Ada lebih banyak contoh AWS SDK yang tersedia di repo Contoh [SDK AWS Doc](https://github.com/awsdocs/aws-doc-sdk-examples). GitHub
Terjemahan disediakan oleh mesin penerjemah. Jika konten terjemahan yang diberikan bertentangan dengan versi bahasa Inggris aslinya, utamakan versi bahasa Inggris.
# Amazon Comprehend contoh menggunakan AWS CLI
Contoh kode berikut menunjukkan cara melakukan tindakan dan menerapkan skenario umum dengan menggunakan Amazon AWS Command Line Interface Comprehend.
*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.
**Topics**
+ [Tindakan](#actions)
## Tindakan
### `batch-detect-dominant-language`
Contoh kode berikut menunjukkan cara menggunakan`batch-detect-dominant-language`.
**AWS CLI**
**Untuk mendeteksi bahasa dominan dari beberapa teks input**
`batch-detect-dominant-language`Contoh berikut menganalisis beberapa teks masukan dan mengembalikan bahasa dominan masing-masing. Skor kepercayaan model yang telah dilatih sebelumnya 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](https://docs.aws.amazon.com/comprehend/latest/dg/how-languages.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [BatchDetectDominantLanguage](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/batch-detect-dominant-language.html)di *Referensi AWS CLI Perintah*.
### `batch-detect-entities`
Contoh kode berikut menunjukkan cara menggunakan`batch-detect-entities`.
**AWS CLI**
**Untuk mendeteksi entitas dari beberapa teks masukan**
`batch-detect-entities`Contoh berikut menganalisis beberapa teks masukan dan mengembalikan entitas bernama masing-masing. Skor kepercayaan model yang telah dilatih sebelumnya 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 [Entitas](https://docs.aws.amazon.com/comprehend/latest/dg/how-entities.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [BatchDetectEntities](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/batch-detect-entities.html)di *Referensi AWS CLI Perintah*.
### `batch-detect-key-phrases`
Contoh kode berikut menunjukkan cara menggunakan`batch-detect-key-phrases`.
**AWS CLI**
**Untuk mendeteksi frase kunci dari beberapa input teks**
`batch-detect-key-phrases`Contoh berikut menganalisis beberapa teks masukan dan mengembalikan frase kata benda kunci masing-masing. Skor kepercayaan model yang telah dilatih sebelumnya 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 [Frasa Kunci](https://docs.aws.amazon.com/comprehend/latest/dg/how-key-phrases.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [BatchDetectKeyPhrases](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/batch-detect-key-phrases.html)di *Referensi AWS CLI Perintah*.
### `batch-detect-sentiment`
Contoh kode berikut menunjukkan cara menggunakan`batch-detect-sentiment`.
**AWS CLI**
**Untuk mendeteksi sentimen yang berlaku dari beberapa teks masukan**
`batch-detect-sentiment`Contoh berikut menganalisis beberapa teks masukan dan mengembalikan sentimen yang berlaku (`POSITIVE`,, `NEUTRAL``MIXED`, atau`NEGATIVE`, 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](https://docs.aws.amazon.com/comprehend/latest/dg/how-sentiment.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [BatchDetectSentiment](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/batch-detect-sentiment.html)di *Referensi AWS CLI Perintah*.
### `batch-detect-syntax`
Contoh kode berikut menunjukkan cara menggunakan`batch-detect-syntax`.
**AWS CLI**
**Untuk memeriksa sintaks dan bagian ucapan kata-kata dalam beberapa teks masukan**
`batch-detect-syntax`Contoh berikut menganalisis sintaks dari beberapa teks masukan dan mengembalikan bagian-bagian yang berbeda dari pidato. Skor kepercayaan model yang telah dilatih sebelumnya 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](https://docs.aws.amazon.com/comprehend/latest/dg/how-syntax.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [BatchDetectSyntax](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/batch-detect-syntax.html)di *Referensi AWS CLI Perintah*.
### `batch-detect-targeted-sentiment`
Contoh kode berikut menunjukkan cara menggunakan`batch-detect-targeted-sentiment`.
**AWS CLI**
**Untuk mendeteksi sentimen dan setiap entitas bernama untuk beberapa teks masukan**
`batch-detect-targeted-sentiment`Contoh berikut menganalisis beberapa teks masukan dan mengembalikan entitas bernama bersama dengan sentimen yang berlaku melekat pada setiap entitas. Skor kepercayaan model yang telah dilatih sebelumnya 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 Bertarget](https://docs.aws.amazon.com/comprehend/latest/dg/how-targeted-sentiment.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [BatchDetectTargetedSentiment](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/batch-detect-targeted-sentiment.html)di *Referensi AWS CLI Perintah*.
### `classify-document`
Contoh kode berikut menunjukkan cara menggunakan`classify-document`.
**AWS CLI**
**Untuk mengklasifikasikan dokumen dengan titik akhir khusus model**
`classify-document`Contoh berikut mengklasifikasikan dokumen dengan titik akhir model kustom. Model dalam contoh ini dilatih pada dataset yang berisi pesan sms berlabel 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 Kustom](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html) di Panduan *Pengembang Amazon Comprehend*.
+ Untuk detail API, lihat [ClassifyDocument](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/classify-document.html)di *Referensi AWS CLI Perintah*.
### `contains-pii-entities`
Contoh kode berikut menunjukkan cara menggunakan`contains-pii-entities`.
**AWS CLI**
**Untuk menganalisis teks input untuk keberadaan informasi PII**
`contains-pii-entities`Contoh berikut menganalisis teks input 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)](https://docs.aws.amazon.com/comprehend/latest/dg/pii.html) di Panduan Pengembang Amazon Comprehend.*
+ Untuk detail API, lihat [ContainsPiiEntities](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/contains-pii-entities.html)di *Referensi AWS CLI Perintah*.
### `create-dataset`
Contoh kode berikut menunjukkan cara menggunakan`create-dataset`.
**AWS CLI**
**Untuk membuat dataset flywheel**
`create-dataset`Contoh 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 [Ikhtisar Roda Gila di Panduan Pengembang](https://docs.aws.amazon.com/comprehend/latest/dg/flywheels-about.html) Amazon *Comprehend*.
+ Untuk detail API, lihat [CreateDataset](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/create-dataset.html)di *Referensi AWS CLI Perintah*.
### `create-document-classifier`
Contoh kode berikut menunjukkan cara menggunakan`create-document-classifier`.
