAIL",
        "Score": 0.9998313473105228
        }
    ],
    "File": "SampleText3.txt",
    "Line": 0
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StartPiiEntitiesDetectionJob。

以下代码示例演示了如何使用 start-sentiment-detection-job。

AWS CLI

启动异步情绪分析作业

以下 start-sentiment-detection-job 示例为位于 --input-data-config 标签指定地址的所有文件启动异步情绪分析检测作业。此示例中的 S3 存储桶文件夹包含 SampleMovieReview1.txt、SampleMovieReview2.txt 和 SampleMovieReview3.txt。作业完成后，文件夹 output 将放置在 --output-data-config 标签指定的位置。该文件夹包含 output.txt，其中包含了每个文本文件中的主导情绪，以及预训练模型对每个预测的置信度分数。每个文件的 Json 输出打印在一行上，但是为了便于阅读，此处设置了格式。


aws comprehend start-sentiment-detection-job \
    --job-name example-sentiment-detection-job \
    --language-code en \
    --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET/MovieData" \
    --output-data-config "S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/" \
    --data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role

SampleMovieReview1.txt 的内容：


"The film, AnyMovie2, is fairly predictable and just okay."

SampleMovieReview2.txt 的内容：


"AnyMovie2 is the essential sci-fi film that I grew up watching when I was a kid. I highly recommend this movie."

SampleMovieReview3.txt 的内容：


"Don't get fooled by the 'awards' for AnyMovie2. All parts of the film were poorly stolen from other modern directors."

输出：


{
    "JobId": "0b5001e25f62ebb40631a9a1a7fde7b3",
    "JobArn": "arn:aws:comprehend:us-west-2:111122223333:sentiment-detection-job/0b5001e25f62ebb40631a9a1a7fde7b3",
    "JobStatus": "SUBMITTED"
}

output.txt 的内容，为便于阅读，采用了行间缩进：


{
    "File": "SampleMovieReview1.txt",
        "Line": 0,
        "Sentiment": "MIXED",
        "SentimentScore": {
            "Mixed": 0.6591159105300903,
            "Negative": 0.26492202281951904,
            "Neutral": 0.035430654883384705,
            "Positive": 0.04053137078881264
            }
        }
    {
    "File": "SampleMovieReview2.txt",
        "Line": 0,
        "Sentiment": "POSITIVE",
        "SentimentScore": {
            "Mixed": 0.000008718466233403888,
            "Negative": 0.00006134175055194646,
            "Neutral": 0.0002941041602753103,
            "Positive": 0.9996358156204224
            }
        }
    {
    "File": "SampleMovieReview3.txt",
        "Line": 0,
        "Sentiment": "NEGATIVE",
        "SentimentScore": {
            "Mixed": 0.004146667663007975,
            "Negative": 0.9645107984542847,
            "Neutral": 0.016559595242142677,
            "Positive": 0.014782938174903393
        }
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StartSentimentDetectionJob。

以下代码示例演示了如何使用 start-targeted-sentiment-detection-job。

AWS CLI

启动异步目标情绪分析作业

以下 start-targeted-sentiment-detection-job 示例为位于 --input-data-config 标签指定地址的所有文件启动异步目标情绪分析检测作业。此示例中的 S3 存储桶文件夹包含 SampleMovieReview1.txt、SampleMovieReview2.txt 和 SampleMovieReview3.txt。作业完成后，output.tar.gz 将放置在 --output-data-config 标签指定的位置。output.tar.gz 包含文件 SampleMovieReview1.txt.out、SampleMovieReview2.txt.out 和 SampleMovieReview3.txt.out，每个文件都包含单个输入文本文件的所有命名实体和关联情绪。


aws comprehend start-targeted-sentiment-detection-job \
    --job-name targeted_movie_review_analysis1 \
    --language-code en \
    --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET/MovieData" \
    --output-data-config "S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/" \
    --data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role

SampleMovieReview1.txt 的内容：


"The film, AnyMovie, is fairly predictable and just okay."

SampleMovieReview2.txt 的内容：


"AnyMovie is the essential sci-fi film that I grew up watching when I was a kid. I highly recommend this movie."

