使用的 Amazon Comprehend 範例 AWS CLI

下列程式碼範例示範如何使用 AWS Command Line Interface 搭配 Amazon Comprehend 執行動作和實作常見案例。

Actions 是大型程式的程式碼摘錄，必須在內容中執行。雖然動作會告訴您如何呼叫個別服務函數，但您可以在其相關情境中查看內容中的動作。

每個範例都包含完整原始程式碼的連結，您可以在其中找到如何在內容中設定和執行程式碼的指示。

主題

動作

動作

以下程式碼範例顯示如何使用 batch-detect-dominant-language。

AWS CLI

偵測多個輸入文字的主要語言

下列batch-detect-dominant-language範例會分析多個輸入文字，並傳回每個輸入文字的主要語言。每個預測也會輸出預先訓練的模型可信度分數。


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."

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的慣用語言。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 BatchDetectDominantLanguage。

以下程式碼範例顯示如何使用 batch-detect-entities。

AWS CLI

從多個輸入文字偵測實體

下列batch-detect-entities範例會分析多個輸入文字，並傳回每個輸入文字的具名實體。每個預測也會輸出預先訓練模型的可信度分數。


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."

輸出：


{
    "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": []
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的實體。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 BatchDetectEntities。

以下程式碼範例顯示如何使用 batch-detect-key-phrases。

AWS CLI

偵測多個文字輸入的金鑰片語

下列batch-detect-key-phrases範例會分析多個輸入文字，並傳回每個輸入文字的索引鍵名詞片語。也會輸出每個預測的預先訓練模型可信度分數。


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."

輸出：


{
    "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": []
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的關鍵詞。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 BatchDetectKeyPhrases。

以下程式碼範例顯示如何使用 batch-detect-sentiment。

AWS CLI

偵測多個輸入文字的普遍情緒

下列batch-detect-sentiment範例會分析多個輸入文字，並傳回普遍的情緒 (POSITIVE、MIXED、 NEUTRAL或 NEGATIVE)。


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

輸出：


{
    "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": []
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的情緒。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 BatchDetectSentiment。

以下程式碼範例顯示如何使用 batch-detect-syntax。

AWS CLI

檢查多個輸入文字中單字的語法和語音部分

下列batch-detect-syntax範例會分析多個輸入文字的語法，並傳回語音的不同部分。每個預測也會輸出預先訓練模型的可信度分數。


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

輸出：


{
    "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": []
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的語法分析。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 BatchDetectSyntax。

以下程式碼範例顯示如何使用 batch-detect-targeted-sentiment。

AWS CLI

針對多個輸入文字偵測情緒和每個具名實體

下列batch-detect-targeted-sentiment範例會分析多個輸入文字，並傳回具名實體，以及連接至每個實體的現行情緒。每個預測也會輸出預先訓練模型的可信度分數。


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."

輸出：


{
    "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": []
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的目標情緒。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 BatchDetectTargetedSentiment。

以下程式碼範例顯示如何使用 classify-document。

AWS CLI

使用模型特定的端點對文件進行分類

下列classify-document範例會分類具有自訂模型端點的文件。此範例中的模型是在資料集上訓練，其中包含標示為垃圾郵件或非垃圾郵件的 sms 訊息，或 "ham"。


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"

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的自訂分類。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ClassifyDocument。

以下程式碼範例顯示如何使用 contains-pii-entities。

AWS CLI

分析 PII 資訊是否存在的輸入文字

下列contains-pii-entities範例會分析輸入文字是否存在個人身分識別資訊 (PII)，並傳回已識別 PII 實體類型的標籤，例如名稱、地址、銀行帳戶號碼或電話號碼。


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."

輸出：


{
    "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
        }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的個人身分識別資訊 (PII)。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ContainsPiiEntities。

以下程式碼範例顯示如何使用 create-dataset。

AWS CLI

建立飛輪資料集

下列create-dataset範例會建立飛輪的資料集。此資料集將用作--dataset-type標籤指定的其他訓練資料。


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 的內容：


{
    "DataFormat": "COMPREHEND_CSV",
    "DocumentClassifierInputDataConfig": {
        "S3Uri": "s3://amzn-s3-demo-bucket/training-data.csv"
    }
}

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的飛輪概觀。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 CreateDataset。

以下程式碼範例顯示如何使用 create-document-classifier。

AWS CLI

建立文件分類器以分類文件

下列create-document-classifier範例會開始文件分類器模型的訓練程序。訓練資料檔案 training.csv位於 --input-data-config標籤。 training.csv 是兩欄文件，其中標籤或分類在第一欄提供，文件在第二欄提供。


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://amzn-s3-demo-bucket/" \
    --language-code en

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的自訂分類。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 CreateDocumentClassifier。

以下程式碼範例顯示如何使用 create-endpoint。

AWS CLI

為自訂模型建立端點

下列create-endpoint範例會為先前訓練的自訂模型建立同步推論的端點。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的管理 Amazon Comprehend 端點。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 CreateEndpoint。

以下程式碼範例顯示如何使用 create-entity-recognizer。

AWS CLI

建立自訂實體辨識器

下列create-entity-recognizer範例會開始自訂實體辨識器模型的訓練程序。此範例使用包含訓練文件、 raw_text.csv和 CSV 實體清單的 CSV 檔案entity_list.csv來訓練模型。 entity-list.csv包含下列資料欄：文字和類型。