**AWS CLI**
**Untuk membuat pengklasifikasi dokumen untuk mengkategorikan dokumen**
`create-document-classifier`Contoh berikut memulai proses pelatihan untuk model pengklasifikasi dokumen. File data pelatihan`training.csv`,, terletak di `--input-data-config` tag. `training.csv`adalah 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-arn arn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE \
--input-data-config "S3Uri=s3://amzn-s3-demo-bucket/" \
--language-code en
```
Output:
```
{
"DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier"
}
```
Untuk informasi selengkapnya, lihat [Klasifikasi Kustom](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html) di Panduan *Pengembang Amazon Comprehend*.
+ Untuk detail API, lihat [CreateDocumentClassifier](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/create-document-classifier.html)di *Referensi AWS CLI Perintah*.
### `create-endpoint`
Contoh kode berikut menunjukkan cara menggunakan`create-endpoint`.
**AWS CLI**
**Untuk membuat endpoint untuk model kustom**
`create-endpoint`Contoh berikut membuat titik akhir untuk inferensi sinkron untuk model kustom yang 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 [Mengelola titik akhir Amazon Comprehend di Panduan *Pengembang* Amazon](https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html) Comprehend.
+ Untuk detail API, lihat [CreateEndpoint](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/create-endpoint.html)di *Referensi AWS CLI Perintah*.
### `create-entity-recognizer`
Contoh kode berikut menunjukkan cara menggunakan`create-entity-recognizer`.
**AWS CLI**
**Untuk membuat pengenal entitas kustom**
`create-entity-recognizer`Contoh 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.csv`berisi 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 khusus](https://docs.aws.amazon.com/comprehend/latest/dg/custom-entity-recognition.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [CreateEntityRecognizer](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/create-entity-recognizer.html)di *Referensi AWS CLI Perintah*.
### `create-flywheel`
Contoh kode berikut menunjukkan cara menggunakan`create-flywheel`.
**AWS CLI**
**Untuk membuat flywheel**
`create-flywheel`Contoh berikut membuat flywheel untuk mengatur pelatihan berkelanjutan baik klasifikasi dokumen atau model pengenalan entitas. Flywheel dalam contoh ini dibuat untuk mengelola model terlatih yang sudah ada yang ditentukan oleh tag. `--active-model-arn` Saat flywheel dibuat, danau data dibuat di tag. `--input-data-lake`
```
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 [Ikhtisar Roda Gila di Panduan Pengembang](https://docs.aws.amazon.com/comprehend/latest/dg/flywheels-about.html) Amazon *Comprehend*.
+ Untuk detail API, lihat [CreateFlywheel](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/create-flywheel.html)di *Referensi AWS CLI Perintah*.
### `delete-document-classifier`
Contoh kode berikut menunjukkan cara menggunakan`delete-document-classifier`.
**AWS CLI**
**Untuk menghapus pengklasifikasi dokumen kustom**
`delete-document-classifier`Contoh 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 [Mengelola titik akhir Amazon Comprehend di Panduan *Pengembang* Amazon](https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html) Comprehend.
+ Untuk detail API, lihat [DeleteDocumentClassifier](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/delete-document-classifier.html)di *Referensi AWS CLI Perintah*.
### `delete-endpoint`
Contoh kode berikut menunjukkan cara menggunakan`delete-endpoint`.
**AWS CLI**
**Untuk menghapus titik akhir untuk model kustom**
`delete-endpoint`Contoh 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 [Mengelola titik akhir Amazon Comprehend di Panduan *Pengembang* Amazon](https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html) Comprehend.
+ Untuk detail API, lihat [DeleteEndpoint](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/delete-endpoint.html)di *Referensi AWS CLI Perintah*.
### `delete-entity-recognizer`
Contoh kode berikut menunjukkan cara menggunakan`delete-entity-recognizer`.
**AWS CLI**
**Untuk menghapus model pengenal entitas kustom**
`delete-entity-recognizer`Contoh 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 [Mengelola titik akhir Amazon Comprehend di Panduan *Pengembang* Amazon](https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html) Comprehend.
+ Untuk detail API, lihat [DeleteEntityRecognizer](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/delete-entity-recognizer.html)di *Referensi AWS CLI Perintah*.
### `delete-flywheel`
Contoh kode berikut menunjukkan cara menggunakan`delete-flywheel`.
**AWS CLI**
**Untuk menghapus flywheel**
`delete-flywheel`Contoh berikut menghapus flywheel. Data lake 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 [ikhtisar Flywheel di Panduan](https://docs.aws.amazon.com/comprehend/latest/dg/flywheels-about.html) Pengembang Amazon *Comprehend*.
+ Untuk detail API, lihat [DeleteFlywheel](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/delete-flywheel.html)di *Referensi AWS CLI Perintah*.
### `delete-resource-policy`
Contoh kode berikut menunjukkan cara menggunakan`delete-resource-policy`.
**AWS CLI**
**Untuk menghapus kebijakan berbasis sumber daya**
`delete-resource-policy`Contoh berikut menghapus kebijakan berbasis sumber daya dari sumber daya Amazon Comprehend.
```
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 [Menyalin model kustom antar AWS akun di Panduan](https://docs.aws.amazon.com/comprehend/latest/dg/custom-copy.html) Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [DeleteResourcePolicy](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/delete-resource-policy.html)di *Referensi AWS CLI Perintah*.
### `describe-dataset`
Contoh kode berikut menunjukkan cara menggunakan`describe-dataset`.
**AWS CLI**
**Untuk menggambarkan kumpulan data flywheel**
`describe-dataset`Contoh 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 [Ikhtisar Roda Gila di Panduan Pengembang](https://docs.aws.amazon.com/comprehend/latest/dg/flywheels-about.html) Amazon *Comprehend*.
+ Untuk detail API, lihat [DescribeDataset](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-dataset.html)di *Referensi AWS CLI Perintah*.
### `describe-document-classification-job`
Contoh kode berikut menunjukkan cara menggunakan`describe-document-classification-job`.
**AWS CLI**
**Untuk menggambarkan pekerjaan klasifikasi dokumen**
`describe-document-classification-job`Contoh 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 Kustom](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html) di Panduan *Pengembang Amazon Comprehend*.
+ Untuk detail API, lihat [DescribeDocumentClassificationJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-document-classification-job.html)di *Referensi AWS CLI Perintah*.
