This documentation is for Version 1 of the Amazon CLI only. For documentation related to Version 2 of the Amazon CLI, see the Version 2 User Guide.
Amazon Comprehend examples using Amazon CLI
The following code examples show you how to perform actions and implement common scenarios by using the Amazon Command Line Interface with Amazon Comprehend.
Actions are code excerpts from larger programs and must be run in context. While actions show you how to call individual service functions, you can see actions in context in their related scenarios.
Each example includes a link to the complete source code, where you can find instructions on how to set up and run the code in context.
Topics
Actions
The following code example shows how to use batch-detect-dominant-language
.
- Amazon CLI
-
To detect the dominant language of multiple input texts
The following
batch-detect-dominant-language
example analyzes multiple input texts and returns the dominant language of each. The pre-trained models confidence score is also output for each prediction.aws comprehend batch-detect-dominant-language \ --text-list
"Physics is the natural science that involves the study of matter and its motion and behavior through space and time, along with related concepts such as energy and force."
Output:
{ "ResultList": [ { "Index": 0, "Languages": [ { "LanguageCode": "en", "Score": 0.9986501932144165 } ] } ], "ErrorList": [] }
For more information, see Dominant Language
in the Amazon Comprehend Developer Guide. -
For API details, see BatchDetectDominantLanguage
in Amazon CLI Command Reference.
-
The following code example shows how to use batch-detect-entities
.
- Amazon CLI
-
To detect entities from multiple input texts
The following
batch-detect-entities
example analyzes multiple input texts and returns the named entities of each. The pre-trained model's confidence score is also output for each prediction.aws compreh
en
d batch-detect-entities \ --language-code en \ --text-list"Dear Jane, Your AnyCompany Financial Services LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st."
"Please send customer feedback to Sunshine Spa, 123 Main St, Anywhere or to Alice at AnySpa@example.com."
Output:
{ "ResultList": [ { "Index": 0, "Entities": [ { "Score": 0.9985517859458923, "Type": "PERSON", "Text": "Jane", "BeginOffset": 5, "EndOffset": 9 }, { "Score": 0.9767839312553406, "Type": "ORGANIZATION", "Text": "AnyCompany Financial Services, LLC", "BeginOffset": 16, "EndOffset": 50 }, { "Score": 0.9856694936752319, "Type": "OTHER", "Text": "1111-XXXX-1111-XXXX", "BeginOffset": 71, "EndOffset": 90 }, { "Score": 0.9652159810066223, "Type": "QUANTITY", "Text": ".53", "BeginOffset": 116, "EndOffset": 119 }, { "Score": 0.9986667037010193, "Type": "DATE", "Text": "July 31st", "BeginOffset": 135, "EndOffset": 144 } ] }, { "Index": 1, "Entities": [ { "Score": 0.720084547996521, "Type": "ORGANIZATION", "Text": "Sunshine Spa", "BeginOffset": 33, "EndOffset": 45 }, { "Score": 0.9865870475769043, "Type": "LOCATION", "Text": "123 Main St", "BeginOffset": 47, "EndOffset": 58 }, { "Score": 0.5895616412162781, "Type": "LOCATION", "Text": "Anywhere", "BeginOffset": 60, "EndOffset": 68 }, { "Score": 0.6809214353561401, "Type": "PERSON", "Text": "Alice", "BeginOffset": 75, "EndOffset": 80 }, { "Score": 0.9979087114334106, "Type": "OTHER", "Text": "AnySpa@example.com", "BeginOffset": 84, "EndOffset": 99 } ] } ], "ErrorList": [] }
For more information, see Entities
in the Amazon Comprehend Developer Guide. -
For API details, see BatchDetectEntities
in Amazon CLI Command Reference.
-
The following code example shows how to use batch-detect-key-phrases
.
- Amazon CLI
-
To detect key phrases of multiple text inputs
The following
batch-detect-key-phrases
example analyzes multiple input texts and returns the key noun phrases of each. The pre-trained model's confidence score for each prediction is also output.aws compreh
en
d batch-detect-key-phrases \ --language-code en \ --text-list"Hello Zhang Wei, I am John, writing to you about the trip for next Saturday."
"Dear Jane, Your AnyCompany Financial Services LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st."
"Please send customer feedback to Sunshine Spa, 123 Main St, Anywhere or to Alice at AnySpa@example.com."
