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.
# Contoh kode untuk Amazon Comprehend menggunakan AWS SDKs
Contoh kode berikut menunjukkan cara menggunakan Amazon Comprehend AWS dengan kit pengembangan perangkat lunak (SDK).
*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.
*Skenario* adalah contoh kode yang menunjukkan kepada Anda bagaimana menyelesaikan tugas tertentu dengan memanggil beberapa fungsi dalam layanan atau dikombinasikan dengan yang lain Layanan AWS.
**Sumber daya lainnya**
+ Panduan Pengembang **[Amazon Comprehend - Informasi lebih lanjut tentang Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/dg/what-is.html)**.
+ Referensi API **[Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/APIReference/welcome.html)** — Detail tentang semua tindakan Amazon Comprehend yang tersedia.
+ **[AWS Pusat Pengembang](https://aws.amazon.com/developer/code-examples/?awsf.sdk-code-examples-product=product%23comprehend)** - Contoh kode yang dapat Anda filter berdasarkan kategori atau pencarian teks lengkap.
+ **[AWS Contoh SDK](https://github.com/awsdocs/aws-doc-sdk-examples)** — GitHub repo dengan kode lengkap dalam bahasa pilihan. Termasuk instruksi untuk mengatur dan menjalankan kode.
**Contents**
+ [Hal-hal mendasar](comprehend_code_examples_basics.md)
+ [Tindakan](comprehend_code_examples_actions.md)
+ [`CreateDocumentClassifier`](comprehend_example_comprehend_CreateDocumentClassifier_section.md)
+ [`DeleteDocumentClassifier`](comprehend_example_comprehend_DeleteDocumentClassifier_section.md)
+ [`DescribeDocumentClassificationJob`](comprehend_example_comprehend_DescribeDocumentClassificationJob_section.md)
+ [`DescribeDocumentClassifier`](comprehend_example_comprehend_DescribeDocumentClassifier_section.md)
+ [`DescribeTopicsDetectionJob`](comprehend_example_comprehend_DescribeTopicsDetectionJob_section.md)
+ [`DetectDominantLanguage`](comprehend_example_comprehend_DetectDominantLanguage_section.md)
+ [`DetectEntities`](comprehend_example_comprehend_DetectEntities_section.md)
+ [`DetectKeyPhrases`](comprehend_example_comprehend_DetectKeyPhrases_section.md)
+ [`DetectPiiEntities`](comprehend_example_comprehend_DetectPiiEntities_section.md)
+ [`DetectSentiment`](comprehend_example_comprehend_DetectSentiment_section.md)
+ [`DetectSyntax`](comprehend_example_comprehend_DetectSyntax_section.md)
+ [`ListDocumentClassificationJobs`](comprehend_example_comprehend_ListDocumentClassificationJobs_section.md)
+ [`ListDocumentClassifiers`](comprehend_example_comprehend_ListDocumentClassifiers_section.md)
+ [`ListTopicsDetectionJobs`](comprehend_example_comprehend_ListTopicsDetectionJobs_section.md)
+ [`StartDocumentClassificationJob`](comprehend_example_comprehend_StartDocumentClassificationJob_section.md)
+ [`StartTopicsDetectionJob`](comprehend_example_comprehend_StartTopicsDetectionJob_section.md)
+ [Skenario](comprehend_code_examples_scenarios.md)
+ [Membangun aplikasi streaming Amazon Transcribe](comprehend_example_cross_TranscriptionStreamingApp_section.md)
+ [Membangun chatbot Amazon Lex](comprehend_example_cross_LexChatbotLanguages_section.md)
+ [Buat aplikasi perpesanan](comprehend_example_cross_SQSMessageApp_section.md)
+ [Buat aplikasi untuk menganalisis umpan balik pelanggan](comprehend_example_cross_FSA_section.md)
+ [Mendeteksi elemen dokumen](comprehend_example_comprehend_Usage_DetectApis_section.md)
+ [Mendeteksi entitas dalam teks yang diekstrak dari gambar](comprehend_example_cross_TextractComprehendDetectEntities_section.md)
+ [Jalankan pekerjaan pemodelan topik pada data sampel](comprehend_example_comprehend_Usage_TopicModeler_section.md)
+ [Latih pengklasifikasi khusus dan klasifikasikan dokumen](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
# Contoh dasar untuk Amazon Comprehend menggunakan AWS SDKs
Contoh kode berikut menunjukkan cara menggunakan dasar-dasar Amazon Comprehend with. AWS SDKs
**Contents**
+ [Tindakan](comprehend_code_examples_actions.md)
+ [`CreateDocumentClassifier`](comprehend_example_comprehend_CreateDocumentClassifier_section.md)
+ [`DeleteDocumentClassifier`](comprehend_example_comprehend_DeleteDocumentClassifier_section.md)
+ [`DescribeDocumentClassificationJob`](comprehend_example_comprehend_DescribeDocumentClassificationJob_section.md)
+ [`DescribeDocumentClassifier`](comprehend_example_comprehend_DescribeDocumentClassifier_section.md)
+ [`DescribeTopicsDetectionJob`](comprehend_example_comprehend_DescribeTopicsDetectionJob_section.md)
+ [`DetectDominantLanguage`](comprehend_example_comprehend_DetectDominantLanguage_section.md)
+ [`DetectEntities`](comprehend_example_comprehend_DetectEntities_section.md)
+ [`DetectKeyPhrases`](comprehend_example_comprehend_DetectKeyPhrases_section.md)
+ [`DetectPiiEntities`](comprehend_example_comprehend_DetectPiiEntities_section.md)
+ [`DetectSentiment`](comprehend_example_comprehend_DetectSentiment_section.md)
+ [`DetectSyntax`](comprehend_example_comprehend_DetectSyntax_section.md)
+ [`ListDocumentClassificationJobs`](comprehend_example_comprehend_ListDocumentClassificationJobs_section.md)
+ [`ListDocumentClassifiers`](comprehend_example_comprehend_ListDocumentClassifiers_section.md)
+ [`ListTopicsDetectionJobs`](comprehend_example_comprehend_ListTopicsDetectionJobs_section.md)
+ [`StartDocumentClassificationJob`](comprehend_example_comprehend_StartDocumentClassificationJob_section.md)
+ [`StartTopicsDetectionJob`](comprehend_example_comprehend_StartTopicsDetectionJob_section.md)
# Tindakan untuk Amazon Comprehend menggunakan AWS SDKs
Contoh kode berikut menunjukkan cara melakukan tindakan Amazon Comprehend individual dengan. AWS SDKs Setiap contoh menyertakan tautan ke GitHub, di mana Anda dapat menemukan instruksi untuk mengatur dan menjalankan kode.
Kutipan ini memanggil Amazon Comprehend API dan merupakan kutipan kode dari program yang lebih besar yang harus dijalankan dalam konteks. Anda dapat melihat tindakan dalam konteks di[Skenario untuk Amazon Comprehend menggunakan AWS SDKs](comprehend_code_examples_scenarios.md).
Contoh berikut hanya mencakup tindakan yang paling umum digunakan. Untuk daftar lengkapnya, lihat Referensi API [Amazon Comprehend](https://docs.aws.amazon.com/comprehend/latest/APIReference/welcome.html).
**Topics**
+ [`CreateDocumentClassifier`](comprehend_example_comprehend_CreateDocumentClassifier_section.md)
+ [`DeleteDocumentClassifier`](comprehend_example_comprehend_DeleteDocumentClassifier_section.md)
+ [`DescribeDocumentClassificationJob`](comprehend_example_comprehend_DescribeDocumentClassificationJob_section.md)
+ [`DescribeDocumentClassifier`](comprehend_example_comprehend_DescribeDocumentClassifier_section.md)
+ [`DescribeTopicsDetectionJob`](comprehend_example_comprehend_DescribeTopicsDetectionJob_section.md)
+ [`DetectDominantLanguage`](comprehend_example_comprehend_DetectDominantLanguage_section.md)
+ [`DetectEntities`](comprehend_example_comprehend_DetectEntities_section.md)
+ [`DetectKeyPhrases`](comprehend_example_comprehend_DetectKeyPhrases_section.md)
+ [`DetectPiiEntities`](comprehend_example_comprehend_DetectPiiEntities_section.md)
+ [`DetectSentiment`](comprehend_example_comprehend_DetectSentiment_section.md)
+ [`DetectSyntax`](comprehend_example_comprehend_DetectSyntax_section.md)
+ [`ListDocumentClassificationJobs`](comprehend_example_comprehend_ListDocumentClassificationJobs_section.md)
+ [`ListDocumentClassifiers`](comprehend_example_comprehend_ListDocumentClassifiers_section.md)
+ [`ListTopicsDetectionJobs`](comprehend_example_comprehend_ListTopicsDetectionJobs_section.md)
+ [`StartDocumentClassificationJob`](comprehend_example_comprehend_StartDocumentClassificationJob_section.md)
+ [`StartTopicsDetectionJob`](comprehend_example_comprehend_StartTopicsDetectionJob_section.md)
# Gunakan `CreateDocumentClassifier` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`CreateDocumentClassifier`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Latih pengklasifikasi khusus dan klasifikasikan dokumen](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**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*.
------
#### [ Java ]
**SDK untuk Java 2.x**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples).
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.comprehend.ComprehendClient;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import software.amazon.awssdk.services.comprehend.model.CreateDocumentClassifierRequest;
import software.amazon.awssdk.services.comprehend.model.CreateDocumentClassifierResponse;
import software.amazon.awssdk.services.comprehend.model.DocumentClassifierInputDataConfig;
/**
* Before running this code example, you can setup the necessary resources, such
* as the CSV file and IAM Roles, by following this document:
* https://aws.amazon.com/blogs/machine-learning/building-a-custom-classifier-using-amazon-comprehend/
*
* Also, set up your development environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DocumentClassifierDemo {
public static void main(String[] args) {
final String usage = """
Usage:
Where:
dataAccessRoleArn - The ARN value of the role used for this operation.
s3Uri - The Amazon S3 bucket that contains the CSV file.
documentClassifierName - The name of the document classifier.
""";
if (args.length != 3) {
System.out.println(usage);
System.exit(1);
}
String dataAccessRoleArn = args[0];
String s3Uri = args[1];
String documentClassifierName = args[2];
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
createDocumentClassifier(comClient, dataAccessRoleArn, s3Uri, documentClassifierName);
comClient.close();
}
public static void createDocumentClassifier(ComprehendClient comClient, String dataAccessRoleArn, String s3Uri,
String documentClassifierName) {
try {
DocumentClassifierInputDataConfig config = DocumentClassifierInputDataConfig.builder()
.s3Uri(s3Uri)
.build();
CreateDocumentClassifierRequest createDocumentClassifierRequest = CreateDocumentClassifierRequest.builder()
.documentClassifierName(documentClassifierName)
.dataAccessRoleArn(dataAccessRoleArn)
.languageCode("en")
.inputDataConfig(config)
.build();
CreateDocumentClassifierResponse createDocumentClassifierResult = comClient
.createDocumentClassifier(createDocumentClassifierRequest);
String documentClassifierArn = createDocumentClassifierResult.documentClassifierArn();
System.out.println("Document Classifier ARN: " + documentClassifierArn);
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ Untuk detail API, lihat [CreateDocumentClassifier](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/CreateDocumentClassifier)di *Referensi AWS SDK for Java 2.x API*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def create(
self,
name,
language_code,
training_bucket,
training_key,
data_access_role_arn,
mode,
):
"""
Creates a custom classifier. After the classifier is created, it immediately
starts training on the data found in the specified Amazon S3 bucket. Training
can take 30 minutes or longer. The `describe_document_classifier` function
can be used to get training status and returns a status of TRAINED when the
classifier is ready to use.