### `describe-document-classifier`
Contoh kode berikut menunjukkan cara menggunakan`describe-document-classifier`.
**AWS CLI**
**Untuk menggambarkan pengklasifikasi dokumen**
`describe-document-classifier`Contoh 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](https://docs.aws.amazon.com/comprehend/latest/dg/manage-models.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [DescribeDocumentClassifier](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-document-classifier.html)di *Referensi AWS CLI Perintah*.
### `describe-dominant-language-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`describe-dominant-language-detection-job`.
**AWS CLI**
**Untuk menggambarkan pekerjaan deteksi deteksi bahasa yang dominan.**
`describe-dominant-language-detection-job`Contoh 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [DescribeDominantLanguageDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-dominant-language-detection-job.html)di *Referensi AWS CLI Perintah*.
### `describe-endpoint`
Contoh kode berikut menunjukkan cara menggunakan`describe-endpoint`.
**AWS CLI**
**Untuk menggambarkan titik akhir tertentu**
`describe-endpoint`Contoh berikut mendapatkan properti dari endpoint model-spesifik.
```
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 [Mengelola titik akhir Amazon Comprehend di Panduan *Pengembang* Amazon](https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html) Comprehend.
+ Untuk detail API, lihat [DescribeEndpoint](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-endpoint.html)di *Referensi AWS CLI Perintah*.
### `describe-entities-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`describe-entities-detection-job`.
**AWS CLI**
**Untuk menggambarkan pekerjaan deteksi entitas**
`describe-entities-detection-job`Contoh 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [DescribeEntitiesDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-entities-detection-job.html)di *Referensi AWS CLI Perintah*.
### `describe-entity-recognizer`
Contoh kode berikut menunjukkan cara menggunakan`describe-entity-recognizer`.
**AWS CLI**
**Untuk menggambarkan pengenal entitas**
`describe-entity-recognizer`Contoh 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 khusus](https://docs.aws.amazon.com/comprehend/latest/dg/custom-entity-recognition.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [DescribeEntityRecognizer](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-entity-recognizer.html)di *Referensi AWS CLI Perintah*.
### `describe-events-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`describe-events-detection-job`.
**AWS CLI**
**Untuk menggambarkan pekerjaan deteksi peristiwa.**
`describe-events-detection-job`Contoh berikut mendapatkan properti 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [DescribeEventsDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-events-detection-job.html)di *Referensi AWS CLI Perintah*.
### `describe-flywheel-iteration`
Contoh kode berikut menunjukkan cara menggunakan`describe-flywheel-iteration`.
**AWS CLI**
**Untuk menggambarkan iterasi flywheel**
`describe-flywheel-iteration`Contoh 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 [ikhtisar Flywheel di Panduan](https://docs.aws.amazon.com/comprehend/latest/dg/flywheels-about.html) Pengembang Amazon *Comprehend*.
+ Untuk detail API, lihat [DescribeFlywheelIteration](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-flywheel-iteration.html)di *Referensi AWS CLI Perintah*.
### `describe-flywheel`
Contoh kode berikut menunjukkan cara menggunakan`describe-flywheel`.
**AWS CLI**
**Untuk menggambarkan flywheel**
`describe-flywheel`Contoh 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 [Ikhtisar Roda Gila di Panduan Pengembang](https://docs.aws.amazon.com/comprehend/latest/dg/flywheels-about.html) Amazon *Comprehend*.
+ Untuk detail API, lihat [DescribeFlywheel](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-flywheel.html)di *Referensi AWS CLI Perintah*.
### `describe-key-phrases-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`describe-key-phrases-detection-job`.
**AWS CLI**
**Untuk menggambarkan pekerjaan deteksi frasa kunci**
`describe-key-phrases-detection-job`Contoh berikut mendapatkan properti dari pekerjaan deteksi frase 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [DescribeKeyPhrasesDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-key-phrases-detection-job.html)di *Referensi AWS CLI Perintah*.
### `describe-pii-entities-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`describe-pii-entities-detection-job`.
**AWS CLI**
**Untuk menggambarkan pekerjaan deteksi entitas PII**
`describe-pii-entities-detection-job`Contoh berikut mendapatkan properti 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [DescribePiiEntitiesDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-pii-entities-detection-job.html)di *Referensi AWS CLI Perintah*.
### `describe-resource-policy`
Contoh kode berikut menunjukkan cara menggunakan`describe-resource-policy`.
**AWS CLI**
**Untuk menggambarkan kebijakan sumber daya yang dilampirkan pada model**
`describe-resource-policy`Contoh 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 [Menyalin model kustom antar AWS akun di Panduan](https://docs.aws.amazon.com/comprehend/latest/dg/custom-copy.html) Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [DescribeResourcePolicy](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-resource-policy.html)di *Referensi AWS CLI Perintah*.
### `describe-sentiment-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`describe-sentiment-detection-job`.
**AWS CLI**
**Untuk menggambarkan pekerjaan deteksi sentimen**
`describe-sentiment-detection-job`Contoh berikut mendapatkan properti 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [DescribeSentimentDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-sentiment-detection-job.html)di *Referensi AWS CLI Perintah*.
### `describe-targeted-sentiment-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`describe-targeted-sentiment-detection-job`.
**AWS CLI**
**Untuk menggambarkan pekerjaan deteksi sentimen yang ditargetkan**
`describe-targeted-sentiment-detection-job`Contoh berikut mendapatkan properti 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [DescribeTargetedSentimentDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-targeted-sentiment-detection-job.html)di *Referensi AWS CLI Perintah*.
### `describe-topics-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`describe-topics-detection-job`.
**AWS CLI**
**Untuk mendeskripsikan pekerjaan deteksi topik**
`describe-topics-detection-job`Contoh 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [DescribeTopicsDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/describe-topics-detection-job.html)di *Referensi AWS CLI Perintah*.
### `detect-dominant-language`
Contoh kode berikut menunjukkan cara menggunakan`detect-dominant-language`.
**AWS CLI**
**Untuk mendeteksi bahasa dominan teks input**
Berikut ini `detect-dominant-language` menganalisis teks input dan mengidentifikasi bahasa dominan. Skor kepercayaan model yang telah dilatih sebelumnya 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](https://docs.aws.amazon.com/comprehend/latest/dg/how-languages.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [DetectDominantLanguage](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-dominant-language.html)di *Referensi AWS CLI Perintah*.
### `detect-entities`
Contoh kode berikut menunjukkan cara menggunakan`detect-entities`.