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://amzn-s3-demo-bucket/trainingdata/raw_text.csv},EntityList={S3Uri=s3://amzn-s3-demo-bucket/trainingdata/entity_list.csv}"
    --language-code en

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的自訂實體辨識。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 CreateEntityRecognizer。

以下程式碼範例顯示如何使用 create-flywheel。

AWS CLI

建立飛輪

下列create-flywheel範例會建立飛輪，以協調文件分類或實體辨識模型的持續訓練。此範例中的飛輪是用來管理 --active-model-arn標籤指定的現有訓練模型。飛輪建立時，會在 --input-data-lake標籤建立資料湖。


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://amzn-s3-demo-bucket"

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的飛輪概觀。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 CreateFlywheel。

以下程式碼範例顯示如何使用 delete-document-classifier。

AWS CLI

刪除自訂文件分類器

下列delete-document-classifier範例會刪除自訂文件分類器模型。


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

此命令不會產生輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的管理 Amazon Comprehend 端點。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DeleteDocumentClassifier。

以下程式碼範例顯示如何使用 delete-endpoint。

AWS CLI

刪除自訂模型的端點

下列delete-endpoint範例會刪除模型特定的端點。必須刪除所有端點，才能刪除模型。


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

此命令不會產生輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的管理 Amazon Comprehend 端點。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DeleteEndpoint。

以下程式碼範例顯示如何使用 delete-entity-recognizer。

AWS CLI

刪除自訂實體辨識器模型

下列delete-entity-recognizer範例會刪除自訂實體辨識器模型。


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

此命令不會產生輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的管理 Amazon Comprehend 端點。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DeleteEntityRecognizer。

以下程式碼範例顯示如何使用 delete-flywheel。

AWS CLI

刪除飛輪

下列delete-flywheel範例會刪除飛輪。不會刪除與飛輪相關聯的資料湖或模型。


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

此命令不會產生輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的飛輪概觀。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DeleteFlywheel。

以下程式碼範例顯示如何使用 delete-resource-policy。

AWS CLI

刪除以資源為基礎的政策

下列delete-resource-policy範例會從 Amazon Comprehend 資源刪除資源型政策。


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

此命令不會產生輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的在AWS 帳戶之間複製自訂模型。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DeleteResourcePolicy。

以下程式碼範例顯示如何使用 describe-dataset。

AWS CLI

描述飛輪資料集

下列describe-dataset範例會取得飛輪資料集的屬性。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的飛輪概觀。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeDataset。

以下程式碼範例顯示如何使用 describe-document-classification-job。

AWS CLI

描述文件分類任務

下列describe-document-classification-job範例會取得非同步文件分類任務的屬性。


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

輸出：


{
    "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://amzn-s3-demo-bucket/jobdata/",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-CLN-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的自訂分類。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeDocumentClassificationJob。

以下程式碼範例顯示如何使用 describe-document-classifier。

AWS CLI

描述文件分類器

下列describe-document-classifier範例會取得自訂文件分類器模型的屬性。


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

輸出：


{
    "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://amzn-s3-demo-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"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的建立和管理自訂模型。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeDocumentClassifier。

以下程式碼範例顯示如何使用 describe-dominant-language-detection-job。

AWS CLI

描述主要語言偵測偵測任務。

下列describe-dominant-language-detection-job範例會取得非同步主要語言偵測任務的屬性。


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

輸出：


{
    "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://amzn-s3-demo-bucket",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-LANGUAGE-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeDominantLanguageDetectionJob。

以下程式碼範例顯示如何使用 describe-endpoint。

AWS CLI

描述特定端點

下列describe-endpoint範例會取得模型特定端點的屬性。


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

輸出：


{
    "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"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的管理 Amazon Comprehend 端點。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeEndpoint。

以下程式碼範例顯示如何使用 describe-entities-detection-job。

AWS CLI

描述實體偵測任務

下列describe-entities-detection-job範例會取得非同步實體偵測任務的屬性。


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

輸出：


{
    "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://amzn-s3-demo-bucket/AsyncBatchJobs/",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://amzn-s3-demo-bucket/thefolder/111122223333-NER-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "LanguageCode": "en",
        "DataAccessRoleArn": "arn:aws:iam::12345678012:role/service-role/AmazonComprehendServiceRole-example-role"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeEntitiesDetectionJob。

以下程式碼範例顯示如何使用 describe-entity-recognizer。

AWS CLI

描述實體辨識器

下列describe-entity-recognizer範例會取得自訂實體辨識器模型的屬性。


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

輸出：


{
    "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://amzn-s3-demo-bucket/trainingdata/dataset/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "EntityList": {
                "S3Uri": "s3://amzn-s3-demo-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"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的自訂實體辨識。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeEntityRecognizer。

以下程式碼範例顯示如何使用 describe-events-detection-job。

AWS CLI

描述事件偵測任務。

下列describe-events-detection-job範例會取得非同步事件偵測任務的屬性。


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

輸出：


{
    "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://amzn-s3-demo-bucket/EventsData",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://amzn-s3-demo-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"
        ]
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeEventsDetectionJob。

以下程式碼範例顯示如何使用 describe-flywheel-iteration。

AWS CLI

描述飛輪反覆運算

下列describe-flywheel-iteration範例會取得飛輪反覆運算的屬性。


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

輸出：


{
    "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://amzn-s3-demo-destination-bucket/flywheel-entity/schemaVersion=1/20230616T200543Z/evaluation/20230616T211026Z/"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的飛輪概觀。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeFlywheelIteration。

