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Contoh Amazon Comprecept menggunakan AWS CLI - AWS Command Line Interface
Tindakan

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Contoh Amazon Comprecept menggunakan AWS CLI

Contoh kode berikut menunjukkan kepada Anda cara melakukan tindakan dan mengimplementasikan skenario umum dengan menggunakan AWS Command Line Interface with Amazon Comprecept.

Tindakan merupakan kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Sementara tindakan menunjukkan cara memanggil fungsi layanan individual, Anda dapat melihat tindakan dalam konteks dalam skenario terkait.

Setiap contoh menyertakan tautan ke kode sumber lengkap, di mana Anda dapat menemukan instruksi tentang cara mengatur dan menjalankan kode dalam konteks.

Tindakan

Contoh kode berikut menunjukkan cara menggunakanbatch-detect-dominant-language.

AWS CLI

Untuk mendeteksi bahasa dominan dari beberapa teks masukan

batch-detect-dominant-languageContoh berikut menganalisis beberapa teks masukan dan mengembalikan bahasa dominan masing-masing. Skor kepercayaan model pra-terlatih juga merupakan output untuk setiap prediksi.

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."

Output:

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

Untuk informasi selengkapnya, lihat Bahasa Dominan di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakanbatch-detect-entities.

AWS CLI

Untuk mendeteksi entitas dari beberapa teks masukan

batch-detect-entitiesContoh berikut menganalisis beberapa teks masukan dan mengembalikan entitas bernama masing-masing. Skor kepercayaan model pra-terlatih juga merupakan output untuk setiap prediksi.

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."

Output:

{
    "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": []
}

Untuk informasi selengkapnya, lihat En titas di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakanbatch-detect-key-phrases.

AWS CLI

Untuk mendeteksi frasa kunci dari beberapa input teks

batch-detect-key-phrasesContoh berikut menganalisis beberapa teks masukan dan mengembalikan frasa kata benda kunci dari masing-masing. Skor kepercayaan model pra-terlatih untuk setiap prediksi juga merupakan output.

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."

Output:

{
    "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": []
}

Untuk informasi selengkapnya, lihat F rasa Kunci di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakanbatch-detect-sentiment.

AWS CLI

Untuk mendeteksi sentimen yang berlaku dari beberapa teks input

batch-detect-sentimentContoh berikut menganalisis beberapa teks masukan dan mengembalikan sentimen yang berlaku (POSITIVENEUTRAL,MIXED,, atauNEGATIVE, dari masing-masing).

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

Output:

{
    "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": []
}

Untuk informasi selengkapnya, lihat Sentimen di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakanbatch-detect-syntax.

AWS CLI

Untuk memeriksa sintaks dan bagian ucapan kata-kata dalam beberapa teks masukan

batch-detect-syntaxContoh berikut menganalisis sintaks beberapa teks masukan dan mengembalikan bagian ucapan yang berbeda. Skor kepercayaan model pra-terlatih juga merupakan output untuk setiap prediksi.

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

Output:

{
    "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": []
}

Untuk informasi selengkapnya, lihat Analisis Sintaks di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakanbatch-detect-targeted-sentiment.

AWS CLI

Untuk mendeteksi sentimen dan setiap entitas bernama untuk beberapa teks masukan

batch-detect-targeted-sentimentContoh berikut menganalisis beberapa teks masukan dan mengembalikan entitas bernama bersama dengan sentimen yang berlaku yang melekat pada setiap entitas. Skor kepercayaan model pra-terlatih juga merupakan output untuk setiap prediksi.

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."

Output:

{
    "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": []
}

Untuk informasi selengkapnya, lihat Sentimen yang Ditargetkan di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakanclassify-document.

AWS CLI

Untuk mengklasifikasikan dokumen dengan titik akhir khusus model

classify-documentContoh berikut mengklasifikasikan dokumen dengan titik akhir model kustom. Model dalam contoh ini dilatih pada kumpulan data yang berisi pesan sms berlabel sebagai spam atau non-spam, atau, “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"

Output:

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

Untuk informasi selengkapnya, lihat Klasifikasi K ustom di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakancontains-pii-entities.

AWS CLI

Untuk menganalisis teks input untuk keberadaan informasi PII

contains-pii-entitiesContoh berikut menganalisis teks masukan untuk keberadaan informasi identitas pribadi (PII) dan mengembalikan label jenis entitas PII yang diidentifikasi seperti nama, alamat, nomor rekening bank, atau nomor telepon.

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."

Output:

{
    "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
        }
}

Untuk informasi selengkapnya, lihat Informasi Identifikasi Pribadi (PII) di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakancreate-dataset.

AWS CLI

Untuk membuat kumpulan data flywheel

create-datasetContoh berikut membuat dataset untuk flywheel. Dataset ini akan digunakan sebagai data pelatihan tambahan seperti yang ditentukan oleh --dataset-type tag.

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

Isi dari file://inputConfig.json:

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

Output:

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

Untuk informasi selengkapnya, lihat Ring kasan Flywheel di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakancreate-document-classifier.