**AWS CLI**
**Untuk mendeteksi entitas bernama dalam teks masukan**
`detect-entities`Contoh berikut menganalisis teks input dan mengembalikan entitas bernama. Skor kepercayaan model yang telah dilatih sebelumnya 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 [Entitas](https://docs.aws.amazon.com/comprehend/latest/dg/how-entities.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [DetectEntities](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-entities.html)di *Referensi AWS CLI Perintah*.
### `detect-key-phrases`
Contoh kode berikut menunjukkan cara menggunakan`detect-key-phrases`.
**AWS CLI**
**Untuk mendeteksi frase kunci dalam teks masukan**
`detect-key-phrases`Contoh berikut menganalisis teks input dan mengidentifikasi frase kata benda kunci. Skor kepercayaan model yang telah dilatih sebelumnya 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 [Frasa Kunci](https://docs.aws.amazon.com/comprehend/latest/dg/how-key-phrases.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [DetectKeyPhrases](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-key-phrases.html)di *Referensi AWS CLI Perintah*.
### `detect-pii-entities`
Contoh kode berikut menunjukkan cara menggunakan`detect-pii-entities`.
**AWS CLI**
**Untuk mendeteksi entitas pii dalam teks input**
`detect-pii-entities`Contoh berikut menganalisis teks input dan mengidentifikasi entitas yang berisi informasi identitas pribadi (PII). Skor kepercayaan model yang telah dilatih sebelumnya 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)](https://docs.aws.amazon.com/comprehend/latest/dg/pii.html) di Panduan Pengembang Amazon Comprehend.*
+ Untuk detail API, lihat [DetectPiiEntities](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-pii-entities.html)di *Referensi AWS CLI Perintah*.
### `detect-sentiment`
Contoh kode berikut menunjukkan cara menggunakan`detect-sentiment`.
**AWS CLI**
**Untuk mendeteksi sentimen teks input**
`detect-sentiment`Contoh berikut menganalisis teks masukan dan mengembalikan inferensi sentimen yang berlaku (`POSITIVE`,,`NEUTRAL`, `MIXED` atau). `NEGATIVE`
```
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](https://docs.aws.amazon.com/comprehend/latest/dg/how-sentiment.html) di Panduan Pengembang *Amazon Comprehend*
+ Untuk detail API, lihat [DetectSentiment](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-sentiment.html)di *Referensi AWS CLI Perintah*.
### `detect-syntax`
Contoh kode berikut menunjukkan cara menggunakan`detect-syntax`.
**AWS CLI**
**Untuk mendeteksi bagian-bagian ucapan dalam teks input**
`detect-syntax`Contoh berikut menganalisis sintaks teks masukan dan mengembalikan bagian-bagian yang berbeda dari pidato. Skor kepercayaan model yang telah dilatih sebelumnya 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](https://docs.aws.amazon.com/comprehend/latest/dg/how-syntax.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [DetectSyntax](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-syntax.html)di *Referensi AWS CLI Perintah*.
### `detect-targeted-sentiment`
Contoh kode berikut menunjukkan cara menggunakan`detect-targeted-sentiment`.
**AWS CLI**
**Untuk mendeteksi sentimen yang ditargetkan dari entitas bernama dalam teks input**
`detect-targeted-sentiment`Contoh berikut menganalisis teks masukan dan mengembalikan entitas bernama selain sentimen yang ditargetkan terkait dengan masing-masing entitas. Skor kepercayaan model yang telah dilatih sebelumnya 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 Bertarget](https://docs.aws.amazon.com/comprehend/latest/dg/how-targeted-sentiment.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [DetectTargetedSentiment](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/detect-targeted-sentiment.html)di *Referensi AWS CLI Perintah*.
### `import-model`
Contoh kode berikut menunjukkan cara menggunakan`import-model`.
**AWS CLI**
**Untuk mengimpor model**
`import-model`Contoh 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 [Menyalin model kustom antar AWS akun di Panduan](https://docs.aws.amazon.com/comprehend/latest/dg/custom-copy.html) Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [ImportModel](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/import-model.html)di *Referensi AWS CLI Perintah*.
### `list-datasets`
Contoh kode berikut menunjukkan cara menggunakan`list-datasets`.
**AWS CLI**
**Untuk membuat daftar semua dataset flywheel**
`list-datasets`Contoh berikut mencantumkan semua dataset 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 [Ikhtisar Roda Gila di Panduan Pengembang](https://docs.aws.amazon.com/comprehend/latest/dg/flywheels-about.html) Amazon *Comprehend*.
+ Untuk detail API, lihat [ListDatasets](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-datasets.html)di *Referensi AWS CLI Perintah*.
### `list-document-classification-jobs`
Contoh kode berikut menunjukkan cara menggunakan`list-document-classification-jobs`.
**AWS CLI**
**Untuk daftar semua pekerjaan klasifikasi dokumen**
`list-document-classification-jobs`Contoh 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 Kustom](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html) di Panduan *Pengembang Amazon Comprehend*.
+ Untuk detail API, lihat [ListDocumentClassificationJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-document-classification-jobs.html)di *Referensi AWS CLI Perintah*.
### `list-document-classifier-summaries`
Contoh kode berikut menunjukkan cara menggunakan`list-document-classifier-summaries`.
**AWS CLI**
**Untuk membuat daftar ringkasan semua pengklasifikasi dokumen yang dibuat**
`list-document-classifier-summaries`Contoh 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](https://docs.aws.amazon.com/comprehend/latest/dg/manage-models.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [ListDocumentClassifierSummaries](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-document-classifier-summaries.html)di *Referensi AWS CLI Perintah*.
### `list-document-classifiers`
Contoh kode berikut menunjukkan cara menggunakan`list-document-classifiers`.
**AWS CLI**
**Untuk daftar semua pengklasifikasi dokumen**
`list-document-classifiers`Contoh berikut mencantumkan semua model pengklasifikasi dokumen 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](https://docs.aws.amazon.com/comprehend/latest/dg/manage-models.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [ListDocumentClassifiers](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-document-classifiers.html)di *Referensi AWS CLI Perintah*.
### `list-dominant-language-detection-jobs`
Contoh kode berikut menunjukkan cara menggunakan`list-dominant-language-detection-jobs`.
**AWS CLI**
**Untuk membuat daftar semua pekerjaan deteksi bahasa yang dominan**
`list-dominant-language-detection-jobs`Contoh 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [ListDominantLanguageDetectionJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-dominant-language-detection-jobs.html)di *Referensi AWS CLI Perintah*.