以下程式碼範例顯示如何使用 describe-flywheel。

AWS CLI

描述飛輪

下列describe-flywheel範例會取得飛輪的屬性。在此範例中，與飛輪相關聯的模型是自訂分類器模型，該模型經過訓練，可將文件分類為垃圾郵件或非垃圾郵件，或 "ham"。


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

輸出：


{
    "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://amzn-s3-demo-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"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的飛輪概觀。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeFlywheel。

以下程式碼範例顯示如何使用 describe-key-phrases-detection-job。

AWS CLI

描述關鍵片語偵測任務

下列describe-key-phrases-detection-job範例會取得非同步金鑰片語偵測任務的屬性。


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

輸出：


{
    "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"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeKeyPhrasesDetectionJob。

以下程式碼範例顯示如何使用 describe-pii-entities-detection-job。

AWS CLI

描述 PII 實體偵測任務

下列describe-pii-entities-detection-job範例會取得非同步 pii 實體偵測任務的屬性。


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

輸出：


{
    "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://amzn-s3-demo-bucket/AsyncBatchJobs/",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://amzn-s3-demo-bucket/thefolder/111122223333-NER-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "LanguageCode": "en",
        "DataAccessRoleArn": "arn:aws:iam::12345678012:role/service-role/AmazonComprehendServiceRole-example-role"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribePiiEntitiesDetectionJob。

以下程式碼範例顯示如何使用 describe-resource-policy。

AWS CLI

描述連接到模型的資源政策

下列describe-resource-policy範例會取得連接至模型之資源型政策的屬性。


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

輸出：


{
    "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"
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的在AWS 帳戶之間複製自訂模型。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeResourcePolicy。

以下程式碼範例顯示如何使用 describe-sentiment-detection-job。

AWS CLI

描述情緒偵測任務

下列describe-sentiment-detection-job範例會取得非同步情緒偵測任務的屬性。


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

輸出：


{
    "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://amzn-s3-demo-bucket/MovieData",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-TS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "LanguageCode": "en",
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeSentimentDetectionJob。

以下程式碼範例顯示如何使用 describe-targeted-sentiment-detection-job。

AWS CLI

描述目標情緒偵測任務

下列describe-targeted-sentiment-detection-job範例會取得非同步目標情緒偵測任務的屬性。


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

輸出：


{
    "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://amzn-s3-demo-bucket/MovieData",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-TS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "LanguageCode": "en",
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeTargetedSentimentDetectionJob。

以下程式碼範例顯示如何使用 describe-topics-detection-job。

AWS CLI

描述主題偵測任務

下列describe-topics-detection-job範例會取得非同步主題偵測任務的屬性。


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

輸出：


{
    "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://amzn-s3-demo-bucket",
            "InputFormat": "ONE_DOC_PER_LINE"
        },
        "OutputDataConfig": {
            "S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-TOPICS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
        },
        "NumberOfTopics": 10,
        "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-examplerole"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DescribeTopicsDetectionJob。

以下程式碼範例顯示如何使用 detect-dominant-language。

AWS CLI

偵測輸入文字的主要語言

以下內容會detect-dominant-language分析輸入文字並識別主要語言。也會輸出預先訓練模型的可信度分數。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的主要語言。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DetectDominantLanguage。

以下程式碼範例顯示如何使用 detect-entities。

AWS CLI

在輸入文字中偵測具名實體

下列detect-entities範例會分析輸入文字，並傳回具名實體。每個預測也會輸出預先訓練模型的可信度分數。


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."

輸出：


{
    "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
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的實體。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DetectEntities。

以下程式碼範例顯示如何使用 detect-key-phrases。

AWS CLI

偵測輸入文字中的金鑰片語

下列detect-key-phrases範例會分析輸入文字，並識別金鑰名詞片語。每個預測也會輸出預先訓練模型的可信度分數。


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."

輸出：


{
    "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
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的關鍵詞。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DetectKeyPhrases。

以下程式碼範例顯示如何使用 detect-pii-entities。

AWS CLI

偵測輸入文字中的 pii 實體

下列detect-pii-entities範例會分析輸入文字，並識別包含個人身分識別資訊 (PII) 的實體。每個預測也會輸出預先訓練模型的可信度分數。


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."

輸出：


{
    "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
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的個人身分識別資訊 (PII)。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DetectPiiEntities。

以下程式碼範例顯示如何使用 detect-sentiment。

AWS CLI

偵測輸入文字的情緒

下列detect-sentiment範例會分析輸入文字，並傳回普遍情緒的推論 (POSITIVE、MIXED、 NEUTRAL或 NEGATIVE)。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的情緒

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DetectSentiment。

以下程式碼範例顯示如何使用 detect-syntax。

AWS CLI

偵測輸入文字中的語音部分

下列detect-syntax範例會分析輸入文字的語法，並傳回語音的不同部分。每個預測也會輸出預先訓練模型的可信度分數。


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

輸出：


{
    "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
            }
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的語法分析。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DetectSyntax。

以下程式碼範例顯示如何使用 detect-targeted-sentiment。

AWS CLI

在輸入文字中偵測具名實體的目標情緒

下列detect-targeted-sentiment範例會分析輸入文字，並傳回具名實體，以及與每個實體相關聯的目標情緒。也會輸出每個預測的預先訓練模型可信度分數。