AWS CLI

Untuk membuat pengklasifikasi dokumen untuk mengkategorikan dokumen

create-document-classifierContoh berikut memulai proses pelatihan untuk model pengklasifikasi dokumen. File data pelatihantraining.csv,, terletak di --input-data-config tag. training.csvadalah dokumen dua kolom di mana label, atau, klasifikasi disediakan di kolom pertama dan dokumen disediakan di kolom kedua.

aws comprehend create-document-classifier \
    --document-classifier-name example-classifier \
    --data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
    --input-data-config "S3Uri=s3://amzn-s3-demo-bucket/" \
    --input-data-config "S3Uri=s3://amzn-s3-demo-bucket/" \
    --language-code en

Output:

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

Untuk informasi selengkapnya, lihat Klasifikasi K ustom di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakancreate-endpoint.

AWS CLI

Untuk membuat titik akhir untuk model kustom

create-endpointContoh berikut membuat titik akhir untuk inferensi sinkron untuk model kustom yang telah dilatih sebelumnya.

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

Output:

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

Untuk informasi selengkapnya, lihat M engelola titik akhir Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakancreate-entity-recognizer.

AWS CLI

Untuk membuat pengenal entitas kustom

create-entity-recognizerContoh berikut memulai proses pelatihan untuk model pengenal entitas kustom. Contoh ini menggunakan file CSV yang berisi dokumen pelatihan,raw_text.csv, dan daftar entitas CSV, entity_list.csv untuk melatih model. entity-list.csvberisi kolom berikut: teks dan jenis.

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

Output:

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

Untuk informasi selengkapnya, lihat Pengenalan entitas kustom di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakancreate-flywheel.

AWS CLI

Untuk membuat flywheel

create-flywheelContoh berikut membuat flywheel untuk mengatur pelatihan berkelanjutan baik klasifikasi dokumen atau model pengenalan entitas. Roda flywheel dalam contoh ini dibuat untuk mengelola model terlatih yang ada yang ditentukan oleh --active-model-arn tag. Ketika flywheel dibuat, danau data dibuat di --input-data-lake tag.

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"

Output:

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

Untuk informasi selengkapnya, lihat Ring kasan Flywheel di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakandelete-document-classifier.

AWS CLI

Untuk menghapus pengklasifikasi dokumen khusus

delete-document-classifierContoh berikut menghapus model pengklasifikasi dokumen kustom.

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

Perintah ini tidak menghasilkan output.

Untuk informasi selengkapnya, lihat M engelola titik akhir Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakandelete-endpoint.

AWS CLI

Untuk menghapus titik akhir untuk model kustom

delete-endpointContoh berikut menghapus titik akhir khusus model. Semua titik akhir harus dihapus agar model dihapus.

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

Perintah ini tidak menghasilkan output.

Untuk informasi selengkapnya, lihat M engelola titik akhir Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakandelete-entity-recognizer.

AWS CLI

Untuk menghapus model pengenal entitas kustom

delete-entity-recognizerContoh berikut menghapus model pengenal entitas kustom.

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

Perintah ini tidak menghasilkan output.

Untuk informasi selengkapnya, lihat M engelola titik akhir Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakandelete-flywheel.

AWS CLI

Untuk menghapus flywheel

delete-flywheelContoh berikut menghapus flywheel. Danau data atau model yang terkait dengan flywheel tidak dihapus.

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

Perintah ini tidak menghasilkan output.

Untuk informasi selengkapnya, lihat ikh tisar Flywheel di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakandelete-resource-policy.

AWS CLI

Untuk menghapus kebijakan berbasis sumber daya

delete-resource-policyContoh berikut menghapus kebijakan berbasis sumber daya dari sumber daya Amazon Comprecept.

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

Perintah ini tidak menghasilkan output.

Untuk informasi selengkapnya, lihat Men yalin model kustom antar AWS akun di Panduan Pengembang Amazon Compreeham.

Contoh kode berikut menunjukkan cara menggunakandescribe-dataset.

AWS CLI

Untuk mendeskripsikan kumpulan data flywheel

describe-datasetContoh berikut mendapatkan properti dari dataset flywheel.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat Ring kasan Flywheel di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakandescribe-document-classification-job.

AWS CLI

Untuk menggambarkan pekerjaan klasifikasi dokumen

describe-document-classification-jobContoh berikut mendapatkan properti pekerjaan klasifikasi dokumen asinkron.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat Klasifikasi K ustom di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakandescribe-document-classifier.

AWS CLI

Untuk mendeskripsikan pengklasifikasi dokumen

describe-document-classifierContoh berikut mendapatkan properti model pengklasifikasi dokumen kustom.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat Membuat dan mengelola model kustom di Panduan Pengembang Amazon Compreeham.

Contoh kode berikut menunjukkan cara menggunakandescribe-dominant-language-detection-job.

AWS CLI

Untuk menggambarkan pekerjaan deteksi bahasa yang dominan.

describe-dominant-language-detection-jobContoh berikut mendapatkan properti dari pekerjaan deteksi bahasa dominan asinkron.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakandescribe-endpoint.

AWS CLI

Untuk menggambarkan titik akhir tertentu

describe-endpointContoh berikut mendapatkan properti dari titik akhir khusus model.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat M engelola titik akhir Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakandescribe-entities-detection-job.

AWS CLI

Untuk mendeskripsikan pekerjaan deteksi entitas

describe-entities-detection-jobContoh berikut mendapatkan properti dari pekerjaan deteksi entitas asinkron.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakandescribe-entity-recognizer.

AWS CLI

Untuk mendeskripsikan pengenal entitas

describe-entity-recognizerContoh berikut mendapatkan properti model pengenal entitas kustom.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat Pengenalan entitas kustom di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakandescribe-events-detection-job.

