使用 Amazon Comprehend 示例 AWS CLI

以下代码示例向您展示了如何使用 AWS Command Line Interface 与 Amazon Comprehend 配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，您可以在其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

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

AWS CLI

检测多个输入文本的主导语言

以下batch-detect-dominant-language示例分析了多个输入文本并返回每个文本的主导语言。还会为每个预测输出预训练模型的置信度分数。


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 “BatchDetectDominantLanguage AWS CLI命令参考”。

以下代码示例演示如何使用 batch-detect-entities。

AWS CLI

检测来自多个输入文本的实体

以下batch-detect-entities示例分析了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 “BatchDetectEntities AWS CLI命令参考”。

以下代码示例演示如何使用 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示例分析了多个输入文本，并返回每个文本的关键名词短语。还会输出预训练模型对每个预测的置信度分数。


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 “BatchDetectKeyPhrases AWS CLI命令参考”。

以下代码示例演示如何使用 batch-detect-sentiment。

AWS CLI

检测多个输入文本的流行情绪

以下batch-detect-sentiment示例分析了多个输入文本并返回主流情绪（POSITIVE每个文本的NEGATIVE、、或）。NEUTRAL MIXED


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 开发者指南中的情绪。

有关API详细信息，请参阅 “BatchDetectSentiment AWS CLI命令参考”。

以下代码示例演示如何使用 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示例分析了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 “BatchDetectSyntax AWS CLI命令参考”。

以下代码示例演示如何使用 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示例分析了多个输入文本，并返回命名实体以及附加到每个实体的流行情绪。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 开发者指南中的定向情绪。

有关API详细信息，请参阅 “BatchDetectTargetedSentiment AWS CLI命令参考”。

以下代码示例演示如何使用 classify-document。

AWS CLI

使用特定于模型的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。本示例中的模型是在一个数据集上训练的，该数据集包含标为垃圾邮件或非垃圾邮件或 “ham” 的短信。


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 “ClassifyDocument AWS CLI命令参考”。

以下代码示例演示如何使用 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 开发者指南中的个人身份信息 (PII)。

有关API详细信息，请参阅 “ContainsPiiEntities AWS CLI命令参考”。

以下代码示例演示如何使用 create-dataset。

AWS CLI

创建飞轮数据集

以下create-dataset示例为飞轮创建数据集。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 开发者指南中的飞轮概述。

有关API详细信息，请参阅 “CreateDataset AWS CLI命令参考”。

以下代码示例演示如何使用 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 “CreateDocumentClassifier AWS CLI命令参考”。

以下代码示例演示如何使用 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创建了一个用于同步推理的端点。


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 “CreateEndpoint AWS CLI命令参考”。

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

AWS CLI

创建自定义实体识别器

以下create-entity-recognizer示例开始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和CSV实体列表CSV的文件entity_list.csv来训练模型。raw_text.csv 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 开发者指南中的自定义实体识别。

有关API详细信息，请参阅 “CreateEntityRecognizer AWS CLI命令参考”。

以下代码示例演示如何使用 create-flywheel。

AWS CLI

创建飞轮

以下create-flywheel示例创建了一个飞轮来协调文档分类或实体识别模型的持续训练。本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 开发者指南中的飞轮概述。

有关API详细信息，请参阅 “CreateFlywheel AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DeleteDocumentClassifier AWS CLI命令参考”。

以下代码示例演示如何使用 delete-endpoint。

AWS CLI

删除自定义模型的终端节点

以下delete-endpoint示例删除了特定于模型的端点。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 “DeleteEndpoint AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DeleteEntityRecognizer AWS CLI命令参考”。

以下代码示例演示如何使用 delete-flywheel。

AWS CLI

删除飞轮

以下delete-flywheel示例删除了飞轮。数据湖或与飞轮关联的模型不会被删除。


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《亚马逊 Comprehend 开发者指南》中的 Flywhe el 概述。

有关API详细信息，请参阅 “DeleteFlywheel AWS CLI命令参考”。

以下代码示例演示如何使用 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 开发者指南中的在AWS 账户之间复制自定义模型。

有关API详细信息，请参阅 “DeleteResourcePolicy AWS CLI命令参考”。

以下代码示例演示如何使用 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 开发者指南中的飞轮概述。

有关API详细信息，请参阅 “DescribeDataset AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DescribeDocumentClassificationJob AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DescribeDocumentClassifier AWS CLI命令参考”。

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

AWS CLI

描述占主导地位的语言检测工作。

以下describe-dominant-language-detection-job示例获取异步主导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 “DescribeDominantLanguageDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 describe-endpoint。