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"

輸出：


{
    "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
                }
            ]
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的目標情緒。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 DetectTargetedSentiment。

以下程式碼範例顯示如何使用 import-model。

AWS CLI

匯入模型

下列import-model範例會從不同的 AWS 帳戶匯入模型。帳戶中的文件分類器模型444455556666具有資源型政策111122223333，允許帳戶匯入模型。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的在AWS 帳戶之間複製自訂模型。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ImportModel。

以下程式碼範例顯示如何使用 list-datasets。

AWS CLI

列出所有飛輪資料集

下列list-datasets範例列出與飛輪相關聯的所有資料集。


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

輸出：


{
    "DatasetPropertiesList": [
        {
            "DatasetArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity/dataset/example-dataset-1",
            "DatasetName": "example-dataset-1",
            "DatasetType": "TRAIN",
            "DatasetS3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-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"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的飛輪概觀。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListDatasets。

以下程式碼範例顯示如何使用 list-document-classification-jobs。

AWS CLI

列出所有文件分類任務

下列list-document-classification-jobs範例列出所有文件分類任務。


aws comprehend list-document-classification-jobs

輸出：


{
    "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://amzn-s3-demo-bucket/jobdata/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-bucket/jobdata/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/1234567890101-CLN-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz"
            },
            "DataAccessRoleArn": "arn:aws:iam::1234567890101:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的自訂分類。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListDocumentClassificationJobs。

以下程式碼範例顯示如何使用 list-document-classifier-summaries。

AWS CLI

列出所有已建立文件分類器的摘要

下列list-document-classifier-summaries範例列出所有建立的文件分類器摘要。


aws comprehend list-document-classifier-summaries

輸出：


{
    "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"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的建立和管理自訂模型。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListDocumentClassifierSummaries。

以下程式碼範例顯示如何使用 list-document-classifiers。

AWS CLI

列出所有文件分類器

下列list-document-classifiers範例列出所有訓練和訓練中文件分類器模型。


aws comprehend list-document-classifiers

輸出：


{
    "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://amzn-s3-demo-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://amzn-s3-demo-bucket/trainingdata"
            },
            "OutputDataConfig": {},
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-testorle",
            "Mode": "MULTI_CLASS"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的建立和管理自訂模型。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListDocumentClassifiers。

以下程式碼範例顯示如何使用 list-dominant-language-detection-jobs。

AWS CLI

列出所有主要語言偵測任務

下列list-dominant-language-detection-jobs範例列出所有進行中和已完成的非同步主要語言偵測任務。


aws comprehend list-dominant-language-detection-jobs

輸出：


{
    "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://amzn-s3-demo-bucket",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-bucket",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-LANGUAGE-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz"
            },
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListDominantLanguageDetectionJobs。

以下程式碼範例顯示如何使用 list-endpoints。

AWS CLI

列出所有端點

下列list-endpoints範例列出所有作用中的模型特定端點。


aws comprehend list-endpoints

輸出：


{
    "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"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的管理 Amazon Comprehend 端點。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListEndpoints。

以下程式碼範例顯示如何使用 list-entities-detection-jobs。

AWS CLI

列出所有實體偵測任務

下列list-entities-detection-jobs範例列出所有非同步實體偵測任務。


aws comprehend list-entities-detection-jobs

輸出：


{
    "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://amzn-s3-demo-bucket/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-bucket/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-bucket/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/111122223333-NER-e00597c36b448b91d70dea165EXAMPLE/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的實體。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListEntitiesDetectionJobs。

以下程式碼範例顯示如何使用 list-entity-recognizer-summaries。

AWS CLI

若要列出所有已建立實體辨識器的摘要

下列list-entity-recognizer-summaries範例列出所有實體辨識器摘要。


aws comprehend list-entity-recognizer-summaries

輸出：


{
    "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"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的自訂實體辨識。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListEntityRecognizerSummaries。

以下程式碼範例顯示如何使用 list-entity-recognizers。

AWS CLI

列出所有自訂實體辨識器

下列list-entity-recognizers範例列出所有建立的自訂實體識別器。


aws comprehend list-entity-recognizers

輸出：


{
    "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://amzn-s3-demo-bucket/trainingdata/dataset/",
                    "InputFormat": "ONE_DOC_PER_LINE"
                },
                "EntityList": {
                    "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-bucket/trainingdata/raw_txt.csv",
                    "InputFormat": "ONE_DOC_PER_LINE"
                },
                "EntityList": {
                    "S3Uri": "s3://amzn-s3-demo-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"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的自訂實體辨識。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListEntityRecognizers。

以下程式碼範例顯示如何使用 list-events-detection-jobs。

AWS CLI

列出所有事件偵測任務

下列list-events-detection-jobs範例列出所有非同步事件偵測任務。


aws comprehend list-events-detection-jobs

輸出：


{
    "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://amzn-s3-demo-source-bucket/EventsData/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-source-bucket/EventsData/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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"
            ]
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListEventsDetectionJobs。

以下程式碼範例顯示如何使用 list-flywheel-iteration-history。

AWS CLI

列出所有飛輪反覆運算歷史記錄

下列list-flywheel-iteration-history範例列出飛輪的所有反覆運算。


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

輸出：


{
    "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://amzn-s3-demo-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://amzn-s3-demo-bucket/example-flywheel-2/schemaVersion=1/20230616TEXAMPLE/evaluation/20230616TEXAMPLE/"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的飛輪概觀。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListFlywheelIterationHistory。