### `list-endpoints`
Contoh kode berikut menunjukkan cara menggunakan`list-endpoints`.
**AWS CLI**
**Untuk daftar semua titik akhir**
`list-endpoints`Contoh 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 [Mengelola titik akhir Amazon Comprehend di Panduan *Pengembang* Amazon](https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html) Comprehend.
+ Untuk detail API, lihat [ListEndpoints](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-endpoints.html)di *Referensi AWS CLI Perintah*.
### `list-entities-detection-jobs`
Contoh kode berikut menunjukkan cara menggunakan`list-entities-detection-jobs`.
**AWS CLI**
**Untuk mencantumkan semua pekerjaan deteksi entitas**
`list-entities-detection-jobs`Contoh 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 [Entitas](https://docs.aws.amazon.com/comprehend/latest/dg/how-entities.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [ListEntitiesDetectionJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-entities-detection-jobs.html)di *Referensi AWS CLI Perintah*.
### `list-entity-recognizer-summaries`
Contoh kode berikut menunjukkan cara menggunakan`list-entity-recognizer-summaries`.
**AWS CLI**
**Untuk daftar ringkasan untuk semua pengenal entitas yang dibuat**
`list-entity-recognizer-summaries`Contoh 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 khusus](https://docs.aws.amazon.com/comprehend/latest/dg/custom-entity-recognition.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [ListEntityRecognizerSummaries](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-entity-recognizer-summaries.html)di *Referensi AWS CLI Perintah*.
### `list-entity-recognizers`
Contoh kode berikut menunjukkan cara menggunakan`list-entity-recognizers`.
**AWS CLI**
**Untuk daftar semua pengenal entitas kustom**
`list-entity-recognizers`Contoh 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 khusus](https://docs.aws.amazon.com/comprehend/latest/dg/custom-entity-recognition.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [ListEntityRecognizers](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-entity-recognizers.html)di *Referensi AWS CLI Perintah*.
### `list-events-detection-jobs`
Contoh kode berikut menunjukkan cara menggunakan`list-events-detection-jobs`.
**AWS CLI**
**Untuk mencantumkan semua pekerjaan deteksi peristiwa**
`list-events-detection-jobs`Contoh 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [ListEventsDetectionJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-events-detection-jobs.html)di *Referensi AWS CLI Perintah*.
### `list-flywheel-iteration-history`
Contoh kode berikut menunjukkan cara menggunakan`list-flywheel-iteration-history`.
**AWS CLI**
**Untuk mencantumkan semua riwayat iterasi flywheel**
`list-flywheel-iteration-history`Contoh 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 [ikhtisar Flywheel di Panduan](https://docs.aws.amazon.com/comprehend/latest/dg/flywheels-about.html) Pengembang Amazon *Comprehend*.
+ Untuk detail API, lihat [ListFlywheelIterationHistory](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-flywheel-iteration-history.html)di *Referensi AWS CLI Perintah*.
### `list-flywheels`
Contoh kode berikut menunjukkan cara menggunakan`list-flywheels`.
**AWS CLI**
**Untuk daftar semua flywheels**
`list-flywheels`Contoh berikut mencantumkan semua flywheels 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 [ikhtisar Flywheel di Panduan](https://docs.aws.amazon.com/comprehend/latest/dg/flywheels-about.html) Pengembang Amazon *Comprehend*.
+ Untuk detail API, lihat [ListFlywheels](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-flywheels.html)di *Referensi AWS CLI Perintah*.
### `list-key-phrases-detection-jobs`
Contoh kode berikut menunjukkan cara menggunakan`list-key-phrases-detection-jobs`.
**AWS CLI**
**Untuk membuat daftar semua pekerjaan deteksi frase kunci**
`list-key-phrases-detection-jobs`Contoh berikut mencantumkan semua pekerjaan deteksi frase kunci asinkron yang sedang berlangsung dan diselesaikan.
```
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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [ListKeyPhrasesDetectionJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-key-phrases-detection-jobs.html)di *Referensi AWS CLI Perintah*.
### `list-pii-entities-detection-jobs`
Contoh kode berikut menunjukkan cara menggunakan`list-pii-entities-detection-jobs`.
**AWS CLI**
**Untuk mencantumkan semua pekerjaan deteksi entitas pii**
`list-pii-entities-detection-jobs`Contoh berikut mencantumkan semua pekerjaan deteksi pii asinkron yang sedang berlangsung dan diselesaikan.
```
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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [ListPiiEntitiesDetectionJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-pii-entities-detection-jobs.html)di *Referensi AWS CLI Perintah*.
### `list-sentiment-detection-jobs`
Contoh kode berikut menunjukkan cara menggunakan`list-sentiment-detection-jobs`.
**AWS CLI**
**Untuk membuat daftar semua pekerjaan deteksi sentimen**
`list-sentiment-detection-jobs`Contoh berikut mencantumkan semua pekerjaan deteksi sentimen asinkron yang sedang berlangsung dan diselesaikan.
```
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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [ListSentimentDetectionJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-sentiment-detection-jobs.html)di *Referensi AWS CLI Perintah*.
### `list-tags-for-resource`
Contoh kode berikut menunjukkan cara menggunakan`list-tags-for-resource`.
**AWS CLI**
**Untuk daftar tag untuk sumber daya**
`list-tags-for-resource`Contoh berikut mencantumkan tag untuk sumber daya Amazon Comprehend.
```
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 [Menandai sumber daya Anda](https://docs.aws.amazon.com/comprehend/latest/dg/tagging.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [ListTagsForResource](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-tags-for-resource.html)di *Referensi AWS CLI Perintah*.
### `list-targeted-sentiment-detection-jobs`
Contoh kode berikut menunjukkan cara menggunakan`list-targeted-sentiment-detection-jobs`.
**AWS CLI**
**Untuk membuat daftar semua pekerjaan deteksi sentimen yang ditargetkan**
`list-targeted-sentiment-detection-jobs`Contoh berikut mencantumkan semua pekerjaan deteksi sentimen bertarget asinkron yang sedang berlangsung dan diselesaikan.
```
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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [ListTargetedSentimentDetectionJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-targeted-sentiment-detection-jobs.html)di *Referensi AWS CLI Perintah*.