AWS CLI

Untuk menggambarkan pekerjaan deteksi peristiwa.

describe-events-detection-jobContoh berikut mendapatkan properti dari pekerjaan deteksi peristiwa asinkron.

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

Output:

{
    "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"
        ]
    }
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakandescribe-flywheel-iteration.

AWS CLI

Untuk menggambarkan iterasi flywheel

describe-flywheel-iterationContoh berikut mendapatkan properti dari iterasi flywheel.

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

Output:

{
    "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/"
    }
}

Untuk informasi selengkapnya, lihat ikh tisar Flywheel di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakandescribe-flywheel.

AWS CLI

Untuk menggambarkan roda flywheel

describe-flywheelContoh berikut mendapatkan properti flywheel. Dalam contoh ini, model yang terkait dengan flywheel adalah model pengklasifikasi khusus yang dilatih untuk mengklasifikasikan dokumen sebagai spam atau nonspam, atau, “ham”.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat Ring kasan Flywheel di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakandescribe-key-phrases-detection-job.

AWS CLI

Untuk menggambarkan pekerjaan deteksi frasa kunci

describe-key-phrases-detection-jobContoh berikut mendapatkan properti pekerjaan deteksi frasa kunci asinkron.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakandescribe-pii-entities-detection-job.

AWS CLI

Untuk menjelaskan pekerjaan deteksi entitas PII

describe-pii-entities-detection-jobContoh berikut mendapatkan properti dari pekerjaan deteksi entitas pii asinkron.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakandescribe-resource-policy.

AWS CLI

Untuk menjelaskan kebijakan sumber daya yang dilampirkan pada model

describe-resource-policyContoh berikut mendapatkan properti kebijakan berbasis sumber daya yang dilampirkan ke model.

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

Output:

{
    "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"
}

Untuk informasi selengkapnya, lihat Men yalin model kustom antar AWS akun di Panduan Pengembang Amazon Compreeham.

Contoh kode berikut menunjukkan cara menggunakandescribe-sentiment-detection-job.

AWS CLI

Untuk mendeskripsikan pekerjaan deteksi sentimen

describe-sentiment-detection-jobContoh berikut mendapatkan properti dari pekerjaan deteksi sentimen asinkron.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakandescribe-targeted-sentiment-detection-job.

AWS CLI

Untuk menggambarkan pekerjaan deteksi sentimen yang ditargetkan

describe-targeted-sentiment-detection-jobContoh berikut mendapatkan properti dari pekerjaan deteksi sentimen bertarget asinkron.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakandescribe-topics-detection-job.

AWS CLI

Untuk mendeskripsikan pekerjaan deteksi topik

describe-topics-detection-jobContoh berikut mendapatkan properti pekerjaan deteksi topik asinkron.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakandetect-dominant-language.

AWS CLI

Untuk mendeteksi bahasa dominan teks masukan

Berikut ini detect-dominant-language menganalisis teks input dan mengidentifikasi bahasa yang dominan. Skor kepercayaan model pra-terlatih juga merupakan output.

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

Output:

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

Untuk informasi selengkapnya, lihat Bahasa Dominan di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakandetect-entities.

AWS CLI

Untuk mendeteksi entitas bernama dalam teks masukan

detect-entitiesContoh berikut menganalisis teks masukan dan mengembalikan entitas bernama. Skor kepercayaan model pra-terlatih juga merupakan output untuk setiap prediksi.

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."

Output:

{
    "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
        }
    ]
}

Untuk informasi selengkapnya, lihat En titas di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakandetect-key-phrases.

AWS CLI

Untuk mendeteksi frasa kunci dalam teks masukan

detect-key-phrasesContoh berikut menganalisis teks input dan mengidentifikasi frasa kata benda kunci. Skor kepercayaan model pra-terlatih juga merupakan output untuk setiap prediksi.

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."

Output:

{
    "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
        }
    ]
}

Untuk informasi selengkapnya, lihat F rasa Kunci di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakandetect-pii-entities.

AWS CLI

Untuk mendeteksi entitas pii dalam teks masukan

detect-pii-entitiesContoh berikut menganalisis teks masukan dan mengidentifikasi entitas yang berisi informasi identitas pribadi (PII). Skor kepercayaan model pra-terlatih juga merupakan output untuk setiap prediksi.

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."

Output:

{
    "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
        }
    ]
}

Untuk informasi selengkapnya, lihat Informasi Identifikasi Pribadi (PII) di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakandetect-sentiment.

AWS CLI

Untuk mendeteksi sentimen teks masukan

detect-sentimentContoh berikut menganalisis teks masukan dan mengembalikan kesimpulan dari sentimen yang berlaku (POSITIVE,, NEUTRALMIXED, atauNEGATIVE).

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

Output:

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

Untuk informasi selengkapnya, lihat Sentimen di Panduan Pengembang Amazon Compredekah

Contoh kode berikut menunjukkan cara menggunakandetect-syntax.

AWS CLI

Untuk mendeteksi bagian-bagian ucapan dalam teks masukan

detect-syntaxContoh berikut menganalisis sintaks teks masukan dan mengembalikan bagian ucapan yang berbeda. Skor kepercayaan model pra-terlatih juga merupakan output untuk setiap prediksi.

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

Output:

{
    "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
            }
        }
    ]
}

Untuk informasi selengkapnya, lihat Analisis Sintaks di Panduan Pengembang Amazon Compredeh.

  • Untuk detail API, lihat DetectSyntax di Refer AWS CLI ensi Perintah.