以下程式碼範例顯示如何使用 list-flywheels。

AWS CLI

列出所有飛輪

下列list-flywheels範例列出所有建立的飛輪。


aws comprehend list-flywheels

輸出：


{
    "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://amzn-s3-demo-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://amzn-s3-demo-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"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的飛輪概觀。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListFlywheels。

以下程式碼範例顯示如何使用 list-key-phrases-detection-jobs。

AWS CLI

列出所有關鍵片語偵測任務

下列list-key-phrases-detection-jobs範例列出所有進行中和已完成的非同步金鑰片語偵測任務。


aws comprehend list-key-phrases-detection-jobs

輸出：


{
    "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://amzn-s3-demo-source-bucket/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-bucket/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-bucket",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-KP-123456abcdeb0e11022f22a44EXAMPLE/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListKeyPhrasesDetectionJobs。

以下程式碼範例顯示如何使用 list-pii-entities-detection-jobs。

AWS CLI

列出所有 pii 實體偵測任務

下列list-pii-entities-detection-jobs範例列出所有進行中和已完成的非同步 pii 偵測任務。


aws comprehend list-pii-entities-detection-jobs

輸出：


{
    "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://amzn-s3-demo-bucket/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-bucket/AsyncBatchJobs/",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/111122223333-PII-d927562638cfa739331a99b3cEXAMPLE/output/"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role",
            "Mode": "ONLY_OFFSETS"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListPiiEntitiesDetectionJobs。

以下程式碼範例顯示如何使用 list-sentiment-detection-jobs。

AWS CLI

列出所有情緒偵測任務

下列list-sentiment-detection-jobs範例列出所有進行中和已完成的非同步情緒偵測任務。


aws comprehend list-sentiment-detection-jobs

輸出：


{
    "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://amzn-s3-demo-bucket/MovieData",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-bucket/MovieData2",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-TS-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListSentimentDetectionJobs。

以下程式碼範例顯示如何使用 list-tags-for-resource。

AWS CLI

列出資源的標籤

下列list-tags-for-resource範例列出 Amazon Comprehend 資源的標籤。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的標記您的資源。

如需 API 詳細資訊，請參閱《AWS CLI 命令參考》中的 ListTagsForResource。

以下程式碼範例顯示如何使用 list-targeted-sentiment-detection-jobs。

AWS CLI

列出所有目標情緒偵測任務

下列list-targeted-sentiment-detection-jobs範例列出所有進行中和已完成的非同步目標情緒偵測任務。


aws comprehend list-targeted-sentiment-detection-jobs

輸出：


{
    "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://amzn-s3-demo-bucket/MovieData",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-bucket/MovieData2",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-destination-bucket/testfolder/111122223333-TS-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz"
            },
            "LanguageCode": "en",
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListTargetedSentimentDetectionJobs。

以下程式碼範例顯示如何使用 list-topics-detection-jobs。

AWS CLI

列出所有主題偵測任務

下列list-topics-detection-jobs範例列出所有進行中和已完成的非同步主題偵測任務。


aws comprehend list-topics-detection-jobs

輸出：


{
    "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://amzn-s3-demo-bucket",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-bucket",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-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://amzn-s3-demo-bucket",
                "InputFormat": "ONE_DOC_PER_LINE"
            },
            "OutputDataConfig": {
                "S3Uri": "s3://amzn-s3-demo-destination-bucket/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a1EXAMPLE3/output/output.tar.gz"
            },
            "NumberOfTopics": 10,
            "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
        }
    ]
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 ListTopicsDetectionJobs。

以下程式碼範例顯示如何使用 put-resource-policy。

AWS CLI

連接以資源為基礎的政策

下列put-resource-policy範例會將資源型政策連接至模型，以便另一個 AWS 帳戶匯入。政策會連接到帳戶中的模型，111122223333並允許帳戶444455556666匯入模型。


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"]}}]}'

Ouput：


{
    "PolicyRevisionId": "aaa111d069d07afaa2aa3106aEXAMPLE"
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的在AWS 帳戶之間複製自訂模型。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 PutResourcePolicy。

以下程式碼範例顯示如何使用 start-document-classification-job。

AWS CLI

啟動文件分類任務

下列start-document-classification-job範例會在--input-data-config標籤指定的地址的所有檔案上，使用自訂模型啟動文件分類任務。在此範例中，輸入 S3 儲存貯體包含 SampleSMStext1.txt、 SampleSMStext2.txt和 SampleSMStext3.txt。此模型先前已針對垃圾郵件和非垃圾郵件的文件分類，或「ham」簡訊進行訓練。當任務完成時， output.tar.gz 會放在 --output-data-config標籤指定的位置。 output.tar.gz包含 predictions.jsonl，其中列出每個文件的分類。Json 輸出會列印在每個檔案的一行上，但在此格式化為可讀性。


aws comprehend start-document-classification-job \
    --job-name exampleclassificationjob \
    --input-data-config "S3Uri=s3://amzn-s3-demo-bucket-INPUT/jobdata/" \
    --output-data-config "S3Uri=s3://amzn-s3-demo-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 的內容：


"CONGRATULATIONS! TXT 2155550100 to win $5000"