AWS CLI

描述特定的终端节点

以下describe-endpoint示例获取特定于模型的端点的属性。


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 “DescribeEndpoint AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DescribeEntitiesDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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 开发者指南中的自定义实体识别。

有关API详细信息，请参阅 “DescribeEntityRecognizer AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DescribeEventsDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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《亚马逊 Comprehend 开发者指南》中的 Flywhe el 概述。

有关API详细信息，请参阅 “DescribeFlywheelIteration AWS CLI命令参考”。

以下代码示例演示如何使用 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或非垃圾邮件或 “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 开发者指南中的飞轮概述。

有关API详细信息，请参阅 “DescribeFlywheel AWS CLI命令参考”。

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

AWS CLI

描述一项关键短语检测工作

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 “DescribeKeyPhrasesDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DescribePiiEntitiesDetectionJob AWS CLI命令参考”。

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

AWS CLI

描述附加到模型的资源策略

以下describe-resource-policy示例获取了附加到模型的基于资源的策略的属性。


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 开发者指南中的在AWS 账户之间复制自定义模型。

有关API详细信息，请参阅 “DescribeResourcePolicy AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DescribeSentimentDetectionJob AWS CLI命令参考”。

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

AWS CLI

描述有针对性的情绪检测工作

以下describe-targeted-sentiment-detection-job示例获取异步定向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 “DescribeTargetedSentimentDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DescribeTopicsDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DetectDominantLanguage AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DetectEntities AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DetectKeyPhrases AWS CLI命令参考”。

以下代码示例演示如何使用 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的实体 (PII)。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 开发者指南中的个人身份信息 (PII)。

有关API详细信息，请参阅 “DetectPiiEntities AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DetectSentiment AWS CLI命令参考”。

以下代码示例演示如何使用 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 “DetectSyntax AWS CLI命令参考”。

以下代码示例演示如何使用 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。还会输出每个预测的预训练模型置信度分数。


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 开发者指南中的定向情绪。

有关API详细信息，请参阅 “DetectTargetedSentiment AWS CLI命令参考”。

以下代码示例演示如何使用 import-model。

AWS CLI

要导入模型

以下import-model示例从不同的 AWS 账户导入模型。账户中的文档分类器模型444455556666具有基于资源的策略，允许账户导111122223333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 开发者指南中的在AWS 账户之间复制自定义模型。

有关API详细信息，请参阅 “ImportModel AWS CLI命令参考”。

以下代码示例演示如何使用 list-datasets。

AWS CLI

列出所有飞轮数据集

以下list-datasets示例列出了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 开发者指南中的飞轮概述。

有关API详细信息，请参阅 “ListDatasets AWS CLI命令参考”。

以下代码示例演示如何使用 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 “ListDocumentClassificationJobs AWS CLI命令参考”。

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

AWS CLI

列出所有已创建的文档分类器的摘要

以下list-document-classifier-summaries示例列出了所有创建的文档分类器摘要。


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 “ListDocumentClassifierSummaries AWS CLI命令参考”。

以下代码示例演示如何使用 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 “ListDocumentClassifiers AWS CLI命令参考”。

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

AWS CLI

列出所有占主导地位的语言检测作业

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


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 “ListDominantLanguageDetectionJobs AWS CLI命令参考”。

以下代码示例演示如何使用 list-endpoints。

AWS CLI

列出所有端点

以下list-endpoints示例列出了所有特定于模型的活动端点。


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 “ListEndpoints AWS CLI命令参考”。

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

AWS CLI

列出所有实体检测任务

以下list-entities-detection-jobs示例列出了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 “ListEntitiesDetectionJobs AWS CLI命令参考”。

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

AWS CLI

查看所有已创建的实体识别器的摘要列表

以下list-entity-recognizer-summaries示例列出了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 开发者指南中的自定义实体识别。

有关API详细信息，请参阅 “ListEntityRecognizerSummaries AWS CLI命令参考”。

以下代码示例演示如何使用 list-entity-recognizers。

AWS CLI

列出所有自定义实体识别器

以下list-entity-recognizers示例列出了所有创建的自定义实体识别器。


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 开发者指南中的自定义实体识别。

有关API详细信息，请参阅 “ListEntityRecognizers AWS CLI命令参考”。

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

AWS CLI

列出所有事件检测作业

以下list-events-detection-jobs示例列出了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 “ListEventsDetectionJobs AWS CLI命令参考”。

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

AWS CLI

列出所有飞轮迭代历史记录

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


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《亚马逊 Comprehend 开发者指南》中的 Flywhe el 概述。