Contoh kode berikut menunjukkan cara menggunakandetect-targeted-sentiment.

AWS CLI

Untuk mendeteksi sentimen yang ditargetkan dari entitas bernama dalam teks input

detect-targeted-sentimentContoh berikut menganalisis teks masukan dan mengembalikan entitas bernama selain sentimen yang ditargetkan terkait dengan setiap entitas. Skor kepercayaan model pra-terlatih untuk setiap prediksi juga merupakan output.

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"

Output:

{
    "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
                }
            ]
        }
    ]
}

Untuk informasi selengkapnya, lihat Sentimen yang Ditargetkan di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakanimport-model.

AWS CLI

Untuk mengimpor model

import-modelContoh berikut mengimpor model dari AWS akun yang berbeda. Model pengklasifikasi dokumen dalam akun 444455556666 memiliki kebijakan berbasis sumber daya yang memungkinkan akun 111122223333 untuk mengimpor model.

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

Output:

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

Untuk informasi selengkapnya, lihat Men yalin model kustom antar AWS akun di Panduan Pengembang Amazon Compreeham.

  • Untuk detail API, lihat ImportModel di Refer AWS CLI ensi Perintah.

Contoh kode berikut menunjukkan cara menggunakanlist-datasets.

AWS CLI

Untuk membuat daftar semua kumpulan data flywheel

list-datasetsContoh berikut mencantumkan semua kumpulan data yang terkait dengan flywheel.

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

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat Ring kasan Flywheel di Panduan Pengembang Amazon Compredeh.

  • Untuk detail API, lihat ListDatasets di Refer AWS CLI ensi Perintah.

Contoh kode berikut menunjukkan cara menggunakanlist-document-classification-jobs.

AWS CLI

Untuk daftar semua pekerjaan klasifikasi dokumen

list-document-classification-jobsContoh berikut mencantumkan semua pekerjaan klasifikasi dokumen.

aws comprehend list-document-classification-jobs

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat Klasifikasi K ustom di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakanlist-document-classifier-summaries.

AWS CLI

Untuk membuat daftar ringkasan semua pengklasifikasi dokumen yang dibuat

list-document-classifier-summariesContoh berikut mencantumkan semua ringkasan pengklasifikasi dokumen yang dibuat.

aws comprehend list-document-classifier-summaries

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat Membuat dan mengelola model kustom di Panduan Pengembang Amazon Compreeham.

Contoh kode berikut menunjukkan cara menggunakanlist-document-classifiers.

AWS CLI

Untuk daftar semua pengklasifikasi dokumen

list-document-classifiersContoh berikut mencantumkan semua model pengklasifikasi dokumen yang terlatih dan dalam pelatihan.

aws comprehend list-document-classifiers

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat Membuat dan mengelola model kustom di Panduan Pengembang Amazon Compreeham.

Contoh kode berikut menunjukkan cara menggunakanlist-dominant-language-detection-jobs.

AWS CLI

Untuk membuat daftar semua pekerjaan deteksi bahasa yang dominan

list-dominant-language-detection-jobsContoh berikut mencantumkan semua pekerjaan deteksi bahasa dominan asinkron yang sedang berlangsung dan diselesaikan.

aws comprehend list-dominant-language-detection-jobs

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanlist-endpoints.

AWS CLI

Untuk daftar semua titik akhir

list-endpointsContoh berikut mencantumkan semua titik akhir khusus model aktif.

aws comprehend list-endpoints

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat M engelola titik akhir Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanlist-entities-detection-jobs.

AWS CLI

Untuk membuat daftar semua pekerjaan deteksi entitas

list-entities-detection-jobsContoh berikut mencantumkan semua pekerjaan deteksi entitas asinkron.

aws comprehend list-entities-detection-jobs

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat En titas di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakanlist-entity-recognizer-summaries.

AWS CLI

Untuk daftar ringkasan untuk semua pengenal entitas yang dibuat

list-entity-recognizer-summariesContoh berikut mencantumkan semua ringkasan pengenal entitas.

aws comprehend list-entity-recognizer-summaries

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat Pengenalan entitas kustom di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakanlist-entity-recognizers.

AWS CLI

Untuk daftar semua pengenal entitas kustom

list-entity-recognizersContoh berikut mencantumkan semua pengenal entitas kustom yang dibuat.

aws comprehend list-entity-recognizers

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat Pengenalan entitas kustom di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakanlist-events-detection-jobs.

AWS CLI

Untuk membuat daftar semua pekerjaan deteksi peristiwa

list-events-detection-jobsContoh berikut mencantumkan semua pekerjaan deteksi peristiwa asinkron.

aws comprehend list-events-detection-jobs

Output:

{
    "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"
            ]
        }
    ]
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanlist-flywheel-iteration-history.

AWS CLI

Untuk membuat daftar semua riwayat iterasi flywheel

list-flywheel-iteration-historyContoh berikut mencantumkan semua iterasi flywheel.

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

Output:

{
    "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/"
        }
    ]
}

Untuk informasi selengkapnya, lihat ikh tisar Flywheel di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakanlist-flywheels.

AWS CLI

Untuk membuat daftar semua flywheel

list-flywheelsContoh berikut mencantumkan semua flywheel yang dibuat.

aws comprehend list-flywheels

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat ikh tisar Flywheel di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakanlist-key-phrases-detection-jobs.