SampleSMStext2.txt 的內容：


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

SampleSMStext3.txt 的內容：


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

輸出：


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

predictions.jsonl 的內容：


{"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}]}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的自訂分類。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StartDocumentClassificationJob。

以下程式碼範例顯示如何使用 start-dominant-language-detection-job。

AWS CLI

啟動非同步語言偵測任務

下列start-dominant-language-detection-job範例會針對位於 --input-data-config標籤所指定地址的所有檔案，啟動非同步語言偵測任務。此範例中的 S3 儲存貯體包含 Sampletext1.txt。當任務完成時，資料夾 output會放置在 --output-data-config標籤指定的位置。資料夾包含output.txt每個文字檔案的主要語言，以及每個預測的預先訓練模型可信度分數。


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

Sampletext1.txt 的內容：


"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."

輸出：


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

output.txt 的內容：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StartDominantLanguageDetectionJob。

以下程式碼範例顯示如何使用 start-entities-detection-job。

AWS CLI

範例 1：使用預先訓練的模型啟動標準實體偵測任務

下列start-entities-detection-job範例會針對位於 --input-data-config標籤所指定地址的所有檔案，啟動非同步實體偵測任務。此範例中的 S3 儲存貯體包含 Sampletext1.txt、 Sampletext2.txt和 Sampletext3.txt。當任務完成時，資料夾 output會放置在 --output-data-config標籤指定的位置。資料夾包含output.txt列出每個文字檔案中偵測到的所有具名實體，以及每個預測的預先訓練模型可信度分數。Json 輸出會列印在每個輸入檔案的一行上，但此處的格式為可讀性。


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

Sampletext1.txt 的內容：


"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 的內容：


"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 的內容：


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

輸出：


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

output.txt 具有行縮排的內容可讀性：


{
"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
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

範例 2：啟動自訂實體偵測任務

下列start-entities-detection-job範例會針對位於 --input-data-config標籤所指定地址的所有檔案，啟動非同步自訂實體偵測任務。在此範例中，此範例中的 S3 儲存貯體包含 SampleFeedback1.txt、 SampleFeedback2.txt和 SampleFeedback3.txt。實體辨識器模型已透過客戶支援意見回饋進行訓練，以辨識裝置名稱。當任務完成時，資料夾 output會放在 --output-data-config標籤指定的位置。資料夾包含 output.txt，列出每個文字檔案中偵測到的所有具名實體，以及每個預測的預先訓練模型可信度分數。Json 輸出會列印在每個檔案的一行上，但在此格式化為可讀性。


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://amzn-s3-demo-bucket/jobdata/" \
    --output-data-config "S3Uri=s3://amzn-s3-demo-destination-bucket/testfolder/" \
    --data-access-role-arn "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-IOrole"

SampleFeedback1.txt 的內容：


"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 的內容：


"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 的內容：


"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!"

輸出：


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

output.txt 具有行縮排的內容可讀性：


{
"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
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的自訂實體辨識。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StartEntitiesDetectionJob。

以下程式碼範例顯示如何使用 start-events-detection-job。

AWS CLI

啟動非同步事件偵測任務

下列start-events-detection-job範例會針對位於 --input-data-config標籤所指定地址的所有檔案，啟動非同步事件偵測任務。可能的目標事件類型包括 BANKRUPCTY、EMPLOYMENT、CORPORATE_ACQUISITION、INVESTMENT_GENERAL、CORPORATE_MERGERIPO、RIGHTS_ISSUE、SECONDARY_OFFERING、SHELF_OFFERING、 TENDER_OFFERING和 STOCK_SPLIT。此範例中的 S3 儲存貯體包含 SampleText1.txt、 SampleText2.txt和 SampleText3.txt。當任務完成時，資料夾 output會放置在 --output-data-config標籤指定的位置。資料夾包含 SampleText1.txt.out、 SampleText2.txt.out和 SampleText3.txt.out。JSON 輸出會列印在每個檔案的一行上，但此處的格式為可讀性。


aws comprehend start-events-detection-job \
    --job-name events-detection-1 \
    --input-data-config "S3Uri=s3://amzn-s3-demo-bucket/EventsData" \
    --output-data-config "S3Uri=s3://amzn-s3-demo-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 的內容：


"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 的內容：


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

SampleText3.txt 的內容：


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

輸出：


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

SampleText1.txt.out 具有行縮排的內容可讀性：


{
    "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 的內容：


{
    "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 的內容：


{
    "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
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StartEventsDetectionJob。

以下程式碼範例顯示如何使用 start-flywheel-iteration。

AWS CLI

啟動飛輪反覆運算

下列start-flywheel-iteration範例會啟動飛輪反覆運算。此操作使用飛輪中的任何新資料集來訓練新的模型版本。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的飛輪概觀。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StartFlywheelIteration。

以下程式碼範例顯示如何使用 start-key-phrases-detection-job。

AWS CLI

啟動金鑰片語偵測任務

下列start-key-phrases-detection-job範例會針對位於 --input-data-config標籤所指定地址的所有檔案，啟動非同步金鑰片語偵測任務。此範例中的 S3 儲存貯體包含 Sampletext1.txt、 Sampletext2.txt和 Sampletext3.txt。任務完成時，資料夾 output會放置在 --output-data-config標籤指定的位置。資料夾包含檔案output.txt，其中包含每個文字檔案中偵測到的所有金鑰片語，以及每個預測的預先訓練模型可信度分數。Json 輸出會列印在每個檔案的一行上，但在此格式化為可讀性。


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

Sampletext1.txt 的內容：


"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 的內容：


"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 的內容：


"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."