SampleMovieReview3.txt 的内容：


"Don't get fooled by the 'awards' for AnyMovie. All parts of the film were poorly stolen from other modern directors."

输出：


{
    "JobId": "0b5001e25f62ebb40631a9a1a7fde7b3",
    "JobArn": "arn:aws:comprehend:us-west-2:111122223333:targeted-sentiment-detection-job/0b5001e25f62ebb40631a9a1a7fde7b3",
    "JobStatus": "SUBMITTED"
}

SampleMovieReview1.txt.out 的内容，为便于阅读，采用了行间缩进：


{
    "Entities": [
        {
        "DescriptiveMentionIndex": [
            0
        ],
        "Mentions": [
            {
            "BeginOffset": 4,
            "EndOffset": 8,
            "Score": 0.994972,
            "GroupScore": 1,
            "Text": "film",
            "Type": "MOVIE",
            "MentionSentiment": {
                "Sentiment": "NEUTRAL",
                "SentimentScore": {
                "Mixed": 0,
                "Negative": 0,
                "Neutral": 1,
                "Positive": 0
                }
            }
            }
        ]
        },
        {
        "DescriptiveMentionIndex": [
            0
        ],
        "Mentions": [
            {
            "BeginOffset": 10,
            "EndOffset": 18,
            "Score": 0.631368,
            "GroupScore": 1,
            "Text": "AnyMovie",
            "Type": "ORGANIZATION",
            "MentionSentiment": {
                "Sentiment": "POSITIVE",
                "SentimentScore": {
                "Mixed": 0.001729,
                "Negative": 0.000001,
                "Neutral": 0.000318,
                "Positive": 0.997952
                }
            }
            }
        ]
        }
    ],
    "File": "SampleMovieReview1.txt",
    "Line": 0
}

SampleMovieReview2.txt.out 的内容，为便于阅读，采用了行间缩进：


{
    "Entities": [
        {
        "DescriptiveMentionIndex": [
            0
        ],
        "Mentions": [
            {
            "BeginOffset": 0,
            "EndOffset": 8,
            "Score": 0.854024,
            "GroupScore": 1,
            "Text": "AnyMovie",
            "Type": "MOVIE",
            "MentionSentiment": {
                "Sentiment": "POSITIVE",
                "SentimentScore": {
                "Mixed": 0,
                "Negative": 0,
                "Neutral": 0.000007,
                "Positive": 0.999993
                }
            }
            },
            {
            "BeginOffset": 104,
            "EndOffset": 109,
            "Score": 0.999129,
            "GroupScore": 0.502937,
            "Text": "movie",
            "Type": "MOVIE",
            "MentionSentiment": {
                "Sentiment": "POSITIVE",
                "SentimentScore": {
                "Mixed": 0,
                "Negative": 0,
                "Neutral": 0,
                "Positive": 1
                }
            }
            },
            {
            "BeginOffset": 33,
            "EndOffset": 37,
            "Score": 0.999823,
            "GroupScore": 0.999252,
            "Text": "film",
            "Type": "MOVIE",
            "MentionSentiment": {
                "Sentiment": "POSITIVE",
                "SentimentScore": {
                "Mixed": 0,
                "Negative": 0,
                "Neutral": 0.000001,
                "Positive": 0.999999
                }
            }
            }
        ]
        },
        {
        "DescriptiveMentionIndex": [
            0,
            1,
            2
        ],
        "Mentions": [
            {
            "BeginOffset": 43,
            "EndOffset": 44,
            "Score": 0.999997,
            "GroupScore": 1,
            "Text": "I",
            "Type": "PERSON",
            "MentionSentiment": {
                "Sentiment": "NEUTRAL",
                "SentimentScore": {
                "Mixed": 0,
                "Negative": 0,
                "Neutral": 1,
                "Positive": 0
                }
            }
            },
            {
            "BeginOffset": 80,
            "EndOffset": 81,
            "Score": 0.999996,
            "GroupScore": 0.52523,
            "Text": "I",
            "Type": "PERSON",
            "MentionSentiment": {
                "Sentiment": "NEUTRAL",
                "SentimentScore": {
                "Mixed": 0,
                "Negative": 0,
                "Neutral": 1,
                "Positive": 0
                }
            }
            },
            {
            "BeginOffset": 67,
            "EndOffset": 68,
            "Score": 0.999994,
            "GroupScore": 0.999499,
            "Text": "I",
            "Type": "PERSON",
            "MentionSentiment": {
                "Sentiment": "NEUTRAL",
                "SentimentScore": {
                "Mixed": 0,
                "Negative": 0,
                "Neutral": 1,
                "Positive": 0
                }
            }
            }
        ]
        },
        {
        "DescriptiveMentionIndex": [
            0
        ],
        "Mentions": [
            {
            "BeginOffset": 75,
            "EndOffset": 78,
            "Score": 0.999978,
            "GroupScore": 1,
            "Text": "kid",
            "Type": "PERSON",
            "MentionSentiment": {
                "Sentiment": "NEUTRAL",
                "SentimentScore": {
                "Mixed": 0,
                "Negative": 0,
                "Neutral": 1,
                "Positive": 0
                }
            }
            }
        ]
        }
    ],
    "File": "SampleMovieReview2.txt",
    "Line": 0
}