有关API详细信息，请参阅 “ListFlywheelIterationHistory AWS CLI命令参考”。

以下代码示例演示如何使用 list-flywheels。

AWS CLI

列出所有飞轮

以下list-flywheels示例列出了所有创建的飞轮。


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《亚马逊 Comprehend 开发者指南》中的 Flywhe el 概述。

有关API详细信息，请参阅 “ListFlywheels AWS CLI命令参考”。

以下代码示例演示如何使用 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示例列出了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 “ListKeyPhrasesDetectionJobs AWS CLI命令参考”。

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

AWS CLI

列出所有 pii 实体检测任务

以下list-pii-entities-detection-jobs示例列出了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 “ListPiiEntitiesDetectionJobs AWS CLI命令参考”。

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

AWS CLI

列出所有情绪检测作业

以下list-sentiment-detection-jobs示例列出了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 “ListSentimentDetectionJobs AWS CLI命令参考”。

以下代码示例演示如何使用 list-tags-for-resource。

AWS CLI

列出资源的标签

以下list-tags-for-resource示例列出了 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 开发者指南中的为资源添加标签。

有关API详细信息，请参阅 “ListTagsForResource AWS CLI命令参考”。

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

AWS CLI

列出所有有针对性的情绪检测作业

以下list-targeted-sentiment-detection-jobs示例列出了所有正在进行和已完成的异步定向情绪检测作业。


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 “ListTargetedSentimentDetectionJobs AWS CLI命令参考”。

以下代码示例演示如何使用 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 “ListTopicsDetectionJobs AWS CLI命令参考”。

以下代码示例演示如何使用 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 账户可以导入该模型。该策略附加到账户中的模型，111122223333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 开发者指南中的在AWS 账户之间复制自定义模型。

有关API详细信息，请参阅 “PutResourcePolicy AWS CLI命令参考”。

以下代码示例演示如何使用 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” SMS 邮件的文档分类的训练。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。Json 输出在每个文件的一行上打印，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 “StartDocumentClassificationJob AWS CLI命令参考”。

以下代码示例演示如何使用 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。本示例中的 S3 存储桶包含Sampletext1.txt。任务完成后output，文件夹将放置在--output-data-config标签指定的位置。output.txt该文件夹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 “StartDominantLanguageDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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。本示例中的 S3 存储桶包含Sampletext1.txtSampletext2.txt、和Sampletext3.txt。任务完成后output，文件夹将放置在--output-data-config标签指定的位置。output.txt该文件夹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得分。Json 输出在每个输入文件中打印一行，但为了便于阅读，在此处进行了格式化。


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"
}

为了便于阅读output.txt，带有行缩进的内容：


{
"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，此示例中的 S3 存储桶包含SampleFeedback1.txtSampleFeedback2.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。任务完成后，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得分。Json 输出在每个文件的一行上打印，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"
}

为了便于阅读output.txt，带有行缩进的内容：


{
"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 开发者指南中的自定义实体识别。

有关API详细信息，请参阅 “StartEntitiesDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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BANKRUPCTYEMPLOYMENT、CORPORATE_ACQUISITION、、INVESTMENT_GENERAL、CORPORATE_MERGER、IPO、RIGHTS_ISSUE、SECONDARY_OFFERING、SHELF_OFFERING、TENDER_OFFERING、和STOCK_SPLIT。本示例中的 S3 存储桶包含SampleText1.txtSampleText2.txt、和SampleText3.txt。任务完成后output，文件夹将放置在--output-data-config标签指定的位置。该文件夹包含SampleText1.txt.outSampleText2.txt.out、和SampleText3.txt.out。JSON输出按每个文件一行打印，但为了便于阅读，在此处进行了格式化。


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"
}

为了便于阅读SampleText1.txt.out，带有行缩进的内容：


{
    "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 “StartEventsDetectionJob AWS CLI命令参考”。

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

AWS CLI

开始飞轮迭代

以下start-flywheel-iteration示例开始飞轮迭代。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《亚马逊 Comprehend 开发者指南》中的 Flywhe el 概述。

有关API详细信息，请参阅 “StartFlywheelIteration AWS CLI命令参考”。

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

AWS CLI

开始关键短语检测作业

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。本示例中的 S3 存储桶包含Sampletext1.txtSampletext2.txt、和Sampletext3.txt。作业完成后output，文件夹将放置在--output-data-config标签指定的位置。该文件夹包含文件，output.txt其中包含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得分。Json 输出在每个文件的一行上打印，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"
}