:param name: The name of the classifier.
:param language_code: The language the classifier can operate on.
:param training_bucket: The Amazon S3 bucket that contains the training data.
:param training_key: The prefix used to find training data in the training
bucket. If multiple objects have the same prefix, all
of them are used.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
training bucket.
:return: The ARN of the newly created classifier.
"""
try:
response = self.comprehend_client.create_document_classifier(
DocumentClassifierName=name,
LanguageCode=language_code,
InputDataConfig={"S3Uri": f"s3://{training_bucket}/{training_key}"},
DataAccessRoleArn=data_access_role_arn,
Mode=mode.value,
)
self.classifier_arn = response["DocumentClassifierArn"]
logger.info("Started classifier creation. Arn is: %s.", self.classifier_arn)
except ClientError:
logger.exception("Couldn't create classifier %s.", name)
raise
else:
return self.classifier_arn
```
+ Untuk detail API, lihat [CreateDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/CreateDocumentClassifier)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->createdocumentclassifier(
iv_documentclassifiername = iv_classifier_name
iv_languagecode = iv_language_code
io_inputdataconfig = NEW /aws1/cl_cpddocclifierinpdat00(
iv_s3uri = iv_training_s3_uri
)
iv_dataaccessrolearn = iv_data_access_role_arn
iv_mode = iv_mode
).
MESSAGE 'Document classifier creation started.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdresrclimitexcdex.
MESSAGE 'Resource limit exceeded.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanytagsex.
MESSAGE 'Too many tags.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [CreateDocumentClassifier](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `DeleteDocumentClassifier` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`DeleteDocumentClassifier`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Latih pengklasifikasi khusus dan klasifikasikan dokumen](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**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*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def delete(self):
"""
Deletes the classifier.
"""
try:
self.comprehend_client.delete_document_classifier(
DocumentClassifierArn=self.classifier_arn
)
logger.info("Deleted classifier %s.", self.classifier_arn)
self.classifier_arn = None
except ClientError:
logger.exception("Couldn't deleted classifier %s.", self.classifier_arn)
raise
```
+ Untuk detail API, lihat [DeleteDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DeleteDocumentClassifier)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->deletedocumentclassifier(
iv_documentclassifierarn = iv_classifier_arn
).
MESSAGE 'Document classifier deleted.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdresourcenotfoundex.
MESSAGE 'Resource not found.' TYPE 'E'.
CATCH /aws1/cx_cpdresourceinuseex.
MESSAGE 'Resource in use.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [DeleteDocumentClassifier](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `DescribeDocumentClassificationJob` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`DescribeDocumentClassificationJob`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Latih pengklasifikasi khusus dan klasifikasikan dokumen](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**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*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def describe_job(self, job_id):
"""
Gets metadata about a classification job.
:param job_id: The ID of the job to look up.
:return: Metadata about the job.
"""
try:
response = self.comprehend_client.describe_document_classification_job(
JobId=job_id
)
job = response["DocumentClassificationJobProperties"]
logger.info("Got classification job %s.", job["JobName"])
except ClientError:
logger.exception("Couldn't get classification job %s.", job_id)
raise
else:
return job
```
+ Untuk detail API, lihat [DescribeDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassificationJob)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->describedocclassificationjob(
iv_jobid = iv_job_id
).
MESSAGE 'Document classification job described.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdjobnotfoundex.
MESSAGE 'Job not found.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [DescribeDocumentClassificationJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `DescribeDocumentClassifier` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`DescribeDocumentClassifier`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Latih pengklasifikasi khusus dan klasifikasikan dokumen](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**AWS CLI**
**Untuk menggambarkan pengklasifikasi dokumen**
`describe-document-classifier`Contoh berikut mendapatkan properti dari 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*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def describe(self, classifier_arn=None):
"""
Gets metadata about a custom classifier, including its current status.
:param classifier_arn: The ARN of the classifier to look up.
:return: Metadata about the classifier.
"""
if classifier_arn is not None:
self.classifier_arn = classifier_arn
try:
response = self.comprehend_client.describe_document_classifier(
DocumentClassifierArn=self.classifier_arn
)
classifier = response["DocumentClassifierProperties"]
logger.info("Got classifier %s.", self.classifier_arn)
except ClientError:
logger.exception("Couldn't get classifier %s.", self.classifier_arn)
raise
else:
return classifier
```
+ Untuk detail API, lihat [DescribeDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassifier)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->describedocumentclassifier(
iv_documentclassifierarn = iv_classifier_arn
).
MESSAGE 'Document classifier described.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdresourcenotfoundex.
MESSAGE 'Resource not found.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [DescribeDocumentClassifier](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `DescribeTopicsDetectionJob` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`DescribeTopicsDetectionJob`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Jalankan pekerjaan pemodelan topik pada data sampel](comprehend_example_comprehend_Usage_TopicModeler_section.md)
------
#### [ CLI ]
**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*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendTopicModeler:
"""Encapsulates a Comprehend topic modeler."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def describe_job(self, job_id):
"""
Gets metadata about a topic modeling job.
:param job_id: The ID of the job to look up.
:return: Metadata about the job.
"""
try:
response = self.comprehend_client.describe_topics_detection_job(
JobId=job_id
)
job = response["TopicsDetectionJobProperties"]
logger.info("Got topic detection job %s.", job_id)
except ClientError:
logger.exception("Couldn't get topic detection job %s.", job_id)
raise
else:
return job
```
+ Untuk detail API, lihat [DescribeTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeTopicsDetectionJob)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->describetopicsdetectionjob(
iv_jobid = iv_job_id
).
MESSAGE 'Topics detection job described.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdjobnotfoundex.
MESSAGE 'Job not found.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [DescribeTopicsDetectionJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `DetectDominantLanguage` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`DetectDominantLanguage`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Mendeteksi elemen dokumen](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**SDK untuk .NET**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example calls the Amazon Comprehend service to determine the
/// dominant language.
///
public static class DetectDominantLanguage
{
///
/// Calls Amazon Comprehend to determine the dominant language used in
/// the sample text.
///
public static async Task Main()
{
string text = "It is raining today in Seattle.";
var comprehendClient = new AmazonComprehendClient(Amazon.RegionEndpoint.USWest2);
Console.WriteLine("Calling DetectDominantLanguage\n");
var detectDominantLanguageRequest = new DetectDominantLanguageRequest()
{
Text = text,
};
var detectDominantLanguageResponse = await comprehendClient.DetectDominantLanguageAsync(detectDominantLanguageRequest);
foreach (var dl in detectDominantLanguageResponse.Languages)
{
Console.WriteLine($"Language Code: {dl.LanguageCode}, Score: {dl.Score}");
}
Console.WriteLine("Done");
}
}
```
+ Untuk detail API, lihat [DetectDominantLanguage](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectDominantLanguage)di *Referensi AWS SDK untuk .NET API*.
------
#### [ CLI ]
**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*.
------
#### [ Java ]
**SDK untuk Java 2.x**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples).
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.comprehend.ComprehendClient;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import software.amazon.awssdk.services.comprehend.model.DetectDominantLanguageRequest;
import software.amazon.awssdk.services.comprehend.model.DetectDominantLanguageResponse;
import software.amazon.awssdk.services.comprehend.model.DominantLanguage;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectLanguage {
public static void main(String[] args) {
// Specify French text - "It is raining today in Seattle".
String text = "Il pleut aujourd'hui à Seattle";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectDominantLanguage");
detectTheDominantLanguage(comClient, text);
comClient.close();
}
public static void detectTheDominantLanguage(ComprehendClient comClient, String text) {
try {
DetectDominantLanguageRequest request = DetectDominantLanguageRequest.builder()
.text(text)
.build();
DetectDominantLanguageResponse resp = comClient.detectDominantLanguage(request);
List allLanList = resp.languages();
for (DominantLanguage lang : allLanList) {
System.out.println("Language is " + lang.languageCode());
}
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ Untuk detail API, lihat [DetectDominantLanguage](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectDominantLanguage)di *Referensi AWS SDK for Java 2.x API*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_languages(self, text):
"""
Detects languages used in a document.
:param text: The document to inspect.
:return: The list of languages along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_dominant_language(Text=text)
languages = response["Languages"]
logger.info("Detected %s languages.", len(languages))
except ClientError:
logger.exception("Couldn't detect languages.")
raise
else:
return languages
```
+ Untuk detail API, lihat [DetectDominantLanguage](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectDominantLanguage)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->detectdominantlanguage( iv_text = iv_text ).
MESSAGE 'Languages detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [DetectDominantLanguage](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `DetectEntities` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`DetectEntities`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Mendeteksi elemen dokumen](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**SDK untuk .NET**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to use the AmazonComprehend service detect any
/// entities in submitted text.
///
public static class DetectEntities
{
///
/// The main method calls the DetectEntitiesAsync method to find any
/// entities in the sample code.
///
public static async Task Main()
{
string text = "It is raining today in Seattle";
var comprehendClient = new AmazonComprehendClient();
Console.WriteLine("Calling DetectEntities\n");
var detectEntitiesRequest = new DetectEntitiesRequest()
{
Text = text,
LanguageCode = "en",
};
var detectEntitiesResponse = await comprehendClient.DetectEntitiesAsync(detectEntitiesRequest);
foreach (var e in detectEntitiesResponse.Entities)
{
Console.WriteLine($"Text: {e.Text}, Type: {e.Type}, Score: {e.Score}, BeginOffset: {e.BeginOffset}, EndOffset: {e.EndOffset}");
}
Console.WriteLine("Done");
}
}
```
+ Untuk detail API, lihat [DetectEntities](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectEntities)di *Referensi AWS SDK untuk .NET API*.
------
#### [ CLI ]
**AWS CLI**
**Untuk mendeteksi entitas bernama dalam teks masukan**
`detect-entities`Contoh berikut menganalisis teks masukan 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*.
------
#### [ Java ]
**SDK untuk Java 2.x**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples).
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.comprehend.ComprehendClient;
import software.amazon.awssdk.services.comprehend.model.DetectEntitiesRequest;
import software.amazon.awssdk.services.comprehend.model.DetectEntitiesResponse;
import software.amazon.awssdk.services.comprehend.model.Entity;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectEntities {
public static void main(String[] args) {
String text = "Amazon.com, Inc. is located in Seattle, WA and was founded July 5th, 1994 by Jeff Bezos, allowing customers to buy everything from books to blenders. Seattle is north of Portland and south of Vancouver, BC. Other notable Seattle - based companies are Starbucks and Boeing.";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectEntities");
detectAllEntities(comClient, text);
comClient.close();
}
public static void detectAllEntities(ComprehendClient comClient, String text) {
try {
DetectEntitiesRequest detectEntitiesRequest = DetectEntitiesRequest.builder()
.text(text)
.languageCode("en")
.build();
DetectEntitiesResponse detectEntitiesResult = comClient.detectEntities(detectEntitiesRequest);
List entList = detectEntitiesResult.entities();
for (Entity entity : entList) {
System.out.println("Entity text is " + entity.text());
}
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ Untuk detail API, lihat [DetectEntities](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectEntities)di *Referensi AWS SDK for Java 2.x API*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_entities(self, text, language_code):
"""
Detects entities in a document. Entities can be things like people and places
or other common terms.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect entities.")
raise
else:
return entities
```
+ Untuk detail API, lihat [DetectEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectEntities)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->detectentities(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'Entities detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [DetectEntities](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `DetectKeyPhrases` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`DetectKeyPhrases`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Mendeteksi elemen dokumen](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**SDK untuk .NET**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to use the Amazon Comprehend service to
/// search text for key phrases.