SampleMovieReview3.txt.out 的内容，为便于阅读，采用了行间缩进：


{
    "Entities": [
        {
        "DescriptiveMentionIndex": [
            1
        ],
        "Mentions": [
            {
            "BeginOffset": 64,
            "EndOffset": 68,
            "Score": 0.992953,
            "GroupScore": 0.999814,
            "Text": "film",
            "Type": "MOVIE",
            "MentionSentiment": {
                "Sentiment": "NEUTRAL",
                "SentimentScore": {
                "Mixed": 0.000004,
                "Negative": 0.010425,
                "Neutral": 0.989543,
                "Positive": 0.000027
                }
            }
            },
            {
            "BeginOffset": 37,
            "EndOffset": 45,
            "Score": 0.999782,
            "GroupScore": 1,
            "Text": "AnyMovie",
            "Type": "ORGANIZATION",
            "MentionSentiment": {
                "Sentiment": "POSITIVE",
                "SentimentScore": {
                "Mixed": 0.000095,
                "Negative": 0.039847,
                "Neutral": 0.000673,
                "Positive": 0.959384
                }
            }
            }
        ]
        },
        {
        "DescriptiveMentionIndex": [
            0
        ],
        "Mentions": [
            {
            "BeginOffset": 47,
            "EndOffset": 50,
            "Score": 0.999991,
            "GroupScore": 1,
            "Text": "All",
            "Type": "QUANTITY",
            "MentionSentiment": {
                "Sentiment": "NEUTRAL",
                "SentimentScore": {
                "Mixed": 0.000001,
                "Negative": 0.000001,
                "Neutral": 0.999998,
                "Positive": 0
                }
            }
            }
        ]
        },
        {
        "DescriptiveMentionIndex": [
            0
        ],
        "Mentions": [
            {
            "BeginOffset": 106,
            "EndOffset": 115,
            "Score": 0.542083,
            "GroupScore": 1,
            "Text": "directors",
            "Type": "PERSON",
            "MentionSentiment": {
                "Sentiment": "NEUTRAL",
                "SentimentScore": {
                "Mixed": 0,
                "Negative": 0,
                "Neutral": 1,
                "Positive": 0
                }
            }
            }
        ]
        }
    ],
    "File": "SampleMovieReview3.txt",
    "Line": 0
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StartTargetedSentimentDetectionJob。

以下代码示例演示了如何使用 start-topics-detection-job。

AWS CLI

启动主题检测分析作业

以下 start-topics-detection-job 示例为位于 --input-data-config 标签指定地址的所有文件启动异步主题检测作业。作业完成后，文件夹 output 将放置在 --ouput-data-config 标签指定的位置。output 包含 topic-terms.csv 和 doc-topics.csv。第一个输出文件 topic-terms.csv 是集合中的主题列表。对于每个主题，默认情况下，该列表按权重排列主题列出根据其的热门术语。第二个文件 doc-topics.csv 列出了与主题相关的文档以及与该主题相关的文档比例。


aws comprehend start-topics-detection-job \
    --job-name example_topics_detection_job \
    --language-code en \
    --input-data-config "S3Uri=s3://DOC-EXAMPLE-BUCKET/" \
    --output-data-config "S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/" \
    --data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
    --language-code en