輸出：


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

output.txt 具有行縮排的內容以供讀取：


{
    "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
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StartKeyPhrasesDetectionJob。

以下程式碼範例顯示如何使用 start-pii-entities-detection-job。

AWS CLI

啟動非同步 PII 偵測任務

下列start-pii-entities-detection-job範例會針對位於 --input-data-config標籤所指定地址的所有檔案，啟動非同步個人身分識別資訊 (PII) 實體偵測任務。此範例中的 S3 儲存貯體包含 Sampletext1.txt、 Sampletext2.txt和 Sampletext3.txt。當任務完成時，資料夾 output會放置在 --output-data-config標籤指定的位置。資料夾包含 SampleText1.txt.out、 SampleText2.txt.out和 SampleText3.txt.out，列出每個文字檔案中的具名實體。Json 輸出會列印在每個檔案的一行上，但在此格式化為可讀性。


aws comprehend start-pii-entities-detection-job \
    --job-name entities_test \
    --language-code en \
    --input-data-config "S3Uri=s3://amzn-s3-demo-bucket/" \
    --output-data-config "S3Uri=s3://amzn-s3-demo-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 的內容：


"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 的內容：


"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 的內容：


"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."

輸出：


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

SampleText1.txt.out 具有行縮排的內容可讀性：


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

SampleText2.txt.out 具有行縮排的內容可讀性：


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

SampleText3.txt.out 具有行縮排的內容可讀性：


{
    "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
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StartPiiEntitiesDetectionJob。

以下程式碼範例顯示如何使用 start-sentiment-detection-job。

AWS CLI

啟動非同步情緒分析任務

下列start-sentiment-detection-job範例會針對位於 --input-data-config標籤所指定地址的所有檔案，啟動非同步情緒分析偵測任務。此範例中的 S3 儲存貯體資料夾包含 SampleMovieReview1.txt、 SampleMovieReview2.txt和 SampleMovieReview3.txt。當任務完成時，資料夾 output會放置在 --output-data-config標籤指定的位置。資料夾包含檔案 output.txt，其中包含每個文字檔案的慣用情緒，以及每個預測的預先訓練模型可信度分數。Json 輸出會列印在每個檔案的一行上，但在此格式化為可讀性。


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

SampleMovieReview1.txt 的內容：


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

SampleMovieReview2.txt 的內容：


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

SampleMovieReview3.txt 的內容：


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

輸出：


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

output.txt 具有可讀性縮排的內容：


{
    "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
        }
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StartSentimentDetectionJob。

以下程式碼範例顯示如何使用 start-targeted-sentiment-detection-job。

AWS CLI

啟動非同步目標情緒分析任務

下列start-targeted-sentiment-detection-job範例會針對位於 --input-data-config標籤所指定地址的所有檔案，啟動非同步目標情緒分析偵測任務。此範例中的 S3 儲存貯體資料夾包含 SampleMovieReview1.txt、 SampleMovieReview2.txt和 SampleMovieReview3.txt。當任務完成時， output.tar.gz 會放置在 --output-data-config標籤指定的位置。 output.tar.gz包含檔案 SampleMovieReview1.txt.out、 SampleMovieReview2.txt.out和 SampleMovieReview3.txt.out，每個檔案都包含單一輸入文字檔案的所有具名實體和相關聯的情緒。


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

SampleMovieReview1.txt 的內容：


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

SampleMovieReview2.txt 的內容：


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

SampleMovieReview3.txt 的內容：


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

輸出：


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

SampleMovieReview1.txt.out 具有行縮排的內容可讀性：


{
    "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
}

可讀性SampleMovieReview2.txt.out之行縮排的內容：


{
    "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
}

SampleMovieReview3.txt.out 具有行縮排的內容以供讀取：


{
    "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
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StartTargetedSentimentDetectionJob。

以下程式碼範例顯示如何使用 start-topics-detection-job。

AWS CLI

啟動主題偵測分析任務

下列start-topics-detection-job範例會針對位於 --input-data-config標籤所指定地址的所有檔案，啟動非同步主題偵測任務。當任務完成時，資料夾 output會放置在 --ouput-data-config標籤指定的位置。 output包含 topic-terms.csv 和 doc-topics.csv。第一個輸出檔案 topic-terms.csv 是集合中的主題清單。對於每個主題，清單預設會根據主題的權重包含依主題排列的熱門詞彙。第二個檔案列出與主題相關聯的文件doc-topics.csv，以及與該主題相關的文件比例。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的主題建模。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StartTopicsDetectionJob。

以下程式碼範例顯示如何使用 stop-dominant-language-detection-job。

AWS CLI

停止非同步主要語言偵測任務

下列stop-dominant-language-detection-job範例會停止進行中的非同步主要語言偵測任務。如果目前的任務狀態為IN_PROGRESS任務標記為終止並進入 STOP_REQUESTED 狀態。如果任務在停止之前完成，則會進入 COMPLETED 狀態。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StopDominantLanguageDetectionJob。