///
public static class DetectKeyPhrase
{
///
/// This method calls the Amazon Comprehend method DetectKeyPhrasesAsync
/// to detect any key phrases in the sample text.
///
public static async Task Main()
{
string text = "It is raining today in Seattle";
var comprehendClient = new AmazonComprehendClient(Amazon.RegionEndpoint.USWest2);
// Call DetectKeyPhrases API
Console.WriteLine("Calling DetectKeyPhrases");
var detectKeyPhrasesRequest = new DetectKeyPhrasesRequest()
{
Text = text,
LanguageCode = "en",
};
var detectKeyPhrasesResponse = await comprehendClient.DetectKeyPhrasesAsync(detectKeyPhrasesRequest);
foreach (var kp in detectKeyPhrasesResponse.KeyPhrases)
{
Console.WriteLine($"Text: {kp.Text}, Score: {kp.Score}, BeginOffset: {kp.BeginOffset}, EndOffset: {kp.EndOffset}");
}
Console.WriteLine("Done");
}
}
```
+ Untuk detail API, lihat [DetectKeyPhrases](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectKeyPhrases)di *Referensi AWS SDK untuk .NET API*.
------
#### [ CLI ]
**AWS CLI**
**Untuk mendeteksi frasa kunci dalam teks input**
`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*.
------
#### [ Java ]
**SDK untuk Java 2.x**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples).
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.comprehend.ComprehendClient;
import software.amazon.awssdk.services.comprehend.model.DetectKeyPhrasesRequest;
import software.amazon.awssdk.services.comprehend.model.DetectKeyPhrasesResponse;
import software.amazon.awssdk.services.comprehend.model.KeyPhrase;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectKeyPhrases {
public static void main(String[] args) {
String text = "Amazon.com, Inc. is located in Seattle, WA and was founded July 5th, 1994 by Jeff Bezos, allowing customers to buy everything from books to blenders. Seattle is north of Portland and south of Vancouver, BC. Other notable Seattle - based companies are Starbucks and Boeing.";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectKeyPhrases");
detectAllKeyPhrases(comClient, text);
comClient.close();
}
public static void detectAllKeyPhrases(ComprehendClient comClient, String text) {
try {
DetectKeyPhrasesRequest detectKeyPhrasesRequest = DetectKeyPhrasesRequest.builder()
.text(text)
.languageCode("en")
.build();
DetectKeyPhrasesResponse detectKeyPhrasesResult = comClient.detectKeyPhrases(detectKeyPhrasesRequest);
List phraseList = detectKeyPhrasesResult.keyPhrases();
for (KeyPhrase keyPhrase : phraseList) {
System.out.println("Key phrase text is " + keyPhrase.text());
}
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ Untuk detail API, lihat [DetectKeyPhrases](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectKeyPhrases)di *Referensi AWS SDK for Java 2.x API*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_key_phrases(self, text, language_code):
"""
Detects key phrases in a document. A key phrase is typically a noun and its
modifiers.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of key phrases along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_key_phrases(
Text=text, LanguageCode=language_code
)
phrases = response["KeyPhrases"]
logger.info("Detected %s phrases.", len(phrases))
except ClientError:
logger.exception("Couldn't detect phrases.")
raise
else:
return phrases
```
+ Untuk detail API, lihat [DetectKeyPhrases](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectKeyPhrases)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->detectkeyphrases(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'Key phrases detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [DetectKeyPhrases](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `DetectPiiEntities` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`DetectPiiEntities`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Mendeteksi elemen dokumen](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**SDK untuk .NET**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to use the Amazon Comprehend service to find
/// personally identifiable information (PII) within text submitted to the
/// DetectPiiEntitiesAsync method.
///
public class DetectingPII
{
///
/// This method calls the DetectPiiEntitiesAsync method to locate any
/// personally dientifiable information within the supplied text.
///
public static async Task Main()
{
var comprehendClient = new AmazonComprehendClient();
var text = @"Hello Paul Santos. The latest statement for your
credit card account 1111-0000-1111-0000 was
mailed to 123 Any Street, Seattle, WA 98109.";
var request = new DetectPiiEntitiesRequest
{
Text = text,
LanguageCode = "EN",
};
var response = await comprehendClient.DetectPiiEntitiesAsync(request);
if (response.Entities.Count > 0)
{
foreach (var entity in response.Entities)
{
var entityValue = text.Substring(entity.BeginOffset, entity.EndOffset - entity.BeginOffset);
Console.WriteLine($"{entity.Type}: {entityValue}");
}
}
}
}
```
+ Untuk detail API, lihat [DetectPiiEntities](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectPiiEntities)di *Referensi AWS SDK untuk .NET API*.
------
#### [ CLI ]
**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*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_pii(self, text, language_code):
"""
Detects personally identifiable information (PII) in a document. PII can be
things like names, account numbers, or addresses.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of PII entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_pii_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s PII entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect PII entities.")
raise
else:
return entities
```
+ Untuk detail API, lihat [DetectPiiEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectPiiEntities)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->detectpiientities(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'PII entities detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [DetectPiiEntities](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `DetectSentiment` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`DetectSentiment`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Mendeteksi elemen dokumen](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**SDK untuk .NET**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to detect the overall sentiment of the supplied
/// text using the Amazon Comprehend service.
///
public static class DetectSentiment
{
///
/// This method calls the DetetectSentimentAsync method to analyze the
/// supplied text and determine the overal sentiment.
///
public static async Task Main()
{
string text = "It is raining today in Seattle";
var comprehendClient = new AmazonComprehendClient(Amazon.RegionEndpoint.USWest2);
// Call DetectKeyPhrases API
Console.WriteLine("Calling DetectSentiment");
var detectSentimentRequest = new DetectSentimentRequest()
{
Text = text,
LanguageCode = "en",
};
var detectSentimentResponse = await comprehendClient.DetectSentimentAsync(detectSentimentRequest);
Console.WriteLine($"Sentiment: {detectSentimentResponse.Sentiment}");
Console.WriteLine("Done");
}
}
```
+ Untuk detail API, lihat [DetectSentiment](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectSentiment)di *Referensi AWS SDK untuk .NET API*.
------
#### [ CLI ]
**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*.
------
#### [ Java ]
**SDK untuk Java 2.x**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples).
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.comprehend.ComprehendClient;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import software.amazon.awssdk.services.comprehend.model.DetectSentimentRequest;
import software.amazon.awssdk.services.comprehend.model.DetectSentimentResponse;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectSentiment {
public static void main(String[] args) {
String text = "Amazon.com, Inc. is located in Seattle, WA and was founded July 5th, 1994 by Jeff Bezos, allowing customers to buy everything from books to blenders. Seattle is north of Portland and south of Vancouver, BC. Other notable Seattle - based companies are Starbucks and Boeing.";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectSentiment");
detectSentiments(comClient, text);
comClient.close();
}
public static void detectSentiments(ComprehendClient comClient, String text) {
try {
DetectSentimentRequest detectSentimentRequest = DetectSentimentRequest.builder()
.text(text)
.languageCode("en")
.build();
DetectSentimentResponse detectSentimentResult = comClient.detectSentiment(detectSentimentRequest);
System.out.println("The Neutral value is " + detectSentimentResult.sentimentScore().neutral());
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ Untuk detail API, lihat [DetectSentiment](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectSentiment)di *Referensi AWS SDK for Java 2.x API*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_sentiment(self, text, language_code):
"""
Detects the overall sentiment expressed in a document. Sentiment can
be positive, negative, neutral, or a mixture.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The sentiments along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_sentiment(
Text=text, LanguageCode=language_code
)
logger.info("Detected primary sentiment %s.", response["Sentiment"])
except ClientError:
logger.exception("Couldn't detect sentiment.")
raise
else:
return response
```
+ Untuk detail API, lihat [DetectSentiment](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSentiment)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->detectsentiment(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'Sentiment detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [DetectSentiment](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `DetectSyntax` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`DetectSyntax`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Mendeteksi elemen dokumen](comprehend_example_comprehend_Usage_DetectApis_section.md)
------
#### [ .NET ]
**SDK untuk .NET**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example shows how to use Amazon Comprehend to detect syntax
/// elements by calling the DetectSyntaxAsync method.
///
public class DetectingSyntax
{
///
/// This method calls DetectSynaxAsync to identify the syntax elements
/// in the sample text.
///
public static async Task Main()
{
string text = "It is raining today in Seattle";
var comprehendClient = new AmazonComprehendClient();
// Call DetectSyntax API
Console.WriteLine("Calling DetectSyntaxAsync\n");
var detectSyntaxRequest = new DetectSyntaxRequest()
{
Text = text,
LanguageCode = "en",
};
DetectSyntaxResponse detectSyntaxResponse = await comprehendClient.DetectSyntaxAsync(detectSyntaxRequest);
foreach (SyntaxToken s in detectSyntaxResponse.SyntaxTokens)
{
Console.WriteLine($"Text: {s.Text}, PartOfSpeech: {s.PartOfSpeech.Tag}, BeginOffset: {s.BeginOffset}, EndOffset: {s.EndOffset}");
}
Console.WriteLine("Done");
}
}
```
+ Untuk detail API, lihat [DetectSyntax](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/DetectSyntax)di *Referensi AWS SDK untuk .NET API*.
------
#### [ CLI ]
**AWS CLI**
**Untuk mendeteksi bagian-bagian pidato dalam teks input**
`detect-syntax`Contoh berikut menganalisis sintaks teks input 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*.