### `list-topics-detection-jobs`
Contoh kode berikut menunjukkan cara menggunakan`list-topics-detection-jobs`.
**AWS CLI**
**Untuk mencantumkan semua pekerjaan deteksi topik**
`list-topics-detection-jobs`Contoh berikut mencantumkan semua pekerjaan deteksi topik asinkron yang sedang berlangsung dan diselesaikan.
```
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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [ListTopicsDetectionJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/list-topics-detection-jobs.html)di *Referensi AWS CLI Perintah*.
### `put-resource-policy`
Contoh kode berikut menunjukkan cara menggunakan`put-resource-policy`.
**AWS CLI**
**Untuk melampirkan kebijakan berbasis sumber daya**
`put-resource-policy`Contoh 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 [Menyalin model kustom antar AWS akun di Panduan](https://docs.aws.amazon.com/comprehend/latest/dg/custom-copy.html) Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [PutResourcePolicy](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/put-resource-policy.html)di *Referensi AWS CLI Perintah*.
### `start-document-classification-job`
Contoh kode berikut menunjukkan cara menggunakan`start-document-classification-job`.
**AWS CLI**
**Untuk memulai pekerjaan klasifikasi dokumen**
`start-document-classification-job`Contoh 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 berisi`SampleSMStext1.txt`,`SampleSMStext2.txt`, dan`SampleSMStext3.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.gz`berisi `predictions.jsonl` yang mencantumkan 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 Kustom](https://docs.aws.amazon.com/comprehend/latest/dg/how-document-classification.html) di Panduan *Pengembang Amazon Comprehend*.
+ Untuk detail API, lihat [StartDocumentClassificationJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-document-classification-job.html)di *Referensi AWS CLI Perintah*.
### `start-dominant-language-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`start-dominant-language-detection-job`.
**AWS CLI**
**Untuk memulai pekerjaan deteksi bahasa asinkron**
`start-dominant-language-detection-job`Contoh 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 berisi`Sampletext1.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 masing-masing file teks serta skor kepercayaan model yang telah dilatih sebelumnya 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StartDominantLanguageDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-dominant-language-detection-job.html)di *Referensi AWS CLI Perintah*.
### `start-entities-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`start-entities-detection-job`.
**AWS CLI**
**Contoh 1: Untuk memulai pekerjaan deteksi entitas standar menggunakan model yang telah dilatih sebelumnya**
`start-entities-detection-job`Contoh 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 berisi`Sampletext1.txt`,`Sampletext2.txt`, dan`Sampletext3.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 yang telah dilatih sebelumnya 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 garis 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
**Contoh 2: Untuk memulai pekerjaan deteksi entitas kustom**
`start-entities-detection-job`Contoh 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 berisi`SampleFeedback1.txt`,`SampleFeedback2.txt`, dan`SampleFeedback3.txt`. Model pengenal entitas dilatih tentang Umpan Balik dukungan pelanggan untuk mengenali nama perangkat. Ketika pekerjaan selesai, folder,`output`, diletakkan di lokasi yang ditentukan oleh `--output-data-config` tag. Folder berisi`output.txt`, yang mencantumkan semua entitas bernama yang terdeteksi dalam setiap file teks serta skor kepercayaan model yang telah dilatih sebelumnya 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 garis 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 khusus](https://docs.aws.amazon.com/comprehend/latest/dg/custom-entity-recognition.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [StartEntitiesDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-entities-detection-job.html)di *Referensi AWS CLI Perintah*.
### `start-events-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`start-events-detection-job`.
**AWS CLI**
**Untuk memulai pekerjaan deteksi peristiwa asinkron**
`start-events-detection-job`Contoh 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 `BANKRUPCTY``EMPLOYMENT`,`CORPORATE_ACQUISITION`,,`INVESTMENT_GENERAL`,`CORPORATE_MERGER`,`IPO`,`RIGHTS_ISSUE`,`SECONDARY_OFFERING`,`SHELF_OFFERING`,`TENDER_OFFERING`, dan`STOCK_SPLIT`. Bucket S3 dalam contoh ini berisi`SampleText1.txt`,`SampleText2.txt`, dan`SampleText3.txt`. Ketika pekerjaan selesai, folder,`output`, ditempatkan di lokasi yang ditentukan oleh `--output-data-config` tag. Folder berisi`SampleText1.txt.out`,`SampleText2.txt.out`, dan`SampleText3.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 garis 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StartEventsDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-events-detection-job.html)di *Referensi AWS CLI Perintah*.
### `start-flywheel-iteration`
Contoh kode berikut menunjukkan cara menggunakan`start-flywheel-iteration`.
**AWS CLI**
**Untuk memulai iterasi flywheel**
`start-flywheel-iteration`Contoh berikut memulai iterasi flywheel. Operasi ini menggunakan dataset 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 [ikhtisar Flywheel di Panduan](https://docs.aws.amazon.com/comprehend/latest/dg/flywheels-about.html) Pengembang Amazon *Comprehend*.
+ Untuk detail API, lihat [StartFlywheelIteration](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-flywheel-iteration.html)di *Referensi AWS CLI Perintah*.
### `start-key-phrases-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`start-key-phrases-detection-job`.
**AWS CLI**
**Untuk memulai pekerjaan deteksi frasa kunci**
`start-key-phrases-detection-job`Contoh berikut memulai pekerjaan deteksi frase kunci asinkron untuk semua file yang terletak di alamat yang ditentukan oleh tag. `--input-data-config` Bucket S3 dalam contoh ini berisi`Sampletext1.txt`,`Sampletext2.txt`, dan`Sampletext3.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 yang telah dilatih sebelumnya 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 garis 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StartKeyPhrasesDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-key-phrases-detection-job.html)di *Referensi AWS CLI Perintah*.
### `start-pii-entities-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`start-pii-entities-detection-job`.
**AWS CLI**
**Untuk memulai pekerjaan deteksi PII asinkron**
`start-pii-entities-detection-job`Contoh berikut memulai pekerjaan deteksi entitas informasi pribadi asinkron (PII) untuk semua file yang terletak di alamat yang ditentukan oleh tag. `--input-data-config` Bucket S3 dalam contoh ini berisi`Sampletext1.txt`,`Sampletext2.txt`, dan`Sampletext3.txt`. Ketika pekerjaan selesai, folder,`output`, ditempatkan di lokasi yang ditentukan oleh `--output-data-config` tag. Folder berisi`SampleText1.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 garis 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 garis 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 garis 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StartPiiEntitiesDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-pii-entities-detection-job.html)di *Referensi AWS CLI Perintah*.