Output:
{ "ResultList": [ { "Index": 0, "KeyPhrases": [ { "Score": 0.99700927734375, "Text": "Zhang Wei", "BeginOffset": 6, "EndOffset": 15 }, { "Score": 0.9929308891296387, "Text": "John", "BeginOffset": 22, "EndOffset": 26 }, { "Score": 0.9997230172157288, "Text": "the trip", "BeginOffset": 49, "EndOffset": 57 }, { "Score": 0.9999470114707947, "Text": "next Saturday", "BeginOffset": 62, "EndOffset": 75 } ] }, { "Index": 1, "KeyPhrases": [ { "Score": 0.8358274102210999, "Text": "Dear Jane", "BeginOffset": 0, "EndOffset": 9 }, { "Score": 0.989359974861145, "Text": "Your AnyCompany Financial Services", "BeginOffset": 11, "EndOffset": 45 }, { "Score": 0.8812323808670044, "Text": "LLC credit card account 1111-XXXX-1111-XXXX", "BeginOffset": 47, "EndOffset": 90 }, { "Score": 0.9999381899833679, "Text": "a minimum payment", "BeginOffset": 95, "EndOffset": 112 }, { "Score": 0.9997439980506897, "Text": ".53", "BeginOffset": 116, "EndOffset": 119 }, { "Score": 0.996875524520874, "Text": "July 31st", "BeginOffset": 135, "EndOffset": 144 } ] }, { "Index": 2, "KeyPhrases": [ { "Score": 0.9990295767784119, "Text": "customer feedback", "BeginOffset": 12, "EndOffset": 29 }, { "Score": 0.9994127750396729, "Text": "Sunshine Spa", "BeginOffset": 33, "EndOffset": 45 }, { "Score": 0.9892991185188293, "Text": "123 Main St", "BeginOffset": 47, "EndOffset": 58 }, { "Score": 0.9969810843467712, "Text": "Alice", "BeginOffset": 75, "EndOffset": 80 }, { "Score": 0.9703696370124817, "Text": "AnySpa@example.com", "BeginOffset": 84, "EndOffset": 99 } ] } ], "ErrorList": [] }
For more information, see Key Phrases
in the Amazon Comprehend Developer Guide. -
For API details, see BatchDetectKeyPhrases
in Amazon CLI Command Reference.
-
The following code example shows how to use batch-detect-sentiment
.
- Amazon CLI
-
To detect the prevailing sentiment of multiple input texts
The following
batch-detect-sentiment
example analyzes multiple input texts and returns the prevailing sentiment (POSITIVE
,NEUTRAL
,MIXED
, orNEGATIVE
, of each one).aws comprehend batch-detect-sentiment \ --text-list
"That movie was very boring, I can't believe it was over four hours long."
"It is a beautiful day for hiking today."
"My meal was okay, I'm excited to try other restaurants."
\ --language-codeen
Output:
{ "ResultList": [ { "Index": 0, "Sentiment": "NEGATIVE", "SentimentScore": { "Positive": 0.00011316669406369328, "Negative": 0.9995445609092712, "Neutral": 0.00014722718333359808, "Mixed": 0.00019498742767609656 } }, { "Index": 1, "Sentiment": "POSITIVE", "SentimentScore": { "Positive": 0.9981263279914856, "Negative": 0.00015240783977787942, "Neutral": 0.0013876151060685515, "Mixed": 0.00033366199932061136 } }, { "Index": 2, "Sentiment": "MIXED", "SentimentScore": { "Positive": 0.15930435061454773, "Negative": 0.11471917480230331, "Neutral": 0.26897063851356506, "Mixed": 0.45700588822364807 } } ], "ErrorList": [] }
For more information, see Sentiment
in the Amazon Comprehend Developer Guide. -
For API details, see BatchDetectSentiment
in Amazon CLI Command Reference.
-
The following code example shows how to use batch-detect-syntax
.
- Amazon CLI
-
To inspect the syntax and parts of speech of words in multiple input texts
The following
batch-detect-syntax
example analyzes the syntax of multiple input texts and returns the different parts of speech. The pre-trained model's confidence score is also output for each prediction.aws comprehend batch-detect-syntax \ --text-list
"It is a beautiful day."
"Can you please pass the salt?"
"Please pay the bill before the 31st."