AWS CLI

Untuk membuat daftar semua pekerjaan deteksi frasa kunci

list-key-phrases-detection-jobsContoh berikut mencantumkan semua pekerjaan deteksi frasa kunci asinkron yang sedang berlangsung dan selesai.

aws comprehend list-key-phrases-detection-jobs

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanlist-pii-entities-detection-jobs.

AWS CLI

Untuk membuat daftar semua pekerjaan deteksi entitas pii

list-pii-entities-detection-jobsContoh berikut mencantumkan semua pekerjaan deteksi pii asinkron yang sedang berlangsung dan selesai.

aws comprehend list-pii-entities-detection-jobs

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanlist-sentiment-detection-jobs.

AWS CLI

Untuk mencantumkan semua pekerjaan deteksi sentimen

list-sentiment-detection-jobsContoh berikut mencantumkan semua pekerjaan deteksi sentimen asinkron yang sedang berlangsung dan selesai.

aws comprehend list-sentiment-detection-jobs

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanlist-tags-for-resource.

AWS CLI

Untuk mencantumkan tag untuk sumber daya

list-tags-for-resourceContoh berikut mencantumkan tag untuk sumber daya Amazon Comprecept.

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

Output:

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

Untuk informasi selengkapnya, lihat Men andai sumber daya Anda di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakanlist-targeted-sentiment-detection-jobs.

AWS CLI

Untuk membuat daftar semua pekerjaan deteksi sentimen yang ditargetkan

list-targeted-sentiment-detection-jobsContoh berikut mencantumkan semua pekerjaan deteksi sentimen bertarget asinkron yang sedang berlangsung dan selesai.

aws comprehend list-targeted-sentiment-detection-jobs

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanlist-topics-detection-jobs.

AWS CLI

Untuk membuat daftar semua pekerjaan deteksi topik

list-topics-detection-jobsContoh berikut mencantumkan semua pekerjaan deteksi topik asinkron yang sedang berlangsung dan selesai.

aws comprehend list-topics-detection-jobs

Output:

{
    "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"
        }
    ]
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanput-resource-policy.

AWS CLI

Untuk melampirkan kebijakan berbasis sumber daya

put-resource-policyContoh berikut melampirkan kebijakan berbasis sumber daya ke model sehingga dapat diimpor oleh akun lain AWS . Kebijakan dilampirkan ke model dalam akun 111122223333 dan memungkinkan akun 444455556666 mengimpor model.

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"]}}]}'

Output:

{
    "PolicyRevisionId": "aaa111d069d07afaa2aa3106aEXAMPLE"
}

Untuk informasi selengkapnya, lihat Men yalin model kustom antar AWS akun di Panduan Pengembang Amazon Compreeham.

Contoh kode berikut menunjukkan cara menggunakanstart-document-classification-job.

AWS CLI

Untuk memulai pekerjaan klasifikasi dokumen

start-document-classification-jobContoh berikut memulai pekerjaan klasifikasi dokumen dengan model kustom pada semua file di alamat yang ditentukan oleh --input-data-config tag. Dalam contoh ini, bucket input S3 berisiSampleSMStext1.txt,SampleSMStext2.txt, danSampleSMStext3.txt. Model ini sebelumnya dilatih pada klasifikasi dokumen spam dan non-spam, atau, “ham”, pesan SMS. Ketika pekerjaan selesai, output.tar.gz diletakkan di lokasi yang ditentukan oleh --output-data-config tag. output.tar.gzberisi predictions.jsonl daftar klasifikasi setiap dokumen. Output Json dicetak pada satu baris per file, tetapi diformat di sini untuk keterbacaan.

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

Isi dari SampleSMStext1.txt:

"CONGRATULATIONS! TXT 2155550100 to win $5000"

Isi dari SampleSMStext2.txt:

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

Isi dari SampleSMStext3.txt:

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

Output:

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

Isi dari predictions.jsonl:

{"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}]}

Untuk informasi selengkapnya, lihat Klasifikasi K ustom di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakanstart-dominant-language-detection-job.

AWS CLI

Untuk memulai pekerjaan deteksi bahasa asinkron

start-dominant-language-detection-jobContoh berikut memulai pekerjaan deteksi bahasa asinkron untuk semua file yang terletak di alamat yang ditentukan oleh tag. --input-data-config Bucket S3 dalam contoh ini berisiSampletext1.txt. Ketika pekerjaan selesai, folder,output, ditempatkan di lokasi yang ditentukan oleh --output-data-config tag. Folder berisi output.txt yang berisi bahasa dominan dari setiap file teks serta skor kepercayaan model pra-terlatih untuk setiap prediksi.

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

Isi dari Sampletext1.txt:

"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."

Output:

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

Isi dari output.txt:

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

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstart-entities-detection-job.

AWS CLI

Contoh 1: Untuk memulai pekerjaan deteksi entitas standar menggunakan model pra-terlatih

start-entities-detection-jobContoh berikut memulai pekerjaan deteksi entitas asinkron untuk semua file yang terletak di alamat yang ditentukan oleh tag. --input-data-config Bucket S3 dalam contoh ini berisiSampletext1.txt,Sampletext2.txt, danSampletext3.txt. Ketika pekerjaan selesai, folder,output, ditempatkan di lokasi yang ditentukan oleh --output-data-config tag. Folder berisi daftar semua entitas bernama output.txt yang terdeteksi dalam setiap file teks serta skor kepercayaan model pra-terlatih untuk setiap prediksi. Output Json dicetak pada satu baris per file input, tetapi diformat di sini untuk keterbacaan.