### `start-sentiment-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`start-sentiment-detection-job`.
**AWS CLI**
**Untuk memulai pekerjaan analisis sentimen asinkron**
`start-sentiment-detection-job`Contoh 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 berisi`SampleMovieReview1.txt`,`SampleMovieReview2.txt`, dan`SampleMovieReview3.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 yang telah dilatih sebelumnya 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 garis indentasi agar mudah dibaca:
```
{
"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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StartSentimentDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-sentiment-detection-job.html)di *Referensi AWS CLI Perintah*.
### `start-targeted-sentiment-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`start-targeted-sentiment-detection-job`.
**AWS CLI**
**Untuk memulai pekerjaan analisis sentimen bertarget asinkron**
`start-targeted-sentiment-detection-job`Contoh 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 berisi`SampleMovieReview1.txt`,`SampleMovieReview2.txt`, dan`SampleMovieReview3.txt`. Ketika pekerjaan selesai, `output.tar.gz` ditempatkan di lokasi yang ditentukan oleh `--output-data-config` tag. `output.tar.gz`berisi file`SampleMovieReview1.txt.out`,`SampleMovieReview2.txt.out`, dan`SampleMovieReview3.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 garis 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 indentasi `SampleMovieReview2.txt.out` 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 garis 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 Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StartTargetedSentimentDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-targeted-sentiment-detection-job.html)di *Referensi AWS CLI Perintah*.
### `start-topics-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`start-topics-detection-job`.
**AWS CLI**
**Untuk memulai pekerjaan analisis deteksi topik**
`start-topics-detection-job`Contoh 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. `output`berisi 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 Topik](https://docs.aws.amazon.com/comprehend/latest/dg/topic-modeling.html) di Panduan *Pengembang Amazon Comprehend*.
+ Untuk detail API, lihat [StartTopicsDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/start-topics-detection-job.html)di *Referensi AWS CLI Perintah*.
### `stop-dominant-language-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`stop-dominant-language-detection-job`.
**AWS CLI**
**Untuk menghentikan pekerjaan deteksi bahasa dominan asinkron**
`stop-dominant-language-detection-job`Contoh berikut menghentikan pekerjaan deteksi bahasa dominan asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah `IN_PROGRESS` pekerjaan ditandai untuk pemutusan hubungan kerja dan dimasukkan ke dalam `STOP_REQUESTED` negara bagian. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam `COMPLETED` negara.
```
aws comprehend stop-dominant-language-detection-job \
--job-id 123456abcdeb0e11022f22a11EXAMPLE
```
Output:
```
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE,
"JobStatus": "STOP_REQUESTED"
}
```
*Untuk informasi selengkapnya, lihat [Analisis asinkron untuk Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StopDominantLanguageDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/stop-dominant-language-detection-job.html)di *Referensi AWS CLI Perintah*.
### `stop-entities-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`stop-entities-detection-job`.
**AWS CLI**
**Untuk menghentikan pekerjaan deteksi entitas asinkron**
`stop-entities-detection-job`Contoh berikut menghentikan pekerjaan deteksi entitas asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah `IN_PROGRESS` pekerjaan ditandai untuk pemutusan hubungan kerja dan dimasukkan ke dalam `STOP_REQUESTED` negara bagian. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam `COMPLETED` negara.
```
aws comprehend stop-entities-detection-job \
--job-id 123456abcdeb0e11022f22a11EXAMPLE
```
Output:
```
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE,
"JobStatus": "STOP_REQUESTED"
}
```
*Untuk informasi selengkapnya, lihat [Analisis asinkron untuk Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StopEntitiesDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/stop-entities-detection-job.html)di *Referensi AWS CLI Perintah*.
### `stop-events-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`stop-events-detection-job`.
**AWS CLI**
**Untuk menghentikan pekerjaan deteksi peristiwa asinkron**
`stop-events-detection-job`Contoh berikut menghentikan pekerjaan deteksi peristiwa asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah `IN_PROGRESS` pekerjaan ditandai untuk pemutusan hubungan kerja dan dimasukkan ke dalam `STOP_REQUESTED` negara bagian. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam `COMPLETED` negara.
```
aws comprehend stop-events-detection-job \
--job-id 123456abcdeb0e11022f22a11EXAMPLE
```
Output:
```
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE,
"JobStatus": "STOP_REQUESTED"
}
```
*Untuk informasi selengkapnya, lihat [Analisis asinkron untuk Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StopEventsDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/stop-events-detection-job.html)di *Referensi AWS CLI Perintah*.
### `stop-key-phrases-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`stop-key-phrases-detection-job`.
**AWS CLI**
**Untuk menghentikan pekerjaan deteksi frase kunci asinkron**
`stop-key-phrases-detection-job`Contoh berikut menghentikan pekerjaan deteksi frase kunci asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah `IN_PROGRESS` pekerjaan ditandai untuk pemutusan hubungan kerja dan dimasukkan ke dalam `STOP_REQUESTED` negara bagian. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam `COMPLETED` negara.
```
aws comprehend stop-key-phrases-detection-job \
--job-id 123456abcdeb0e11022f22a11EXAMPLE
```
Output:
```
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE,
"JobStatus": "STOP_REQUESTED"
}
```
*Untuk informasi selengkapnya, lihat [Analisis asinkron untuk Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StopKeyPhrasesDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/stop-key-phrases-detection-job.html)di *Referensi AWS CLI Perintah*.
### `stop-pii-entities-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`stop-pii-entities-detection-job`.
**AWS CLI**
**Untuk menghentikan pekerjaan deteksi entitas pii asinkron**
`stop-pii-entities-detection-job`Contoh berikut menghentikan pekerjaan deteksi entitas pii asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah `IN_PROGRESS` pekerjaan ditandai untuk pemutusan hubungan kerja dan dimasukkan ke dalam `STOP_REQUESTED` negara bagian. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam `COMPLETED` negara.
```
aws comprehend stop-pii-entities-detection-job \
--job-id 123456abcdeb0e11022f22a11EXAMPLE
```
Output:
```
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE,
"JobStatus": "STOP_REQUESTED"
}
```
*Untuk informasi selengkapnya, lihat [Analisis asinkron untuk Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StopPiiEntitiesDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/stop-pii-entities-detection-job.html)di *Referensi AWS CLI Perintah*.