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE",
    "JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/123456abcdeb0e11022f22a11EXAMPLE",
    "JobStatus": "SUBMITTED"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的主题建模。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StartTopicsDetectionJob。

以下代码示例演示了如何使用 stop-dominant-language-detection-job。

AWS CLI

停止异步主要语言检测作业

以下 stop-dominant-language-detection-job 示例停止正在进行的异步主要语言检测作业。如果当前作业状态为 IN_PROGRESS，则该作业被标记为终止并进入 STOP_REQUESTED 状态。如果作业在可以停止之前就完成了，则会进入 COMPLETED 状态。


aws comprehend stop-dominant-language-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE,
    "JobStatus": "STOP_REQUESTED"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StopDominantLanguageDetectionJob。

以下代码示例演示了如何使用 stop-entities-detection-job。

AWS CLI

停止异步实体检测作业

以下 stop-entities-detection-job 示例停止正在进行的异步实体检测作业。如果当前作业状态为 IN_PROGRESS，则该作业被标记为终止并进入 STOP_REQUESTED 状态。如果作业在可以停止之前就完成了，则会进入 COMPLETED 状态。


aws comprehend stop-entities-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE,
    "JobStatus": "STOP_REQUESTED"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StopEntitiesDetectionJob。

以下代码示例演示了如何使用 stop-events-detection-job。

AWS CLI

停止异步事件检测作业

以下 stop-events-detection-job 示例停止正在进行的异步事件检测作业。如果当前作业状态为 IN_PROGRESS，则该作业被标记为终止并进入 STOP_REQUESTED 状态。如果作业在可以停止之前就完成了，则会进入 COMPLETED 状态。


aws comprehend stop-events-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE,
    "JobStatus": "STOP_REQUESTED"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StopEventsDetectionJob。

以下代码示例演示了如何使用 stop-key-phrases-detection-job。

AWS CLI

停止异步关键短语检测作业

以下 stop-key-phrases-detection-job 示例停止正在进行的异步关键短语检测作业。如果当前作业状态为 IN_PROGRESS，则该作业被标记为终止并进入 STOP_REQUESTED 状态。如果作业在可以停止之前就完成了，则会进入 COMPLETED 状态。


aws comprehend stop-key-phrases-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE,
    "JobStatus": "STOP_REQUESTED"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StopKeyPhrasesDetectionJob。

以下代码示例演示了如何使用 stop-pii-entities-detection-job。

AWS CLI

停止异步 PII 实体检测作业

以下 stop-pii-entities-detection-job 示例停止正在进行的异步 PII 实体检测作业。如果当前作业状态为 IN_PROGRESS，则该作业被标记为终止并进入 STOP_REQUESTED 状态。如果作业在可以停止之前就完成了，则会进入 COMPLETED 状态。


aws comprehend stop-pii-entities-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE,
    "JobStatus": "STOP_REQUESTED"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StopPiiEntitiesDetectionJob。

以下代码示例演示了如何使用 stop-sentiment-detection-job。

AWS CLI

停止异步情绪检测作业

以下 stop-sentiment-detection-job 示例停止正在进行的异步情绪检测作业。如果当前作业状态为 IN_PROGRESS，则该作业被标记为终止并进入 STOP_REQUESTED 状态。如果作业在可以停止之前就完成了，则会进入 COMPLETED 状态。


aws comprehend stop-sentiment-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE,
    "JobStatus": "STOP_REQUESTED"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StopSentimentDetectionJob。

以下代码示例演示了如何使用 stop-targeted-sentiment-detection-job。

AWS CLI

停止异步目标情绪检测作业

以下 stop-targeted-sentiment-detection-job 示例停止正在进行的异步目标情绪检测作业。如果当前作业状态为 IN_PROGRESS，则该作业被标记为终止并进入 STOP_REQUESTED 状态。如果作业在可以停止之前就完成了，则会进入 COMPLETED 状态。


aws comprehend stop-targeted-sentiment-detection-job \
    --job-id 123456abcdeb0e11022f22a11EXAMPLE