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

Isi dari Sampletext1.txt:

"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."

Isi dari Sampletext2.txt:

"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. "

Isi dari Sampletext3.txt:

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

Output:

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

Isi output.txt dengan indentasi baris untuk keterbacaan:

{
"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
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh 2: Untuk memulai pekerjaan deteksi entitas kustom

start-entities-detection-jobContoh berikut memulai pekerjaan deteksi entitas kustom asinkron untuk semua file yang terletak di alamat yang ditentukan oleh tag. --input-data-config Dalam contoh ini, bucket S3 dalam contoh ini berisiSampleFeedback1.txt,SampleFeedback2.txt, danSampleFeedback3.txt. Model pengenal entitas dilatih pada Umpan Balik dukungan pelanggan untuk mengenali nama perangkat. Ketika pekerjaan selesai, folder,output, diletakkan di lokasi yang ditentukan oleh --output-data-config tag. Folder berisioutput.txt, yang mencantumkan semua entitas bernama yang terdeteksi dalam setiap file teks serta skor kepercayaan model pra-terlatih untuk setiap prediksi. Output Json dicetak pada satu baris per file, tetapi diformat di sini untuk keterbacaan.

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"

Isi dari SampleFeedback1.txt:

"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!"

Isi dari SampleFeedback2.txt:

"Hi, I have a discrepancy with my new bill. Could we get it sorted out? A rep added stuff I didn't sign up for when I did my AnyPhone 10 upgrade. | We can absolutely get this sorted!"

Isi dari SampleFeedback3.txt:

"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!"

Output:

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

Isi output.txt dengan indentasi baris untuk keterbacaan:

{
"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
}

Untuk informasi selengkapnya, lihat Pengenalan entitas kustom di Panduan Pengembang Amazon Compredekah.

Contoh kode berikut menunjukkan cara menggunakanstart-events-detection-job.

AWS CLI

Untuk memulai pekerjaan deteksi peristiwa asinkron

start-events-detection-jobContoh berikut memulai pekerjaan deteksi peristiwa asinkron untuk semua file yang terletak di alamat yang ditentukan oleh tag. --input-data-config Jenis acara target yang mungkin termasuk BANKRUPCTYEMPLOYMENT,CORPORATE_ACQUISITION,INVESTMENT_GENERAL,CORPORATE_MERGER,IPO,RIGHTS_ISSUE,SECONDARY_OFFERING,SHELF_OFFERING,TENDER_OFFERING, danSTOCK_SPLIT. Bucket S3 dalam contoh ini berisiSampleText1.txt,SampleText2.txt, danSampleText3.txt. Ketika pekerjaan selesai, folder,output, ditempatkan di lokasi yang ditentukan oleh --output-data-config tag. Folder berisiSampleText1.txt.out,SampleText2.txt.out, danSampleText3.txt.out. Output JSON dicetak pada satu baris per file, tetapi diformat di sini untuk keterbacaan.

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"

Isi dari SampleText1.txt:

"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."

Isi dari SampleText2.txt:

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

Isi dari SampleText3.txt:

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

Output:

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

Isi SampleText1.txt.out dengan indentasi baris untuk keterbacaan:

{
    "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
}

Isi dari SampleText2.txt.out:

{
    "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
}

Isi dari SampleText3.txt.out:

{
    "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
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstart-flywheel-iteration.

AWS CLI

Untuk memulai iterasi flywheel

start-flywheel-iterationContoh berikut memulai iterasi flywheel. Operasi ini menggunakan kumpulan data baru di flywheel untuk melatih versi model baru.

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

Output:

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

Untuk informasi selengkapnya, lihat ikh tisar Flywheel di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakanstart-key-phrases-detection-job.

AWS CLI

Untuk memulai pekerjaan deteksi frasa kunci

start-key-phrases-detection-jobContoh berikut memulai pekerjaan deteksi frasa kunci asinkron untuk semua file yang terletak di alamat yang ditentukan oleh tag. --input-data-config Bucket S3 dalam contoh ini berisiSampletext1.txt,Sampletext2.txt, danSampletext3.txt. Ketika pekerjaan selesai, folder,output, ditempatkan di lokasi yang ditentukan oleh --output-data-config tag. Folder berisi file output.txt yang berisi semua frasa kunci yang terdeteksi dalam setiap file teks dan skor kepercayaan model pra-terlatih untuk setiap prediksi. Output Json dicetak pada satu baris per file, tetapi diformat di sini untuk keterbacaan.

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

Isi dari Sampletext1.txt:

"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."

Isi dari Sampletext2.txt:

"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. "

Isi dari Sampletext3.txt:

"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."

Output:

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

Isi output.txt dengan indentasi baris untuk keterbacaan:

{
    "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
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstart-pii-entities-detection-job.

AWS CLI

Untuk memulai pekerjaan deteksi PII asinkron

start-pii-entities-detection-jobContoh berikut memulai pekerjaan deteksi entitas informasi identitas pribadi (PII) asinkron untuk semua file yang terletak di alamat yang ditentukan oleh tag. --input-data-config Bucket S3 dalam contoh ini berisiSampletext1.txt,Sampletext2.txt, danSampletext3.txt. Ketika pekerjaan selesai, folder,output, ditempatkan di lokasi yang ditentukan oleh --output-data-config tag. Folder berisiSampleText1.txt.out,SampleText2.txt.out, dan SampleText3.txt.out yang mencantumkan entitas bernama dalam setiap file teks. Output Json dicetak pada satu baris per file, tetapi diformat di sini untuk keterbacaan.