### `stop-sentiment-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`stop-sentiment-detection-job`.
**AWS CLI**
**Untuk menghentikan pekerjaan deteksi sentimen asinkron**
`stop-sentiment-detection-job`Contoh berikut menghentikan pekerjaan deteksi sentimen asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah `IN_PROGRESS` pekerjaan ditandai untuk pemutusan hubungan kerja dan dimasukkan ke dalam `STOP_REQUESTED` negara bagian. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam `COMPLETED` negara.
```
aws comprehend stop-sentiment-detection-job \
--job-id 123456abcdeb0e11022f22a11EXAMPLE
```
Output:
```
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE,
"JobStatus": "STOP_REQUESTED"
}
```
*Untuk informasi selengkapnya, lihat [Analisis asinkron untuk Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StopSentimentDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/stop-sentiment-detection-job.html)di *Referensi AWS CLI Perintah*.
### `stop-targeted-sentiment-detection-job`
Contoh kode berikut menunjukkan cara menggunakan`stop-targeted-sentiment-detection-job`.
**AWS CLI**
**Untuk menghentikan pekerjaan deteksi sentimen bertarget asinkron**
`stop-targeted-sentiment-detection-job`Contoh berikut menghentikan pekerjaan deteksi sentimen bertarget asinkron yang sedang berlangsung. Jika status pekerjaan saat ini adalah `IN_PROGRESS` pekerjaan ditandai untuk pemutusan hubungan kerja dan dimasukkan ke dalam `STOP_REQUESTED` negara bagian. Jika pekerjaan selesai sebelum dapat dihentikan, itu dimasukkan ke dalam `COMPLETED` negara.
```
aws comprehend stop-targeted-sentiment-detection-job \
--job-id 123456abcdeb0e11022f22a11EXAMPLE
```
Output:
```
{
"JobId": "123456abcdeb0e11022f22a11EXAMPLE,
"JobStatus": "STOP_REQUESTED"
}
```
*Untuk informasi selengkapnya, lihat [Analisis asinkron untuk Amazon Comprehend insight di Panduan Pengembang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/api-async-insights.html).*
+ Untuk detail API, lihat [StopTargetedSentimentDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/stop-targeted-sentiment-detection-job.html)di *Referensi AWS CLI Perintah*.
### `stop-training-document-classifier`
Contoh kode berikut menunjukkan cara menggunakan`stop-training-document-classifier`.
**AWS CLI**
**Untuk menghentikan pelatihan model pengklasifikasi dokumen**
`stop-training-document-classifier`Contoh 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](https://docs.aws.amazon.com/comprehend/latest/dg/manage-models.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [StopTrainingDocumentClassifier](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/stop-training-document-classifier.html)di *Referensi AWS CLI Perintah*.
### `stop-training-entity-recognizer`
Contoh kode berikut menunjukkan cara menggunakan`stop-training-entity-recognizer`.
**AWS CLI**
**Untuk menghentikan pelatihan model pengenal entitas**
`stop-training-entity-recognizer`Contoh berikut menghentikan pelatihan model pengenal entitas saat dalam proses.
```
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](https://docs.aws.amazon.com/comprehend/latest/dg/manage-models.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [StopTrainingEntityRecognizer](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/stop-training-entity-recognizer.html)di *Referensi AWS CLI Perintah*.
### `tag-resource`
Contoh kode berikut menunjukkan cara menggunakan`tag-resource`.
**AWS CLI**
**Contoh 1: Untuk menandai sumber daya**
`tag-resource`Contoh berikut menambahkan satu tag ke sumber daya Amazon Comprehend.
```
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 [Menandai sumber daya Anda](https://docs.aws.amazon.com/comprehend/latest/dg/tagging.html) di Panduan Pengembang *Amazon Comprehend*.
**Contoh 2: Untuk menambahkan beberapa tag ke sumber daya**
`tag-resource`Contoh berikut menambahkan beberapa tag ke sumber daya Amazon Comprehend.
```
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 [Menandai sumber daya Anda](https://docs.aws.amazon.com/comprehend/latest/dg/tagging.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [TagResource](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/tag-resource.html)di *Referensi AWS CLI Perintah*.
### `untag-resource`
Contoh kode berikut menunjukkan cara menggunakan`untag-resource`.
**AWS CLI**
**Contoh 1: Untuk menghapus satu tag dari sumber daya**
`untag-resource`Contoh berikut menghapus satu tag dari sumber daya Amazon Comprehend.
```
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 [Menandai sumber daya Anda](https://docs.aws.amazon.com/comprehend/latest/dg/tagging.html) di Panduan Pengembang *Amazon Comprehend*.
**Contoh 2: Untuk menghapus beberapa tag dari sumber daya**
`untag-resource`Contoh berikut menghapus beberapa tag dari sumber daya Amazon Comprehend.
```
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 [Menandai sumber daya Anda](https://docs.aws.amazon.com/comprehend/latest/dg/tagging.html) di Panduan Pengembang *Amazon Comprehend*.
+ Untuk detail API, lihat [UntagResource](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/untag-resource.html)di *Referensi AWS CLI Perintah*.
### `update-endpoint`
Contoh kode berikut menunjukkan cara menggunakan`update-endpoint`.
**AWS CLI**
**Contoh 1: Untuk memperbarui unit inferensi titik akhir**
`update-endpoint`Contoh 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 [Mengelola titik akhir Amazon Comprehend di Panduan *Pengembang* Amazon](https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html) Comprehend.
**Contoh 2: Untuk memperbarui model aksi titik akhir**
`update-endpoint`Contoh 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 [Mengelola titik akhir Amazon Comprehend di Panduan *Pengembang* Amazon](https://docs.aws.amazon.com/comprehend/latest/dg/manage-endpoints.html) Comprehend.
+ Untuk detail API, lihat [UpdateEndpoint](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/update-endpoint.html)di *Referensi AWS CLI Perintah*.
### `update-flywheel`
Contoh kode berikut menunjukkan cara menggunakan`update-flywheel`.
**AWS CLI**
**Untuk memperbarui konfigurasi flywheel**
`update-flywheel`Contoh 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 [ikhtisar Flywheel di Panduan](https://docs.aws.amazon.com/comprehend/latest/dg/flywheels-about.html) Pengembang Amazon *Comprehend*.
+ Untuk detail API, lihat [UpdateFlywheel](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehend/update-flywheel.html)di *Referensi AWS CLI Perintah*.