------
#### [ Java ]
**SDK untuk Java 2.x**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend#code-examples).
```
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.comprehend.ComprehendClient;
import software.amazon.awssdk.services.comprehend.model.ComprehendException;
import software.amazon.awssdk.services.comprehend.model.DetectSyntaxRequest;
import software.amazon.awssdk.services.comprehend.model.DetectSyntaxResponse;
import software.amazon.awssdk.services.comprehend.model.SyntaxToken;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development
* environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectSyntax {
public static void main(String[] args) {
String text = "Amazon.com, Inc. is located in Seattle, WA and was founded July 5th, 1994 by Jeff Bezos, allowing customers to buy everything from books to blenders. Seattle is north of Portland and south of Vancouver, BC. Other notable Seattle - based companies are Starbucks and Boeing.";
Region region = Region.US_EAST_1;
ComprehendClient comClient = ComprehendClient.builder()
.region(region)
.build();
System.out.println("Calling DetectSyntax");
detectAllSyntax(comClient, text);
comClient.close();
}
public static void detectAllSyntax(ComprehendClient comClient, String text) {
try {
DetectSyntaxRequest detectSyntaxRequest = DetectSyntaxRequest.builder()
.text(text)
.languageCode("en")
.build();
DetectSyntaxResponse detectSyntaxResult = comClient.detectSyntax(detectSyntaxRequest);
List syntaxTokens = detectSyntaxResult.syntaxTokens();
for (SyntaxToken token : syntaxTokens) {
System.out.println("Language is " + token.text());
System.out.println("Part of speech is " + token.partOfSpeech().tagAsString());
}
} catch (ComprehendException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
}
```
+ Untuk detail API, lihat [DetectSyntax](https://docs.aws.amazon.com/goto/SdkForJavaV2/comprehend-2017-11-27/DetectSyntax)di *Referensi AWS SDK for Java 2.x API*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_syntax(self, text, language_code):
"""
Detects syntactical elements of a document. Syntax tokens are portions of
text along with their use as parts of speech, such as nouns, verbs, and
interjections.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of syntax tokens along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_syntax(
Text=text, LanguageCode=language_code
)
tokens = response["SyntaxTokens"]
logger.info("Detected %s syntax tokens.", len(tokens))
except ClientError:
logger.exception("Couldn't detect syntax.")
raise
else:
return tokens
```
+ Untuk detail API, lihat [DetectSyntax](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSyntax)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->detectsyntax(
iv_text = iv_text
iv_languagecode = iv_language_code
).
MESSAGE 'Syntax tokens detected.' TYPE 'I'.
CATCH /aws1/cx_cpdtextsizelmtexcdex.
MESSAGE 'Text size exceeds limit.' TYPE 'E'.
CATCH /aws1/cx_cpdunsuppedlanguageex.
MESSAGE 'Unsupported language.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [DetectSyntax](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `ListDocumentClassificationJobs` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`ListDocumentClassificationJobs`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Latih pengklasifikasi khusus dan klasifikasikan dokumen](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**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*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def list_jobs(self):
"""
Lists the classification jobs for the current account.
:return: The list of jobs.
"""
try:
response = self.comprehend_client.list_document_classification_jobs()
jobs = response["DocumentClassificationJobPropertiesList"]
logger.info("Got %s document classification jobs.", len(jobs))
except ClientError:
logger.exception(
"Couldn't get document classification jobs.",
)
raise
else:
return jobs
```
+ Untuk detail API, lihat [ListDocumentClassificationJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassificationJobs)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->listdocclassificationjobs( ).
MESSAGE 'Document classification jobs listed.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidfilterex.
MESSAGE 'Invalid filter.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [ListDocumentClassificationJobs](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `ListDocumentClassifiers` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`ListDocumentClassifiers`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Latih pengklasifikasi khusus dan klasifikasikan dokumen](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**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*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def list(self):
"""
Lists custom classifiers for the current account.
:return: The list of classifiers.
"""
try:
response = self.comprehend_client.list_document_classifiers()
classifiers = response["DocumentClassifierPropertiesList"]
logger.info("Got %s classifiers.", len(classifiers))
except ClientError:
logger.exception(
"Couldn't get classifiers.",
)
raise
else:
return classifiers
```
+ Untuk detail API, lihat [ListDocumentClassifiers](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassifiers)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->listdocumentclassifiers( ).
MESSAGE 'Document classifiers listed.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidfilterex.
MESSAGE 'Invalid filter.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [ListDocumentClassifiers](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `ListTopicsDetectionJobs` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`ListTopicsDetectionJobs`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Jalankan pekerjaan pemodelan topik pada data sampel](comprehend_example_comprehend_Usage_TopicModeler_section.md)
------
#### [ CLI ]
**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*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendTopicModeler:
"""Encapsulates a Comprehend topic modeler."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def list_jobs(self):
"""
Lists topic modeling jobs for the current account.
:return: The list of jobs.
"""
try:
response = self.comprehend_client.list_topics_detection_jobs()
jobs = response["TopicsDetectionJobPropertiesList"]
logger.info("Got %s topic detection jobs.", len(jobs))
except ClientError:
logger.exception("Couldn't get topic detection jobs.")
raise
else:
return jobs
```
+ Untuk detail API, lihat [ListTopicsDetectionJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListTopicsDetectionJobs)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->listtopicsdetectionjobs( ).
MESSAGE 'Topics detection jobs listed.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdinvalidfilterex.
MESSAGE 'Invalid filter.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [ListTopicsDetectionJobs](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `StartDocumentClassificationJob` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`StartDocumentClassificationJob`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Latih pengklasifikasi khusus dan klasifikasikan dokumen](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
------
#### [ CLI ]
**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``predictions.jsonl`berisi daftar klasifikasi setiap dokumen. Output Json dicetak pada satu baris per file, tetapi diformat di sini untuk keterbacaan.
```
aws comprehend start-document-classification-job \
--job-name exampleclassificationjob \
--input-data-config "S3Uri=s3://amzn-s3-demo-bucket-INPUT/jobdata/" \
--output-data-config "S3Uri=s3://amzn-s3-demo-destination-bucket/testfolder/" \
--data-access-role-arn arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role \
--document-classifier-arn arn:aws:comprehend:us-west-2:111122223333:document-classifier/mymodel/version/12
```
Isi dari `SampleSMStext1.txt`:
```
"CONGRATULATIONS! TXT 2155550100 to win $5000"
```
Isi dari `SampleSMStext2.txt`:
```
"Hi, when do you want me to pick you up from practice?"
```
Isi dari `SampleSMStext3.txt`:
```
"Plz send bank account # to 2155550100 to claim prize!!"
```
Output:
```
{
"JobId": "e758dd56b824aa717ceab551fEXAMPLE",
"JobArn": "arn:aws:comprehend:us-west-2:111122223333:document-classification-job/e758dd56b824aa717ceab551fEXAMPLE",
"JobStatus": "SUBMITTED"
}
```
Isi dari `predictions.jsonl`:
```
{"File": "SampleSMSText1.txt", "Line": "0", "Classes": [{"Name": "spam", "Score": 0.9999}, {"Name": "ham", "Score": 0.0001}]}
{"File": "SampleSMStext2.txt", "Line": "0", "Classes": [{"Name": "ham", "Score": 0.9994}, {"Name": "spam", "Score": 0.0006}]}
{"File": "SampleSMSText3.txt", "Line": "0", "Classes": [{"Name": "spam", "Score": 0.9999}, {"Name": "ham", "Score": 0.0001}]}
```
Untuk informasi selengkapnya, lihat [Klasifikasi 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*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def start_job(
self,
job_name,
input_bucket,
input_key,
input_format,
output_bucket,
output_key,
data_access_role_arn,
):
"""
Starts a classification job. The classifier must be trained or the job
will fail. Input is read from the specified Amazon S3 input bucket and
written to the specified output bucket. Output data is stored in a tar
archive compressed in gzip format. The job runs asynchronously, so you can
call `describe_document_classification_job` to get job status until it
returns a status of SUCCEEDED.
:param job_name: The name of the job.
:param input_bucket: The Amazon S3 bucket that contains input data.
:param input_key: The prefix used to find input data in the input
bucket. If multiple objects have the same prefix, all
of them are used.
:param input_format: The format of the input data, either one document per
file or one document per line.
:param output_bucket: The Amazon S3 bucket where output data is written.
:param output_key: The prefix prepended to the output data.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
input bucket and write to the output bucket.
:return: Information about the job, including the job ID.
"""
try:
response = self.comprehend_client.start_document_classification_job(
DocumentClassifierArn=self.classifier_arn,
JobName=job_name,
InputDataConfig={
"S3Uri": f"s3://{input_bucket}/{input_key}",
"InputFormat": input_format.value,
},
OutputDataConfig={"S3Uri": f"s3://{output_bucket}/{output_key}"},
DataAccessRoleArn=data_access_role_arn,
)
logger.info(
"Document classification job %s is %s.", job_name, response["JobStatus"]
)
except ClientError:
logger.exception("Couldn't start classification job %s.", job_name)
raise
else:
return response
```
+ Untuk detail API, lihat [StartDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartDocumentClassificationJob)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->startdocclassificationjob(
iv_jobname = iv_job_name
iv_documentclassifierarn = iv_classifier_arn
io_inputdataconfig = NEW /aws1/cl_cpdinputdataconfig(
iv_s3uri = iv_input_s3_uri
iv_inputformat = iv_input_format
)
io_outputdataconfig = NEW /aws1/cl_cpdoutputdataconfig(
iv_s3uri = iv_output_s3_uri
)
iv_dataaccessrolearn = iv_data_access_role_arn
).
MESSAGE 'Document classification job started.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdresourcenotfoundex.
MESSAGE 'Resource not found.' TYPE 'E'.
CATCH /aws1/cx_cpdresourceunavailex.
MESSAGE 'Resource unavailable.' TYPE 'E'.
CATCH /aws1/cx_cpdkmskeyvalidationex.
MESSAGE 'KMS key validation error.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanytagsex.
MESSAGE 'Too many tags.' TYPE 'E'.
CATCH /aws1/cx_cpdresrclimitexcdex.
MESSAGE 'Resource limit exceeded.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [StartDocumentClassificationJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Gunakan `StartTopicsDetectionJob` dengan AWS SDK atau CLI
Contoh kode berikut menunjukkan cara menggunakan`StartTopicsDetectionJob`.
Contoh tindakan adalah kutipan kode dari program yang lebih besar dan harus dijalankan dalam konteks. Anda dapat melihat tindakan ini dalam konteks dalam contoh kode berikut:
+ [Jalankan pekerjaan pemodelan topik pada data sampel](comprehend_example_comprehend_Usage_TopicModeler_section.md)
------
#### [ .NET ]
**SDK untuk .NET**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/Comprehend/#code-examples).
```
using System;
using System.Threading.Tasks;
using Amazon.Comprehend;
using Amazon.Comprehend.Model;
///
/// This example scans the documents in an Amazon Simple Storage Service
/// (Amazon S3) bucket and analyzes it for topics. The results are stored
/// in another bucket and then the resulting job properties are displayed
/// on the screen. This example was created using the AWS SDK for .NEt
/// version 3.7 and .NET Core version 5.0.
///
public static class TopicModeling
{
///
/// This methos calls a topic detection job by calling the Amazon
/// Comprehend StartTopicsDetectionJobRequest.
///
public static async Task Main()
{
var comprehendClient = new AmazonComprehendClient();
string inputS3Uri = "s3://input bucket/input path";
InputFormat inputDocFormat = InputFormat.ONE_DOC_PER_FILE;
string outputS3Uri = "s3://output bucket/output path";
string dataAccessRoleArn = "arn:aws:iam::account ID:role/data access role";
int numberOfTopics = 10;
var startTopicsDetectionJobRequest = new StartTopicsDetectionJobRequest()
{
InputDataConfig = new InputDataConfig()
{
S3Uri = inputS3Uri,
InputFormat = inputDocFormat,
},
OutputDataConfig = new OutputDataConfig()
{
S3Uri = outputS3Uri,
},
DataAccessRoleArn = dataAccessRoleArn,
NumberOfTopics = numberOfTopics,
};
var startTopicsDetectionJobResponse = await comprehendClient.StartTopicsDetectionJobAsync(startTopicsDetectionJobRequest);
var jobId = startTopicsDetectionJobResponse.JobId;
Console.WriteLine("JobId: " + jobId);
var describeTopicsDetectionJobRequest = new DescribeTopicsDetectionJobRequest()
{
JobId = jobId,
};
var describeTopicsDetectionJobResponse = await comprehendClient.DescribeTopicsDetectionJobAsync(describeTopicsDetectionJobRequest);
PrintJobProperties(describeTopicsDetectionJobResponse.TopicsDetectionJobProperties);
var listTopicsDetectionJobsResponse = await comprehendClient.ListTopicsDetectionJobsAsync(new ListTopicsDetectionJobsRequest());
foreach (var props in listTopicsDetectionJobsResponse.TopicsDetectionJobPropertiesList)
{
PrintJobProperties(props);
}
}
///
/// This method is a helper method that displays the job properties
/// from the call to StartTopicsDetectionJobRequest.