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

Isi dari Sampletext1.txt:

"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."

Isi dari Sampletext2.txt:

"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. "

Isi dari Sampletext3.txt:

"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."

Output:

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

Isi SampleText1.txt.out dengan indentasi baris untuk keterbacaan:

{
    "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
}

Isi SampleText2.txt.out dengan indentasi baris untuk keterbacaan:

{
    "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
}

Isi SampleText3.txt.out dengan indentasi baris untuk keterbacaan:

{
    "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
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstart-sentiment-detection-job.

AWS CLI

Untuk memulai pekerjaan analisis sentimen asinkron

start-sentiment-detection-jobContoh berikut memulai pekerjaan deteksi analisis sentimen asinkron untuk semua file yang terletak di alamat yang ditentukan oleh tag. --input-data-config Folder bucket S3 dalam contoh ini berisiSampleMovieReview1.txt,SampleMovieReview2.txt, danSampleMovieReview3.txt. Ketika pekerjaan selesai, folder,output, ditempatkan di lokasi yang ditentukan oleh --output-data-config tag. Folder berisi file,output.txt, yang berisi sentimen yang berlaku untuk setiap file teks dan skor kepercayaan model pra-terlatih untuk setiap prediksi. Output Json dicetak pada satu baris per file, tetapi diformat di sini untuk keterbacaan.

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

Isi dari SampleMovieReview1.txt:

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

Isi dari SampleMovieReview2.txt:

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

Isi dari SampleMovieReview3.txt:

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

Output:

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

Isi output.txt dengan baris indentasi untuk keterbacaan:

{
    "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
        }
    }
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstart-targeted-sentiment-detection-job.

AWS CLI

Untuk memulai pekerjaan analisis sentimen bertarget asinkron

start-targeted-sentiment-detection-jobContoh berikut memulai pekerjaan deteksi analisis sentimen bertarget asinkron untuk semua file yang terletak di alamat yang ditentukan oleh tag. --input-data-config Folder bucket S3 dalam contoh ini berisiSampleMovieReview1.txt,SampleMovieReview2.txt, danSampleMovieReview3.txt. Ketika pekerjaan selesai, output.tar.gz ditempatkan di lokasi yang ditentukan oleh --output-data-config tag. output.tar.gzberisi fileSampleMovieReview1.txt.out,SampleMovieReview2.txt.out, danSampleMovieReview3.txt.out, yang masing-masing berisi semua entitas bernama dan sentimen terkait untuk satu file teks input.

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

Isi dari SampleMovieReview1.txt:

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

Isi dari SampleMovieReview2.txt:

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

Isi dari SampleMovieReview3.txt:

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

Output:

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

Isi SampleMovieReview1.txt.out dengan indentasi baris untuk keterbacaan:

{
    "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
}

Isi indent SampleMovieReview2.txt.out asi baris untuk keterbacaan:

{
    "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
}

Isi SampleMovieReview3.txt.out dengan indentasi baris untuk keterbacaan:

{
    "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
}

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstart-topics-detection-job.

AWS CLI

Untuk memulai pekerjaan analisis deteksi topik

start-topics-detection-jobContoh berikut memulai pekerjaan deteksi topik asinkron untuk semua file yang terletak di alamat yang ditentukan oleh tag. --input-data-config Ketika pekerjaan selesai, folder,output, ditempatkan di lokasi yang ditentukan oleh --ouput-data-config tag. outputberisi topic-terms.csv dan doc-topics.csv. File keluaran pertama, topic-terms.csv, adalah daftar topik dalam koleksi. Untuk setiap topik, daftar tersebut mencakup, secara default, istilah teratas berdasarkan topik sesuai dengan bobotnya. File kedua,doc-topics.csv, mencantumkan dokumen yang terkait dengan topik dan proporsi dokumen yang berkaitan dengan topik tersebut.

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

Output:

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

Untuk informasi selengkapnya, lihat Pemodelan Top ik di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakanstop-dominant-language-detection-job.

AWS CLI

Untuk menghentikan pekerjaan deteksi bahasa dominan asinkron

stop-dominant-language-detection-jobContoh berikut menghentikan pekerjaan deteksi bahasa dominan asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah IN_PROGRESS pekerjaan ditandai untuk penghentian dan dimasukkan ke dalam STOP_REQUESTED status. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam COMPLETED keadaan.

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

Output:

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

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstop-entities-detection-job.

AWS CLI

Untuk menghentikan pekerjaan deteksi entitas asinkron

stop-entities-detection-jobContoh berikut menghentikan pekerjaan deteksi entitas asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah IN_PROGRESS pekerjaan ditandai untuk penghentian dan dimasukkan ke dalam STOP_REQUESTED status. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam COMPLETED keadaan.

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

Output:

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

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstop-events-detection-job.

AWS CLI

Untuk menghentikan pekerjaan deteksi peristiwa asinkron

stop-events-detection-jobContoh berikut menghentikan pekerjaan deteksi peristiwa asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah IN_PROGRESS pekerjaan ditandai untuk penghentian dan dimasukkan ke dalam STOP_REQUESTED status. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam COMPLETED keadaan.

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

Output:

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

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstop-key-phrases-detection-job.