输出：


{
    "JobId": "123456abcdeb0e11022f22a11EXAMPLE,
    "JobStatus": "STOP_REQUESTED"
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的 Amazon Comprehend 洞察的异步分析。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StopTargetedSentimentDetectionJob。

以下代码示例演示了如何使用 stop-training-document-classifier。

AWS CLI

停止训练文档分类器模型

以下 stop-training-document-classifier 示例停止训练正在进行的文档分类器模型。


aws comprehend stop-training-document-classifier
    --document-classifier-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier

此命令不生成任何输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的创建和管理自定义模型。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StopTrainingDocumentClassifier。

以下代码示例演示了如何使用 stop-training-entity-recognizer。

AWS CLI

停止训练实体识别器模型

以下 stop-training-entity-recognizer 示例停止训练正在进行的实体识别器模型。


aws comprehend stop-training-entity-recognizer
    --entity-recognizer-arn "arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/examplerecognizer1"

此命令不生成任何输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的创建和管理自定义模型。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 StopTrainingEntityRecognizer。

以下代码示例演示了如何使用 tag-resource。

AWS CLI

示例 1：标记资源

以下 tag-resource 示例为 Amazon Comprehend 资源添加一个标签。


aws comprehend tag-resource \
    --resource-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1 \
    --tags Key=Location,Value=Seattle

此命令没有输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的标记资源。

示例 2：为资源添加多个标记

以下 tag-resource 示例为 Amazon Comprehend 资源添加多个标签。


aws comprehend tag-resource \
    --resource-arn "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1" \
    --tags Key=location,Value=Seattle Key=Department,Value=Finance

此命令没有输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的标记资源。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 TagResource。

以下代码示例演示了如何使用 untag-resource。

AWS CLI

示例 1：从资源中移除单个标签

以下 untag-resource 示例从 Amazon Comprehend 资源中移除一个标签。


aws comprehend untag-resource \
    --resource-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1
    --tag-keys Location

此命令不生成任何输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的标记资源。

示例 2：从资源中删除多个标签

以下 untag-resource 示例从 Amazon Comprehend 资源中移除多个标签。


aws comprehend untag-resource \
    --resource-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1
    --tag-keys Location Department

此命令不生成任何输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的标记资源。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 UntagResource。

以下代码示例演示了如何使用 update-endpoint。

AWS CLI

示例 1：更新端点的推理单元

以下 update-endpoint 示例更新有关端点的信息。在此示例中，增加了推理单元的数量。


aws comprehend update-endpoint \
    --endpoint-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint
    --desired-inference-units 2

此命令不生成任何输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的管理 Amazon Comprehend 端点。

示例 2：更新端点的活动模型

以下 update-endpoint 示例更新有关端点的信息。在此示例中，更改了活动模型。


aws comprehend update-endpoint \
    --endpoint-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint
    --active-model-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-new

此命令不生成任何输出。

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的管理 Amazon Comprehend 端点。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 UpdateEndpoint。

以下代码示例演示了如何使用 update-flywheel。

AWS CLI

更新飞轮配置

以下 update-flywheel 示例更新飞轮配置。在此示例中，更新了飞轮的活动模型。


aws comprehend update-flywheel \
    --flywheel-arn arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel-1 \
    --active-model-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/new-example-classifier-model

输出：


{
    "FlywheelProperties": {
        "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity",
        "ActiveModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/new-example-classifier-model",
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role",
        "TaskConfig": {
            "LanguageCode": "en",
            "DocumentClassificationConfig": {
                "Mode": "MULTI_CLASS"
            }
        },
        "DataLakeS3Uri": "s3://DOC-EXAMPLE-BUCKET/flywheel-entity/schemaVersion=1/20230616T200543Z/",
        "DataSecurityConfig": {},
        "Status": "ACTIVE",
        "ModelType": "DOCUMENT_CLASSIFIER",
        "CreationTime": "2023-06-16T20:05:43.242000+00:00",
        "LastModifiedTime": "2023-06-19T04:00:43.027000+00:00",
        "LatestFlywheelIteration": "20230619T040032Z"
    }
}

有关更多信息，请参阅《Amazon Comprehend 开发人员指南》中的飞轮概述。

有关 API 详细信息，请参阅《AWS CLI 命令参考》中的 UpdateFlywheel。

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