///
/// A list of properties from the call to
/// StartTopicsDetectionJobRequest.
private static void PrintJobProperties(TopicsDetectionJobProperties props)
{
Console.WriteLine($"JobId: {props.JobId}, JobName: {props.JobName}, JobStatus: {props.JobStatus}");
Console.WriteLine($"NumberOfTopics: {props.NumberOfTopics}\nInputS3Uri: {props.InputDataConfig.S3Uri}");
Console.WriteLine($"InputFormat: {props.InputDataConfig.InputFormat}, OutputS3Uri: {props.OutputDataConfig.S3Uri}");
}
}
```
+ Untuk detail API, lihat [StartTopicsDetectionJob](https://docs.aws.amazon.com/goto/DotNetSDKV3/comprehend-2017-11-27/StartTopicsDetectionJob)di *Referensi AWS SDK untuk .NET API*.
------
#### [ CLI ]
**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*.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
```
class ComprehendTopicModeler:
"""Encapsulates a Comprehend topic modeler."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def start_job(
self,
job_name,
input_bucket,
input_key,
input_format,
output_bucket,
output_key,
data_access_role_arn,
):
"""
Starts a topic modeling job. Input is read from the specified Amazon S3
input bucket and written to the specified output bucket. Output data is stored
in a tar archive compressed in gzip format. The job runs asynchronously, so you
can call `describe_topics_detection_job` to get job status until it
returns a status of SUCCEEDED.
:param job_name: The name of the job.
:param input_bucket: An Amazon S3 bucket that contains job input.
:param input_key: The prefix used to find input data in the input
bucket. If multiple objects have the same prefix, all
of them are used.
:param input_format: The format of the input data, either one document per
file or one document per line.
:param output_bucket: The Amazon S3 bucket where output data is written.
:param output_key: The prefix prepended to the output data.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
input bucket and write to the output bucket.
:return: Information about the job, including the job ID.
"""
try:
response = self.comprehend_client.start_topics_detection_job(
JobName=job_name,
DataAccessRoleArn=data_access_role_arn,
InputDataConfig={
"S3Uri": f"s3://{input_bucket}/{input_key}",
"InputFormat": input_format.value,
},
OutputDataConfig={"S3Uri": f"s3://{output_bucket}/{output_key}"},
)
logger.info("Started topic modeling job %s.", response["JobId"])
except ClientError:
logger.exception("Couldn't start topic modeling job.")
raise
else:
return response
```
+ Untuk detail API, lihat [StartTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartTopicsDetectionJob)di *AWS SDK for Python (Boto3) Referensi* API.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/cpd#code-examples).
```
TRY.
oo_result = lo_cpd->starttopicsdetectionjob(
iv_jobname = iv_job_name
io_inputdataconfig = NEW /aws1/cl_cpdinputdataconfig(
iv_s3uri = iv_input_s3_uri
iv_inputformat = iv_input_format
)
io_outputdataconfig = NEW /aws1/cl_cpdoutputdataconfig(
iv_s3uri = iv_output_s3_uri
)
iv_dataaccessrolearn = iv_data_access_role_arn
).
MESSAGE 'Topics detection job started.' TYPE 'I'.
CATCH /aws1/cx_cpdinvalidrequestex.
MESSAGE 'Invalid request.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanyrequestsex.
MESSAGE 'Too many requests.' TYPE 'E'.
CATCH /aws1/cx_cpdkmskeyvalidationex.
MESSAGE 'KMS key validation error.' TYPE 'E'.
CATCH /aws1/cx_cpdtoomanytagsex.
MESSAGE 'Too many tags.' TYPE 'E'.
CATCH /aws1/cx_cpdresrclimitexcdex.
MESSAGE 'Resource limit exceeded.' TYPE 'E'.
CATCH /aws1/cx_cpdinternalserverex.
MESSAGE 'Internal server error occurred.' TYPE 'E'.
ENDTRY.
```
+ Untuk detail API, lihat [StartTopicsDetectionJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)di *AWS SDK untuk referensi SAP ABAP* API.
------
# Skenario untuk Amazon Comprehend menggunakan AWS SDKs
Contoh kode berikut menunjukkan cara menerapkan skenario umum di Amazon Comprehend with. AWS SDKs Skenario ini menunjukkan kepada Anda cara menyelesaikan tugas tertentu dengan memanggil beberapa fungsi dalam Amazon Comprehend atau digabungkan dengan yang lain. Layanan AWS Setiap skenario menyertakan tautan ke kode sumber lengkap, di mana Anda dapat menemukan instruksi tentang cara mengatur dan menjalankan kode.
Skenario menargetkan tingkat pengalaman menengah untuk membantu Anda memahami tindakan layanan dalam konteks.
**Topics**
+ [Membangun aplikasi streaming Amazon Transcribe](comprehend_example_cross_TranscriptionStreamingApp_section.md)
+ [Membangun chatbot Amazon Lex](comprehend_example_cross_LexChatbotLanguages_section.md)
+ [Buat aplikasi perpesanan](comprehend_example_cross_SQSMessageApp_section.md)
+ [Buat aplikasi untuk menganalisis umpan balik pelanggan](comprehend_example_cross_FSA_section.md)
+ [Mendeteksi elemen dokumen](comprehend_example_comprehend_Usage_DetectApis_section.md)
+ [Mendeteksi entitas dalam teks yang diekstrak dari gambar](comprehend_example_cross_TextractComprehendDetectEntities_section.md)
+ [Jalankan pekerjaan pemodelan topik pada data sampel](comprehend_example_comprehend_Usage_TopicModeler_section.md)
+ [Latih pengklasifikasi khusus dan klasifikasikan dokumen](comprehend_example_comprehend_Usage_ComprehendClassifier_section.md)
# Membangun aplikasi streaming Amazon Transcribe
Contoh kode berikut menunjukkan cara membuat aplikasi yang merekam, mentranskripsikan, dan menerjemahkan audio langsung secara real-time, dan mengirim email hasilnya.
------
#### [ JavaScript ]
**SDK untuk JavaScript (v3)**
Menunjukkan cara menggunakan Amazon Transcribe untuk membuat aplikasi yang merekam, menyalin, dan menerjemahkan audio langsung secara real-time, dan mengirim email hasilnya menggunakan Amazon Simple Email Service (Amazon SES).
Untuk kode sumber lengkap dan instruksi tentang cara mengatur dan menjalankan, lihat contoh lengkapnya di [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/transcribe-streaming-app).
**Layanan yang digunakan dalam contoh ini**
+ Amazon Comprehend
+ Amazon SES
+ Amazon Transcribe
+ Amazon Translate
------
# Buat chatbot Amazon Lex untuk melibatkan pengunjung situs web Anda
Contoh kode berikut menunjukkan cara membuat chatbot untuk melibatkan pengunjung situs web Anda.
------
#### [ Java ]
**SDK untuk Java 2.x**
Menunjukkan cara menggunakan Amazon Lex API untuk membuat Chatbot dalam aplikasi web untuk melibatkan pengunjung situs web Anda.
Untuk kode sumber lengkap dan instruksi tentang cara mengatur dan menjalankan, lihat contoh lengkapnya di [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_lex_chatbot).
**Layanan yang digunakan dalam contoh ini**
+ Amazon Comprehend
+ Amazon Lex
+ Amazon Translate
------
#### [ JavaScript ]
**SDK untuk JavaScript (v3)**
Menunjukkan cara menggunakan Amazon Lex API untuk membuat Chatbot dalam aplikasi web untuk melibatkan pengunjung situs web Anda.
Untuk kode sumber lengkap dan petunjuk tentang cara mengatur dan menjalankan, lihat contoh lengkap [Membangun chatbot Amazon Lex](https://docs.aws.amazon.com/sdk-for-javascript/v3/developer-guide/lex-bot-example.html) di panduan AWS SDK untuk JavaScript pengembang.
**Layanan yang digunakan dalam contoh ini**
+ Amazon Comprehend
+ Amazon Lex
+ Amazon Translate
------
# Buat aplikasi web yang mengirim dan mengambil pesan dengan menggunakan Amazon SQS
Contoh kode berikut menunjukkan cara membuat aplikasi perpesanan dengan menggunakan Amazon SQS.
------
#### [ Java ]
**SDK untuk Java 2.x**
Menunjukkan cara menggunakan Amazon SQS API untuk mengembangkan Spring REST API yang mengirim dan mengambil pesan.
Untuk kode sumber lengkap dan instruksi tentang cara mengatur dan menjalankan, lihat contoh lengkapnya di [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_message_application).
**Layanan yang digunakan dalam contoh ini**
+ Amazon Comprehend
+ Amazon SQS
------
#### [ Kotlin ]
**SDK untuk Kotlin**
Menunjukkan cara menggunakan Amazon SQS API untuk mengembangkan Spring REST API yang mengirim dan mengambil pesan.
Untuk kode sumber lengkap dan instruksi tentang cara mengatur dan menjalankan, lihat contoh lengkapnya di [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/creating_message_application).
**Layanan yang digunakan dalam contoh ini**
+ Amazon Comprehend
+ Amazon SQS
------
# Buat aplikasi yang menganalisis umpan balik pelanggan dan mensintesis audio
Contoh kode berikut menunjukkan cara membuat aplikasi yang menganalisis kartu komentar pelanggan, menerjemahkannya dari bahasa aslinya, menentukan sentimen mereka, dan menghasilkan file audio dari teks yang diterjemahkan.
------
#### [ .NET ]
**SDK untuk .NET**
Aplikasi contoh ini menganalisis dan menyimpan kartu umpan balik pelanggan. Secara khusus, ini memenuhi kebutuhan hotel fiktif di New York City. Hotel menerima umpan balik dari para tamu dalam berbagai bahasa dalam bentuk kartu komentar fisik. Umpan balik itu diunggah ke aplikasi melalui klien web. Setelah gambar kartu komentar diunggah, langkah-langkah berikut terjadi:
+ Teks diekstraksi dari gambar menggunakan Amazon Textract.
+ Amazon Comprehend menentukan sentimen teks yang diekstraksi dan bahasanya.
+ Teks yang diekstraksi diterjemahkan ke bahasa Inggris menggunakan Amazon Translate.
+ Amazon Polly mensintesis file audio dari teks yang diekstraksi.
Aplikasi lengkap dapat digunakan dengan. AWS CDK Untuk kode sumber dan petunjuk penerapan, lihat proyek di [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/cross-service/FeedbackSentimentAnalyzer).