以下程式碼範例顯示如何使用 stop-entities-detection-job。

AWS CLI

停止非同步實體偵測任務

下列stop-entities-detection-job範例會停止進行中的非同步實體偵測任務。如果目前的任務狀態為IN_PROGRESS任務標記為終止並進入 STOP_REQUESTED 狀態。如果任務在停止之前完成，則會進入 COMPLETED 狀態。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StopEntitiesDetectionJob。

以下程式碼範例顯示如何使用 stop-events-detection-job。

AWS CLI

停止非同步事件偵測任務

下列stop-events-detection-job範例會停止進行中的非同步事件偵測任務。如果目前的任務狀態為IN_PROGRESS任務標記為終止並進入 STOP_REQUESTED 狀態。如果任務在停止之前完成，則會進入 COMPLETED 狀態。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StopEventsDetectionJob。

以下程式碼範例顯示如何使用 stop-key-phrases-detection-job。

AWS CLI

停止非同步金鑰片語偵測任務

下列stop-key-phrases-detection-job範例會停止進行中的非同步金鑰片語偵測任務。如果目前的任務狀態為IN_PROGRESS任務標記為終止並進入 STOP_REQUESTED 狀態。如果任務在停止之前完成，則會進入 COMPLETED 狀態。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StopKeyPhrasesDetectionJob。

以下程式碼範例顯示如何使用 stop-pii-entities-detection-job。

AWS CLI

停止非同步 pii 實體偵測任務

下列stop-pii-entities-detection-job範例會停止進行中的非同步 pii 實體偵測任務。如果目前的任務狀態為IN_PROGRESS任務標記為終止並進入 STOP_REQUESTED 狀態。如果任務在停止之前完成，則會進入 COMPLETED 狀態。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StopPiiEntitiesDetectionJob。

以下程式碼範例顯示如何使用 stop-sentiment-detection-job。

AWS CLI

停止非同步情緒偵測任務

下列stop-sentiment-detection-job範例會停止進行中的非同步情緒偵測任務。如果目前的任務狀態為IN_PROGRESS任務標記為終止並進入 STOP_REQUESTED 狀態。如果任務在停止之前完成，則會進入 COMPLETED 狀態。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StopSentimentDetectionJob。

以下程式碼範例顯示如何使用 stop-targeted-sentiment-detection-job。

AWS CLI

停止非同步目標情緒偵測任務

下列stop-targeted-sentiment-detection-job範例會停止進行中的非同步目標情緒偵測任務。如果目前的任務狀態為IN_PROGRESS任務標記為終止並進入 STOP_REQUESTED 狀態。如果任務在停止之前完成，則會進入 COMPLETED 狀態。


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

輸出：


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

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的 Amazon Comprehend 洞察的非同步分析。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StopTargetedSentimentDetectionJob。

以下程式碼範例顯示如何使用 stop-training-document-classifier。

AWS CLI

停止訓練文件分類器模型

下列stop-training-document-classifier範例會在進行中時停止訓練文件分類器模型。


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

此命令不會產生輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的建立和管理自訂模型。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StopTrainingDocumentClassifier。

以下程式碼範例顯示如何使用 stop-training-entity-recognizer。

AWS CLI

停止實體辨識器模型的訓練

下列stop-training-entity-recognizer範例會在進行中時停止實體辨識器模型的訓練。


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

此命令不會產生輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的建立和管理自訂模型。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 StopTrainingEntityRecognizer。

以下程式碼範例顯示如何使用 tag-resource。

AWS CLI

範例 1：標記資源

下列tag-resource範例會將單一標籤新增至 Amazon Comprehend 資源。


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

此命令沒有輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的標記您的資源。

範例 2：將多個標籤新增至資源

下列tag-resource範例會將多個標籤新增至 Amazon Comprehend 資源。


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

此命令沒有輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的標記您的資源。

如需 API 詳細資訊，請參閱《AWS CLI 命令參考》中的 TagResource。

以下程式碼範例顯示如何使用 untag-resource。

AWS CLI

範例 1：從資源中移除單一標籤

下列untag-resource範例會從 Amazon Comprehend 資源移除單一標籤。


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

此命令不會產生輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的標記您的資源。

範例 2：從資源中移除多個標籤

下列untag-resource範例會從 Amazon Comprehend 資源移除多個標籤。


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

此命令不會產生輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的標記您的資源。

如需 API 詳細資訊，請參閱《AWS CLI 命令參考》中的 UntagResource。

以下程式碼範例顯示如何使用 update-endpoint。

AWS CLI

範例 1：更新端點的推論單位

下列update-endpoint範例會更新端點的相關資訊。在此範例中，推論單位的數量會增加。


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

此命令不會產生輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的管理 Amazon Comprehend 端點。 Amazon Comprehend

範例 2：更新端點的動作模型

下列update-endpoint範例會更新端點的相關資訊。在此範例中，作用中模型已變更。


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

此命令不會產生輸出。

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的管理 Amazon Comprehend 端點。 Amazon Comprehend

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 UpdateEndpoint。

以下程式碼範例顯示如何使用 update-flywheel。

AWS CLI

更新飛輪組態

下列update-flywheel範例會更新飛輪組態。在此範例中，飛輪的作用中模型會更新。


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

輸出：


{
    "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://amzn-s3-demo-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"
    }
}

如需詳細資訊，請參閱《Amazon Comprehend 開發人員指南》中的飛輪概觀。

如需 API 詳細資訊，請參閱《 AWS CLI 命令參考》中的 UpdateFlywheel。

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