为了便于output.txt阅读，带有行缩进的内容：


{
    "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 “StartKeyPhrasesDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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。本示例中的 S3 存储桶包含Sampletext1.txtSampletext2.txt、和Sampletext3.txt。任务完成后output，文件夹将放置在--output-data-config标签指定的位置。该文件夹包含SampleText1.txt.outSampleText2.txt.out、和，SampleText3.txt.out它们列出了每个文本文件中的命名实体。Json 输出在每个文件的一行上打印，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"
}

为了便于阅读SampleText1.txt.out，带有行缩进的内容：


{
    "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
}

为了便于阅读SampleText2.txt.out，带有行缩进的内容：


{
    "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
}

为了便于阅读SampleText3.txt.out，带有行缩进的内容：


{
    "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 “StartPiiEntitiesDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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。本示例中的 S3 存储桶文件夹包含SampleMovieReview1.txtSampleMovieReview2.txt、和SampleMovieReview3.txt。任务完成后output，文件夹将放置在--output-data-config标签指定的位置。该文件夹包含该文件output.txt，其中包含每个文本文件的主要情绪以及预训练模型对每个预测的置信度得分。Json 输出在每个文件的一行上打印，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"
}

为了便于阅读output.txt，带有缩进行的内容：


{
    "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 “StartSentimentDetectionJob AWS CLI命令参考”。

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

AWS CLI

启动异步定向情绪分析作业

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定向情绪分析检测作业。本示例中的 S3 存储桶文件夹包含SampleMovieReview1.txtSampleMovieReview2.txt、和SampleMovieReview3.txt。任务完成后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.outSampleMovieReview2.txt.outSampleMovieReview3.txt.out、和，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"
}

为了便于阅读SampleMovieReview1.txt.out，带有行缩进的内容：


{
    "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
}

为了便于阅读，SampleMovieReview2.txt.out行缩进的内容：


{
    "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
}

为了便于SampleMovieReview3.txt.out阅读，带有行缩进的内容：


{
    "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 “StartTargetedSentimentDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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 “StartTopicsDetectionJob AWS CLI命令参考”。

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

AWS CLI

停止异步主导语言检测作业

以下stop-dominant-language-detection-job示例停止正在进行的异步主导语言检测作业。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，则会将其置于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 “StopDominantLanguageDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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，则会将其置于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 “StopEntitiesDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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，则会将其置于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 “StopEventsDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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，则会将其置于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 “StopKeyPhrasesDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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，则会将其置于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 “StopPiiEntitiesDetectionJob AWS CLI命令参考”。

以下代码示例演示如何使用 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，则会将其置于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 “StopSentimentDetectionJob AWS CLI命令参考”。

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

AWS CLI

停止异步定向情绪检测作业

以下stop-targeted-sentiment-detection-job示例停止正在进行的异步定向情绪检测作业。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，则会将其置于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 “StopTargetedSentimentDetectionJob AWS CLI命令参考”。

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

AWS CLI

停止训练文档分类器模型

以下stop-training-document-classifier示例在文档分类器模型的训练过程中停止训练。


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 “StopTrainingDocumentClassifier AWS CLI命令参考”。

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

AWS CLI

停止训练实体识别器模型

以下stop-training-entity-recognizer示例在实体识别器模型的训练过程中停止训练。


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 “StopTrainingEntityRecognizer AWS CLI命令参考”。

以下代码示例演示如何使用 tag-resource。

AWS CLI

示例 1：为资源添加标签

以下tag-resource示例向 Amazon Comprehend 资源添加了一个标签。


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 开发者指南中的为资源添加标签。

示例 2：为资源添加多个标签

以下tag-resource示例向 Amazon Comprehend 资源添加了多个标签。


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 开发者指南中的为资源添加标签。

有关API详细信息，请参阅 “TagResource AWS CLI命令参考”。

以下代码示例演示如何使用 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 资源中删除单个标签。


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 开发者指南中的为资源添加标签。

示例 2：从资源中移除多个标签

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 开发者指南中的为资源添加标签。

有关API详细信息，请参阅 “UntagResource AWS CLI命令参考”。

以下代码示例演示如何使用 update-endpoint。

AWS CLI

示例 1：更新终端节点的推理单元

以下update-endpoint示例更新了有关终端节点的信息。在此示例中，推理单元的数量增加了。


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示例更新了有关终端节点的信息。在此示例中，活动模型已更改。


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 “UpdateEndpoint AWS CLI命令参考”。

以下代码示例演示如何使用 update-flywheel。

AWS CLI

更新飞轮配置

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


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《亚马逊 Comprehend 开发者指南》中的 Flywhe el 概述。

有关API详细信息，请参阅 “UpdateFlywheel AWS CLI命令参考”。

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