**Layanan yang digunakan dalam contoh ini**
+ Amazon Comprehend
+ Lambda
+ Amazon Polly
+ Amazon Textract
+ Amazon Translate
------
#### [ Java ]
**SDK untuk Java 2.x**
Aplikasi contoh ini menganalisis dan menyimpan kartu umpan balik pelanggan. Secara khusus, ini memenuhi kebutuhan hotel fiktif di New York City. Hotel menerima umpan balik dari para tamu dalam berbagai bahasa dalam bentuk kartu komentar fisik. Umpan balik itu diunggah ke aplikasi melalui klien web. Setelah gambar kartu komentar diunggah, langkah-langkah berikut terjadi:
+ Teks diekstraksi dari gambar menggunakan Amazon Textract.
+ Amazon Comprehend menentukan sentimen teks yang diekstraksi dan bahasanya.
+ Teks yang diekstraksi diterjemahkan ke bahasa Inggris menggunakan Amazon Translate.
+ Amazon Polly mensintesis file audio dari teks yang diekstraksi.
Aplikasi lengkap dapat digunakan dengan. AWS CDK Untuk kode sumber dan petunjuk penerapan, lihat proyek di [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/creating_fsa_app).
**Layanan yang digunakan dalam contoh ini**
+ Amazon Comprehend
+ Lambda
+ Amazon Polly
+ Amazon Textract
+ Amazon Translate
------
#### [ JavaScript ]
**SDK untuk JavaScript (v3)**
Aplikasi contoh ini menganalisis dan menyimpan kartu umpan balik pelanggan. Secara khusus, ini memenuhi kebutuhan hotel fiktif di New York City. Hotel menerima umpan balik dari para tamu dalam berbagai bahasa dalam bentuk kartu komentar fisik. Umpan balik itu diunggah ke aplikasi melalui klien web. Setelah gambar kartu komentar diunggah, langkah-langkah berikut terjadi:
+ Teks diekstraksi dari gambar menggunakan Amazon Textract.
+ Amazon Comprehend menentukan sentimen teks yang diekstraksi dan bahasanya.
+ Teks yang diekstraksi diterjemahkan ke bahasa Inggris menggunakan Amazon Translate.
+ Amazon Polly mensintesis file audio dari teks yang diekstraksi.
Aplikasi lengkap dapat digunakan dengan. AWS CDK Untuk kode sumber dan petunjuk penerapan, lihat proyek di [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/cross-services/feedback-sentiment-analyzer). Kutipan berikut menunjukkan bagaimana yang AWS SDK untuk JavaScript digunakan di dalam fungsi Lambda.
```
import {
ComprehendClient,
DetectDominantLanguageCommand,
DetectSentimentCommand,
} from "@aws-sdk/client-comprehend";
/**
* Determine the language and sentiment of the extracted text.
*
* @param {{ source_text: string}} extractTextOutput
*/
export const handler = async (extractTextOutput) => {
const comprehendClient = new ComprehendClient({});
const detectDominantLanguageCommand = new DetectDominantLanguageCommand({
Text: extractTextOutput.source_text,
});
// The source language is required for sentiment analysis and
// translation in the next step.
const { Languages } = await comprehendClient.send(
detectDominantLanguageCommand,
);
const languageCode = Languages[0].LanguageCode;
const detectSentimentCommand = new DetectSentimentCommand({
Text: extractTextOutput.source_text,
LanguageCode: languageCode,
});
const { Sentiment } = await comprehendClient.send(detectSentimentCommand);
return {
sentiment: Sentiment,
language_code: languageCode,
};
};
```
```
import {
DetectDocumentTextCommand,
TextractClient,
} from "@aws-sdk/client-textract";
/**
* Fetch the S3 object from the event and analyze it using Amazon Textract.
*
* @param {import("@types/aws-lambda").EventBridgeEvent<"Object Created">} eventBridgeS3Event
*/
export const handler = async (eventBridgeS3Event) => {
const textractClient = new TextractClient();
const detectDocumentTextCommand = new DetectDocumentTextCommand({
Document: {
S3Object: {
Bucket: eventBridgeS3Event.bucket,
Name: eventBridgeS3Event.object,
},
},
});
// Textract returns a list of blocks. A block can be a line, a page, word, etc.
// Each block also contains geometry of the detected text.
// For more information on the Block type, see https://docs.aws.amazon.com/textract/latest/dg/API_Block.html.
const { Blocks } = await textractClient.send(detectDocumentTextCommand);
// For the purpose of this example, we are only interested in words.
const extractedWords = Blocks.filter((b) => b.BlockType === "WORD").map(
(b) => b.Text,
);
return extractedWords.join(" ");
};
```
```
import { PollyClient, SynthesizeSpeechCommand } from "@aws-sdk/client-polly";
import { S3Client } from "@aws-sdk/client-s3";
import { Upload } from "@aws-sdk/lib-storage";
/**
* Synthesize an audio file from text.
*
* @param {{ bucket: string, translated_text: string, object: string}} sourceDestinationConfig
*/
export const handler = async (sourceDestinationConfig) => {
const pollyClient = new PollyClient({});
const synthesizeSpeechCommand = new SynthesizeSpeechCommand({
Engine: "neural",
Text: sourceDestinationConfig.translated_text,
VoiceId: "Ruth",
OutputFormat: "mp3",
});
const { AudioStream } = await pollyClient.send(synthesizeSpeechCommand);
const audioKey = `${sourceDestinationConfig.object}.mp3`;
// Store the audio file in S3.
const s3Client = new S3Client();
const upload = new Upload({
client: s3Client,
params: {
Bucket: sourceDestinationConfig.bucket,
Key: audioKey,
Body: AudioStream,
ContentType: "audio/mp3",
},
});
await upload.done();
return audioKey;
};
```
```
import {
TranslateClient,
TranslateTextCommand,
} from "@aws-sdk/client-translate";
/**
* Translate the extracted text to English.
*
* @param {{ extracted_text: string, source_language_code: string}} textAndSourceLanguage
*/
export const handler = async (textAndSourceLanguage) => {
const translateClient = new TranslateClient({});
const translateCommand = new TranslateTextCommand({
SourceLanguageCode: textAndSourceLanguage.source_language_code,
TargetLanguageCode: "en",
Text: textAndSourceLanguage.extracted_text,
});
const { TranslatedText } = await translateClient.send(translateCommand);
return { translated_text: TranslatedText };
};
```
**Layanan yang digunakan dalam contoh ini**
+ Amazon Comprehend
+ Lambda
+ Amazon Polly
+ Amazon Textract
+ Amazon Translate
------
#### [ Ruby ]
**SDK untuk Ruby**
Aplikasi contoh ini menganalisis dan menyimpan kartu umpan balik pelanggan. Secara khusus, ini memenuhi kebutuhan hotel fiktif di New York City. Hotel menerima umpan balik dari para tamu dalam berbagai bahasa dalam bentuk kartu komentar fisik. Umpan balik itu diunggah ke aplikasi melalui klien web. Setelah gambar kartu komentar diunggah, langkah-langkah berikut terjadi:
+ Teks diekstraksi dari gambar menggunakan Amazon Textract.
+ Amazon Comprehend menentukan sentimen teks yang diekstraksi dan bahasanya.
+ Teks yang diekstraksi diterjemahkan ke bahasa Inggris menggunakan Amazon Translate.
+ Amazon Polly mensintesis file audio dari teks yang diekstraksi.
Aplikasi lengkap dapat digunakan dengan. AWS CDK Untuk kode sumber dan petunjuk penerapan, lihat proyek di [ GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/ruby/cross_service_examples/feedback_sentiment_analyzer).
**Layanan yang digunakan dalam contoh ini**
+ Amazon Comprehend
+ Lambda
+ Amazon Polly
+ Amazon Textract
+ Amazon Translate
------
# Mendeteksi elemen dokumen dengan Amazon Comprehend dan SDK AWS
Contoh kode berikut ini menunjukkan cara untuk melakukan:
+ Mendeteksi bahasa, entitas, dan frasa kunci dalam dokumen.
+ Mendeteksi informasi identitas pribadi (PII) dalam dokumen.
+ Mendeteksi sentimen dokumen.
+ Mendeteksi elemen sintaks dalam dokumen.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
Buat kelas yang membungkus tindakan Amazon Comprehend.
```
import logging
from pprint import pprint
import boto3
from botocore.exceptions import ClientError
logger = logging.getLogger(__name__)
class ComprehendDetect:
"""Encapsulates Comprehend detection functions."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def detect_languages(self, text):
"""
Detects languages used in a document.
:param text: The document to inspect.
:return: The list of languages along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_dominant_language(Text=text)
languages = response["Languages"]
logger.info("Detected %s languages.", len(languages))
except ClientError:
logger.exception("Couldn't detect languages.")
raise
else:
return languages
def detect_entities(self, text, language_code):
"""
Detects entities in a document. Entities can be things like people and places
or other common terms.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect entities.")
raise
else:
return entities
def detect_key_phrases(self, text, language_code):
"""
Detects key phrases in a document. A key phrase is typically a noun and its
modifiers.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of key phrases along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_key_phrases(
Text=text, LanguageCode=language_code
)
phrases = response["KeyPhrases"]
logger.info("Detected %s phrases.", len(phrases))
except ClientError:
logger.exception("Couldn't detect phrases.")
raise
else:
return phrases
def detect_pii(self, text, language_code):
"""
Detects personally identifiable information (PII) in a document. PII can be
things like names, account numbers, or addresses.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of PII entities along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_pii_entities(
Text=text, LanguageCode=language_code
)
entities = response["Entities"]
logger.info("Detected %s PII entities.", len(entities))
except ClientError:
logger.exception("Couldn't detect PII entities.")
raise
else:
return entities
def detect_sentiment(self, text, language_code):
"""
Detects the overall sentiment expressed in a document. Sentiment can
be positive, negative, neutral, or a mixture.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The sentiments along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_sentiment(
Text=text, LanguageCode=language_code
)
logger.info("Detected primary sentiment %s.", response["Sentiment"])
except ClientError:
logger.exception("Couldn't detect sentiment.")
raise
else:
return response
def detect_syntax(self, text, language_code):
"""
Detects syntactical elements of a document. Syntax tokens are portions of
text along with their use as parts of speech, such as nouns, verbs, and
interjections.
:param text: The document to inspect.
:param language_code: The language of the document.
:return: The list of syntax tokens along with their confidence scores.
"""
try:
response = self.comprehend_client.detect_syntax(
Text=text, LanguageCode=language_code
)
tokens = response["SyntaxTokens"]
logger.info("Detected %s syntax tokens.", len(tokens))
except ClientError:
logger.exception("Couldn't detect syntax.")
raise
else:
return tokens
```
Fungsi panggilan pada kelas pembungkus untuk mendeteksi entitas, frasa, dan lainnya dalam dokumen.