AWS CLI

Untuk menghentikan pekerjaan deteksi frasa kunci asinkron

stop-key-phrases-detection-jobContoh berikut menghentikan pekerjaan deteksi frasa kunci asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah IN_PROGRESS pekerjaan ditandai untuk penghentian dan dimasukkan ke dalam STOP_REQUESTED status. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam COMPLETED keadaan.

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

Output:

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

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstop-pii-entities-detection-job.

AWS CLI

Untuk menghentikan pekerjaan deteksi entitas pii asinkron

stop-pii-entities-detection-jobContoh berikut menghentikan pekerjaan deteksi entitas pii asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah IN_PROGRESS pekerjaan ditandai untuk penghentian dan dimasukkan ke dalam STOP_REQUESTED status. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam COMPLETED keadaan.

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

Output:

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

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstop-sentiment-detection-job.

AWS CLI

Untuk menghentikan pekerjaan deteksi sentimen asinkron

stop-sentiment-detection-jobContoh berikut menghentikan pekerjaan deteksi sentimen asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah IN_PROGRESS pekerjaan ditandai untuk penghentian dan dimasukkan ke dalam STOP_REQUESTED status. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam COMPLETED keadaan.

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

Output:

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

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstop-targeted-sentiment-detection-job.

AWS CLI

Untuk menghentikan pekerjaan deteksi sentimen bertarget asinkron

stop-targeted-sentiment-detection-jobContoh berikut menghentikan pekerjaan deteksi sentimen bertarget asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah IN_PROGRESS pekerjaan ditandai untuk penghentian dan dimasukkan ke dalam STOP_REQUESTED status. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam COMPLETED keadaan.

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

Output:

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

Untuk informasi selengkapnya, lihat Analisis asinkron untuk wawasan Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanstop-training-document-classifier.

AWS CLI

Untuk menghentikan pelatihan model pengklasifikasi dokumen

stop-training-document-classifierContoh berikut menghentikan pelatihan model pengklasifikasi dokumen saat sedang berlangsung.

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

Perintah ini tidak menghasilkan output.

Untuk informasi selengkapnya, lihat Membuat dan mengelola model kustom di Panduan Pengembang Amazon Compreeham.

Contoh kode berikut menunjukkan cara menggunakanstop-training-entity-recognizer.

AWS CLI

Untuk menghentikan pelatihan model pengenal entitas

stop-training-entity-recognizerContoh berikut menghentikan pelatihan model pengenal entitas saat sedang berlangsung.

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

Perintah ini tidak menghasilkan output.

Untuk informasi selengkapnya, lihat Membuat dan mengelola model kustom di Panduan Pengembang Amazon Compreeham.

Contoh kode berikut menunjukkan cara menggunakantag-resource.

AWS CLI

Contoh 1: Untuk menandai sumber daya

tag-resourceContoh berikut menambahkan satu tag ke sumber daya Amazon Comprecept.

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

Perintah ini tidak memiliki output.

Untuk informasi selengkapnya, lihat Men andai sumber daya Anda di Panduan Pengembang Amazon Compredeh.

Contoh 2: Untuk menambahkan beberapa tag ke sumber daya

tag-resourceContoh berikut menambahkan beberapa tag ke sumber daya Amazon Comprecept.

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

Perintah ini tidak memiliki output.

Untuk informasi selengkapnya, lihat Men andai sumber daya Anda di Panduan Pengembang Amazon Compredeh.

  • Untuk detail API, lihat TagResource di Refer AWS CLI ensi Perintah.

Contoh kode berikut menunjukkan cara menggunakanuntag-resource.

AWS CLI

Contoh 1: Untuk menghapus tag tunggal dari sumber daya

untag-resourceContoh berikut menghapus satu tag dari sumber daya Amazon Comprecept.

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

Perintah ini tidak menghasilkan output.

Untuk informasi selengkapnya, lihat Men andai sumber daya Anda di Panduan Pengembang Amazon Compredeh.

Contoh 2: Untuk menghapus beberapa tag dari sumber daya

untag-resourceContoh berikut menghapus beberapa tag dari sumber daya Amazon Comprecept.

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

Perintah ini tidak menghasilkan output.

Untuk informasi selengkapnya, lihat Men andai sumber daya Anda di Panduan Pengembang Amazon Compredeh.

Contoh kode berikut menunjukkan cara menggunakanupdate-endpoint.

AWS CLI

Contoh 1: Untuk memperbarui unit inferensi titik akhir

update-endpointContoh berikut memperbarui informasi tentang titik akhir. Dalam contoh ini, jumlah unit inferensi meningkat.

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

Perintah ini tidak menghasilkan output.

Untuk informasi selengkapnya, lihat M engelola titik akhir Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh 2: Untuk memperbarui model aksi titik akhir

update-endpointContoh berikut memperbarui informasi tentang titik akhir. Dalam contoh ini, model aktif diubah.

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

Perintah ini tidak menghasilkan output.

Untuk informasi selengkapnya, lihat M engelola titik akhir Amazon Comprecept di Panduan Pengembang Amazon Compregre.

Contoh kode berikut menunjukkan cara menggunakanupdate-flywheel.

AWS CLI

Untuk memperbarui konfigurasi flywheel

update-flywheelContoh berikut memperbarui konfigurasi flywheel. Dalam contoh ini, model aktif untuk flywheel diperbarui.

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

Output:

{
    "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"
    }
}

Untuk informasi selengkapnya, lihat ikh tisar Flywheel di Panduan Pengembang Amazon Compredeh.