```
def usage_demo():
print("-" * 88)
print("Welcome to the Amazon Comprehend detection demo!")
print("-" * 88)
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
comp_detect = ComprehendDetect(boto3.client("comprehend"))
with open("detect_sample.txt") as sample_file:
sample_text = sample_file.read()
demo_size = 3
print("Sample text used for this demo:")
print("-" * 88)
print(sample_text)
print("-" * 88)
print("Detecting languages.")
languages = comp_detect.detect_languages(sample_text)
pprint(languages)
lang_code = languages[0]["LanguageCode"]
print("Detecting entities.")
entities = comp_detect.detect_entities(sample_text, lang_code)
print(f"The first {demo_size} are:")
pprint(entities[:demo_size])
print("Detecting key phrases.")
phrases = comp_detect.detect_key_phrases(sample_text, lang_code)
print(f"The first {demo_size} are:")
pprint(phrases[:demo_size])
print("Detecting personally identifiable information (PII).")
pii_entities = comp_detect.detect_pii(sample_text, lang_code)
print(f"The first {demo_size} are:")
pprint(pii_entities[:demo_size])
print("Detecting sentiment.")
sentiment = comp_detect.detect_sentiment(sample_text, lang_code)
print(f"Sentiment: {sentiment['Sentiment']}")
print("SentimentScore:")
pprint(sentiment["SentimentScore"])
print("Detecting syntax elements.")
syntax_tokens = comp_detect.detect_syntax(sample_text, lang_code)
print(f"The first {demo_size} are:")
pprint(syntax_tokens[:demo_size])
print("Thanks for watching!")
print("-" * 88)
```
+ Untuk detail API, lihat topik berikut di *Referensi API AWS SDK untuk Python (Boto3)*.
+ [DetectDominantLanguage](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectDominantLanguage)
+ [DetectEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectEntities)
+ [DetectKeyPhrases](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectKeyPhrases)
+ [DetectPiiEntities](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectPiiEntities)
+ [DetectSentiment](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSentiment)
+ [DetectSyntax](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DetectSyntax)
------
# Mendeteksi entitas dalam teks yang diekstrak dari gambar menggunakan SDK AWS
Contoh kode berikut menunjukkan cara menggunakan Amazon Comprehend untuk mendeteksi entitas dalam teks yang diekstrak oleh Amazon Textract dari gambar yang disimpan di Amazon S3.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Menunjukkan cara menggunakan AWS SDK untuk Python (Boto3) dalam buku catatan Jupyter untuk mendeteksi entitas dalam teks yang diekstraksi dari gambar. Contoh ini menggunakan Amazon Textract untuk mengekstrak teks dari gambar yang disimpan di Amazon Simple Storage Service (Amazon S3) dan Amazon Comprehend untuk mendeteksi entitas dalam teks yang diekstraksi.
Contoh ini adalah notebook Jupyter dan harus dijalankan di lingkungan yang dapat meng-host notebook. Untuk petunjuk tentang cara menjalankan contoh menggunakan Amazon SageMaker AI, lihat petunjuk di [TextractAndComprehendNotebook.ipynb](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook/TextractAndComprehendNotebook.ipynb).
Untuk kode sumber lengkap dan instruksi tentang cara mengatur dan menjalankan, lihat contoh lengkapnya di [GitHub](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/cross_service/textract_comprehend_notebook#readme).
**Layanan yang digunakan dalam contoh ini**
+ Amazon Comprehend
+ Amazon S3
+ Amazon Textract
------
# Jalankan pekerjaan pemodelan topik Amazon Comprehend pada data sampel menggunakan SDK AWS
Contoh kode berikut ini menunjukkan cara untuk melakukan:
+ Jalankan pekerjaan pemodelan topik Amazon Comprehend pada data sampel.
+ Dapatkan informasi tentang pekerjaan itu.
+ Ekstrak data output pekerjaan dari Amazon S3.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
Buat kelas pembungkus untuk memanggil tindakan pemodelan topik Amazon Comprehend.
```
class ComprehendTopicModeler:
"""Encapsulates a Comprehend topic modeler."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
def start_job(
self,
job_name,
input_bucket,
input_key,
input_format,
output_bucket,
output_key,
data_access_role_arn,
):
"""
Starts a topic modeling job. Input is read from the specified Amazon S3
input bucket and written to the specified output bucket. Output data is stored
in a tar archive compressed in gzip format. The job runs asynchronously, so you
can call `describe_topics_detection_job` to get job status until it
returns a status of SUCCEEDED.
:param job_name: The name of the job.
:param input_bucket: An Amazon S3 bucket that contains job input.
:param input_key: The prefix used to find input data in the input
bucket. If multiple objects have the same prefix, all
of them are used.
:param input_format: The format of the input data, either one document per
file or one document per line.
:param output_bucket: The Amazon S3 bucket where output data is written.
:param output_key: The prefix prepended to the output data.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
input bucket and write to the output bucket.
:return: Information about the job, including the job ID.
"""
try:
response = self.comprehend_client.start_topics_detection_job(
JobName=job_name,
DataAccessRoleArn=data_access_role_arn,
InputDataConfig={
"S3Uri": f"s3://{input_bucket}/{input_key}",
"InputFormat": input_format.value,
},
OutputDataConfig={"S3Uri": f"s3://{output_bucket}/{output_key}"},
)
logger.info("Started topic modeling job %s.", response["JobId"])
except ClientError:
logger.exception("Couldn't start topic modeling job.")
raise
else:
return response
def describe_job(self, job_id):
"""
Gets metadata about a topic modeling job.
:param job_id: The ID of the job to look up.
:return: Metadata about the job.
"""
try:
response = self.comprehend_client.describe_topics_detection_job(
JobId=job_id
)
job = response["TopicsDetectionJobProperties"]
logger.info("Got topic detection job %s.", job_id)
except ClientError:
logger.exception("Couldn't get topic detection job %s.", job_id)
raise
else:
return job
def list_jobs(self):
"""
Lists topic modeling jobs for the current account.
:return: The list of jobs.
"""
try:
response = self.comprehend_client.list_topics_detection_jobs()
jobs = response["TopicsDetectionJobPropertiesList"]
logger.info("Got %s topic detection jobs.", len(jobs))
except ClientError:
logger.exception("Couldn't get topic detection jobs.")
raise
else:
return jobs
```
Gunakan kelas pembungkus untuk menjalankan pekerjaan pemodelan topik dan mendapatkan data pekerjaan.
```
def usage_demo():
print("-" * 88)
print("Welcome to the Amazon Comprehend topic modeling demo!")
print("-" * 88)
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
input_prefix = "input/"
output_prefix = "output/"
demo_resources = ComprehendDemoResources(
boto3.resource("s3"), boto3.resource("iam")
)
topic_modeler = ComprehendTopicModeler(boto3.client("comprehend"))
print("Setting up storage and security resources needed for the demo.")
demo_resources.setup("comprehend-topic-modeler-demo")
print("Copying sample data from public bucket into input bucket.")
demo_resources.bucket.copy(
{"Bucket": "public-sample-us-west-2", "Key": "TopicModeling/Sample.txt"},
f"{input_prefix}sample.txt",
)
print("Starting topic modeling job on sample data.")
job_info = topic_modeler.start_job(
"demo-topic-modeling-job",
demo_resources.bucket.name,
input_prefix,
JobInputFormat.per_line,
demo_resources.bucket.name,
output_prefix,
demo_resources.data_access_role.arn,
)
print(
f"Waiting for job {job_info['JobId']} to complete. This typically takes "
f"20 - 30 minutes."
)
job_waiter = JobCompleteWaiter(topic_modeler.comprehend_client)
job_waiter.wait(job_info["JobId"])
job = topic_modeler.describe_job(job_info["JobId"])
print(f"Job {job['JobId']} complete:")
pprint(job)
print(
f"Getting job output data from the output Amazon S3 bucket: "
f"{job['OutputDataConfig']['S3Uri']}."
)
job_output = demo_resources.extract_job_output(job)
lines = 10
print(f"First {lines} lines of document topics output:")
pprint(job_output["doc-topics.csv"]["data"][:lines])
print(f"First {lines} lines of terms output:")
pprint(job_output["topic-terms.csv"]["data"][:lines])
print("Cleaning up resources created for the demo.")
demo_resources.cleanup()
print("Thanks for watching!")
print("-" * 88)
```
+ Untuk detail API, lihat topik berikut di *Referensi API AWS SDK untuk Python (Boto3)*.
+ [DescribeTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeTopicsDetectionJob)
+ [ListTopicsDetectionJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListTopicsDetectionJobs)
+ [StartTopicsDetectionJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartTopicsDetectionJob)
------
# Latih pengklasifikasi Amazon Comprehend kustom dan klasifikasikan dokumen menggunakan SDK AWS
Contoh kode berikut ini menunjukkan cara untuk melakukan:
+ Buat pengklasifikasi multi-label Amazon Comprehend.
+ Latih pengklasifikasi pada data sampel.
+ Jalankan pekerjaan klasifikasi pada kumpulan data kedua.
+ Ekstrak data output pekerjaan dari Amazon S3.
------
#### [ Python ]
**SDK untuk Python (Boto3)**
Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di [Repositori Contoh Kode AWS](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/python/example_code/comprehend#code-examples).
Buat kelas pembungkus untuk memanggil tindakan pengklasifikasi dokumen Amazon Comprehend.
```
class ComprehendClassifier:
"""Encapsulates an Amazon Comprehend custom classifier."""
def __init__(self, comprehend_client):
"""
:param comprehend_client: A Boto3 Comprehend client.
"""
self.comprehend_client = comprehend_client
self.classifier_arn = None
def create(
self,
name,
language_code,
training_bucket,
training_key,
data_access_role_arn,
mode,
):
"""
Creates a custom classifier. After the classifier is created, it immediately
starts training on the data found in the specified Amazon S3 bucket. Training
can take 30 minutes or longer. The `describe_document_classifier` function
can be used to get training status and returns a status of TRAINED when the
classifier is ready to use.
:param name: The name of the classifier.
:param language_code: The language the classifier can operate on.
:param training_bucket: The Amazon S3 bucket that contains the training data.
:param training_key: The prefix used to find training data in the training
bucket. If multiple objects have the same prefix, all
of them are used.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
training bucket.
:return: The ARN of the newly created classifier.
"""
try:
response = self.comprehend_client.create_document_classifier(
DocumentClassifierName=name,
LanguageCode=language_code,
InputDataConfig={"S3Uri": f"s3://{training_bucket}/{training_key}"},
DataAccessRoleArn=data_access_role_arn,
Mode=mode.value,
)
self.classifier_arn = response["DocumentClassifierArn"]
logger.info("Started classifier creation. Arn is: %s.", self.classifier_arn)
except ClientError:
logger.exception("Couldn't create classifier %s.", name)
raise
else:
return self.classifier_arn
def describe(self, classifier_arn=None):
"""
Gets metadata about a custom classifier, including its current status.
:param classifier_arn: The ARN of the classifier to look up.
:return: Metadata about the classifier.
"""
if classifier_arn is not None:
self.classifier_arn = classifier_arn
try:
response = self.comprehend_client.describe_document_classifier(
DocumentClassifierArn=self.classifier_arn
)
classifier = response["DocumentClassifierProperties"]
logger.info("Got classifier %s.", self.classifier_arn)
except ClientError:
logger.exception("Couldn't get classifier %s.", self.classifier_arn)
raise
else:
return classifier
def list(self):
"""
Lists custom classifiers for the current account.
:return: The list of classifiers.
"""
try:
response = self.comprehend_client.list_document_classifiers()
classifiers = response["DocumentClassifierPropertiesList"]
logger.info("Got %s classifiers.", len(classifiers))
except ClientError:
logger.exception(
"Couldn't get classifiers.",
)
raise
else:
return classifiers
def delete(self):
"""
Deletes the classifier.
"""
try:
self.comprehend_client.delete_document_classifier(
DocumentClassifierArn=self.classifier_arn
)
logger.info("Deleted classifier %s.", self.classifier_arn)
self.classifier_arn = None
except ClientError:
logger.exception("Couldn't deleted classifier %s.", self.classifier_arn)
raise
def start_job(
self,
job_name,
input_bucket,
input_key,
input_format,
output_bucket,
output_key,
data_access_role_arn,
):
"""
Starts a classification job. The classifier must be trained or the job
will fail. Input is read from the specified Amazon S3 input bucket and
written to the specified output bucket. Output data is stored in a tar
archive compressed in gzip format. The job runs asynchronously, so you can
call `describe_document_classification_job` to get job status until it
returns a status of SUCCEEDED.
:param job_name: The name of the job.
:param input_bucket: The Amazon S3 bucket that contains input data.
:param input_key: The prefix used to find input data in the input
bucket. If multiple objects have the same prefix, all
of them are used.
:param input_format: The format of the input data, either one document per
file or one document per line.
:param output_bucket: The Amazon S3 bucket where output data is written.
:param output_key: The prefix prepended to the output data.
:param data_access_role_arn: The Amazon Resource Name (ARN) of a role that
grants Comprehend permission to read from the
input bucket and write to the output bucket.
:return: Information about the job, including the job ID.
"""
try:
response = self.comprehend_client.start_document_classification_job(
DocumentClassifierArn=self.classifier_arn,
JobName=job_name,
InputDataConfig={
"S3Uri": f"s3://{input_bucket}/{input_key}",
"InputFormat": input_format.value,
},
OutputDataConfig={"S3Uri": f"s3://{output_bucket}/{output_key}"},
DataAccessRoleArn=data_access_role_arn,
)
logger.info(
"Document classification job %s is %s.", job_name, response["JobStatus"]
)
except ClientError:
logger.exception("Couldn't start classification job %s.", job_name)
raise
else:
return response
def describe_job(self, job_id):
"""
Gets metadata about a classification job.
:param job_id: The ID of the job to look up.
:return: Metadata about the job.
"""
try:
response = self.comprehend_client.describe_document_classification_job(
JobId=job_id
)
job = response["DocumentClassificationJobProperties"]
logger.info("Got classification job %s.", job["JobName"])
except ClientError:
logger.exception("Couldn't get classification job %s.", job_id)
raise
else:
return job
def list_jobs(self):
"""
Lists the classification jobs for the current account.
:return: The list of jobs.
"""
try:
response = self.comprehend_client.list_document_classification_jobs()
jobs = response["DocumentClassificationJobPropertiesList"]
logger.info("Got %s document classification jobs.", len(jobs))
except ClientError:
logger.exception(
"Couldn't get document classification jobs.",
)
raise
else:
return jobs
```
Buat kelas untuk membantu menjalankan skenario.
```
class ClassifierDemo:
"""
Encapsulates functions used to run the demonstration.
"""
def __init__(self, demo_resources):
"""
:param demo_resources: A ComprehendDemoResources class that manages resources
for the demonstration.
"""
self.demo_resources = demo_resources
self.training_prefix = "training/"
self.input_prefix = "input/"
self.input_format = JobInputFormat.per_line
self.output_prefix = "output/"
def setup(self):
"""Creates AWS resources used by the demo."""
self.demo_resources.setup("comprehend-classifier-demo")
def cleanup(self):
"""Deletes AWS resources used by the demo."""
self.demo_resources.cleanup()
@staticmethod
def _sanitize_text(text):
"""Removes characters that cause errors for the document parser."""
return text.replace("\r", " ").replace("\n", " ").replace(",", ";")
@staticmethod
def _get_issues(query, issue_count):
"""
Gets issues from GitHub using the specified query parameters.
:param query: The query string used to request issues from the GitHub API.
:param issue_count: The number of issues to retrieve.
:return: The list of issues retrieved from GitHub.
"""
issues = []
logger.info("Requesting issues from %s?%s.", GITHUB_SEARCH_URL, query)
response = requests.get(f"{GITHUB_SEARCH_URL}?{query}&per_page={issue_count}")
if response.status_code == 200:
issue_page = response.json()["items"]
logger.info("Got %s issues.", len(issue_page))
issues = [
{
"title": ClassifierDemo._sanitize_text(issue["title"]),
"body": ClassifierDemo._sanitize_text(issue["body"]),
"labels": {label["name"] for label in issue["labels"]},
}
for issue in issue_page
]
else:
logger.error(
"GitHub returned error code %s with message %s.",
response.status_code,
response.json(),
)
logger.info("Found %s issues.", len(issues))
return issues
def get_training_issues(self, training_labels):
"""
Gets issues used for training the custom classifier. Training issues are
closed issues from the Boto3 repo that have known labels. Comprehend
requires a minimum of ten training issues per label.
:param training_labels: The issue labels to use for training.
:return: The set of issues used for training.
"""
issues = []
per_label_count = 15
for label in training_labels:
issues += self._get_issues(
f"q=type:issue+repo:boto/boto3+state:closed+label:{label}",
per_label_count,
)
for issue in issues:
issue["labels"] = issue["labels"].intersection(training_labels)
return issues
def get_input_issues(self, training_labels):
"""
Gets input issues from GitHub. For demonstration purposes, input issues
are open issues from the Boto3 repo with known labels, though in practice
any issue could be submitted to the classifier for labeling.
:param training_labels: The set of labels to query for.
:return: The set of issues used for input.
"""
issues = []
per_label_count = 5
for label in training_labels:
issues += self._get_issues(
f"q=type:issue+repo:boto/boto3+state:open+label:{label}",
per_label_count,
)
return issues
def upload_issue_data(self, issues, training=False):
"""
Uploads issue data to an Amazon S3 bucket, either for training or for input.
The data is first put into the format expected by Comprehend. For training,
the set of pipe-delimited labels is prepended to each document. For
input, labels are not sent.
:param issues: The set of issues to upload to Amazon S3.
:param training: Indicates whether the issue data is used for training or
input.
"""
try:
obj_key = (
self.training_prefix if training else self.input_prefix
) + "issues.txt"
if training:
issue_strings = [
f"{'|'.join(issue['labels'])},{issue['title']} {issue['body']}"
for issue in issues
]
else:
issue_strings = [
f"{issue['title']} {issue['body']}" for issue in issues
]
issue_bytes = BytesIO("\n".join(issue_strings).encode("utf-8"))
self.demo_resources.bucket.upload_fileobj(issue_bytes, obj_key)
logger.info(
"Uploaded data as %s to bucket %s.",
obj_key,
self.demo_resources.bucket.name,
)
except ClientError:
logger.exception(
"Couldn't upload data to bucket %s.", self.demo_resources.bucket.name
)
raise
def extract_job_output(self, job):
"""Extracts job output from Amazon S3."""
return self.demo_resources.extract_job_output(job)
@staticmethod
def reconcile_job_output(input_issues, output_dict):
"""
Reconciles job output with the list of input issues. Because the input issues
have known labels, these can be compared with the labels added by the
classifier to judge the accuracy of the output.
:param input_issues: The list of issues used as input.
:param output_dict: The dictionary of data that is output by the classifier.
:return: The list of reconciled input and output data.
"""
reconciled = []
for archive in output_dict.values():
for line in archive["data"]:
in_line = int(line["Line"])
in_labels = input_issues[in_line]["labels"]
out_labels = {
label["Name"]
for label in line["Labels"]
if float(label["Score"]) > 0.3
}
reconciled.append(
f"{line['File']}, line {in_line} has labels {in_labels}.\n"
f"\tClassifier assigned {out_labels}."
)
logger.info("Reconciled input and output labels.")
return reconciled
```
Latih pengklasifikasi pada serangkaian GitHub masalah dengan label yang diketahui, lalu kirim serangkaian GitHub masalah kedua ke pengklasifikasi sehingga dapat diberi label.
```
def usage_demo():
print("-" * 88)
print("Welcome to the Amazon Comprehend custom document classifier demo!")
print("-" * 88)
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
comp_demo = ClassifierDemo(
ComprehendDemoResources(boto3.resource("s3"), boto3.resource("iam"))
)
comp_classifier = ComprehendClassifier(boto3.client("comprehend"))
classifier_trained_waiter = ClassifierTrainedWaiter(
comp_classifier.comprehend_client
)
training_labels = {"bug", "feature-request", "dynamodb", "s3"}
print("Setting up storage and security resources needed for the demo.")
comp_demo.setup()
print("Getting training data from GitHub and uploading it to Amazon S3.")
training_issues = comp_demo.get_training_issues(training_labels)
comp_demo.upload_issue_data(training_issues, True)
classifier_name = "doc-example-classifier"
print(f"Creating document classifier {classifier_name}.")
comp_classifier.create(
classifier_name,
"en",
comp_demo.demo_resources.bucket.name,
comp_demo.training_prefix,
comp_demo.demo_resources.data_access_role.arn,
ClassifierMode.multi_label,
)
print(
f"Waiting until {classifier_name} is trained. This typically takes "
f"30–40 minutes."
)
classifier_trained_waiter.wait(comp_classifier.classifier_arn)
print(f"Classifier {classifier_name} is trained:")
pprint(comp_classifier.describe())
print("Getting input data from GitHub and uploading it to Amazon S3.")
input_issues = comp_demo.get_input_issues(training_labels)
comp_demo.upload_issue_data(input_issues)
print("Starting classification job on input data.")
job_info = comp_classifier.start_job(
"issue_classification_job",
comp_demo.demo_resources.bucket.name,
comp_demo.input_prefix,
comp_demo.input_format,
comp_demo.demo_resources.bucket.name,
comp_demo.output_prefix,
comp_demo.demo_resources.data_access_role.arn,
)
print(f"Waiting for job {job_info['JobId']} to complete.")
job_waiter = JobCompleteWaiter(comp_classifier.comprehend_client)
job_waiter.wait(job_info["JobId"])
job = comp_classifier.describe_job(job_info["JobId"])
print(f"Job {job['JobId']} complete:")
pprint(job)
print(
f"Getting job output data from Amazon S3: "
f"{job['OutputDataConfig']['S3Uri']}."
)
job_output = comp_demo.extract_job_output(job)
print("Job output:")
pprint(job_output)
print("Reconciling job output with labels from GitHub:")
reconciled_output = comp_demo.reconcile_job_output(input_issues, job_output)
print(*reconciled_output, sep="\n")
answer = input(f"Do you want to delete the classifier {classifier_name} (y/n)? ")
if answer.lower() == "y":
print(f"Deleting {classifier_name}.")
comp_classifier.delete()
print("Cleaning up resources created for the demo.")
comp_demo.cleanup()
print("Thanks for watching!")
print("-" * 88)
```
+ Untuk detail API, lihat topik berikut di *Referensi API AWS SDK untuk Python (Boto3)*.
+ [CreateDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/CreateDocumentClassifier)
+ [DeleteDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DeleteDocumentClassifier)
+ [DescribeDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassificationJob)
+ [DescribeDocumentClassifier](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/DescribeDocumentClassifier)
+ [ListDocumentClassificationJobs](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassificationJobs)
+ [ListDocumentClassifiers](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/ListDocumentClassifiers)
+ [StartDocumentClassificationJob](https://docs.aws.amazon.com/goto/boto3/comprehend-2017-11-27/StartDocumentClassificationJob)
------