\ --language-codeen
Output:
{ "ResultList": [ { "Index": 0, "SyntaxTokens": [ { "TokenId": 1, "Text": "It", "BeginOffset": 0, "EndOffset": 2, "PartOfSpeech": { "Tag": "PRON", "Score": 0.9999740719795227 } }, { "TokenId": 2, "Text": "is", "BeginOffset": 3, "EndOffset": 5, "PartOfSpeech": { "Tag": "VERB", "Score": 0.999937117099762 } }, { "TokenId": 3, "Text": "a", "BeginOffset": 6, "EndOffset": 7, "PartOfSpeech": { "Tag": "DET", "Score": 0.9999926686286926 } }, { "TokenId": 4, "Text": "beautiful", "BeginOffset": 8, "EndOffset": 17, "PartOfSpeech": { "Tag": "ADJ", "Score": 0.9987891912460327 } }, { "TokenId": 5, "Text": "day", "BeginOffset": 18, "EndOffset": 21, "PartOfSpeech": { "Tag": "NOUN", "Score": 0.9999778866767883 } }, { "TokenId": 6, "Text": ".", "BeginOffset": 21, "EndOffset": 22, "PartOfSpeech": { "Tag": "PUNCT", "Score": 0.9999974966049194 } } ] }, { "Index": 1, "SyntaxTokens": [ { "TokenId": 1, "Text": "Can", "BeginOffset": 0, "EndOffset": 3, "PartOfSpeech": { "Tag": "AUX", "Score": 0.9999770522117615 } }, { "TokenId": 2, "Text": "you", "BeginOffset": 4, "EndOffset": 7, "PartOfSpeech": { "Tag": "PRON", "Score": 0.9999986886978149 } }, { "TokenId": 3, "Text": "please", "BeginOffset": 8, "EndOffset": 14, "PartOfSpeech": { "Tag": "INTJ", "Score": 0.9681622385978699 } }, { "TokenId": 4, "Text": "pass", "BeginOffset": 15, "EndOffset": 19, "PartOfSpeech": { "Tag": "VERB", "Score": 0.9999874830245972 } }, { "TokenId": 5, "Text": "the", "BeginOffset": 20, "EndOffset": 23, "PartOfSpeech": { "Tag": "DET", "Score": 0.9999827146530151 } }, { "TokenId": 6, "Text": "salt", "BeginOffset": 24, "EndOffset": 28, "PartOfSpeech": { "Tag": "NOUN", "Score": 0.9995040893554688 } }, { "TokenId": 7, "Text": "?", "BeginOffset": 28, "EndOffset": 29, "PartOfSpeech": { "Tag": "PUNCT", "Score": 0.999998152256012 } } ] }, { "Index": 2, "SyntaxTokens": [ { "TokenId": 1, "Text": "Please", "BeginOffset": 0, "EndOffset": 6, "PartOfSpeech": { "Tag": "INTJ", "Score": 0.9997857809066772 } }, { "TokenId": 2, "Text": "pay", "BeginOffset": 7, "EndOffset": 10, "PartOfSpeech": { "Tag": "VERB", "Score": 0.9999252557754517 } }, { "TokenId": 3, "Text": "the", "BeginOffset": 11, "EndOffset": 14, "PartOfSpeech": { "Tag": "DET", "Score": 0.9999842643737793 } }, { "TokenId": 4, "Text": "bill", "BeginOffset": 15, "EndOffset": 19, "PartOfSpeech": { "Tag": "NOUN", "Score": 0.9999588131904602 } }, { "TokenId": 5, "Text": "before", "BeginOffset": 20, "EndOffset": 26, "PartOfSpeech": { "Tag": "ADP", "Score": 0.9958304762840271 } }, { "TokenId": 6, "Text": "the", "BeginOffset": 27, "EndOffset": 30, "PartOfSpeech": { "Tag": "DET", "Score": 0.9999947547912598 } }, { "TokenId": 7, "Text": "31st", "BeginOffset": 31, "EndOffset": 35, "PartOfSpeech": { "Tag": "NOUN", "Score": 0.9924124479293823 } }, { "TokenId": 8, "Text": ".", "BeginOffset": 35, "EndOffset": 36, "PartOfSpeech": { "Tag": "PUNCT", "Score": 0.9999955892562866 } } ] } ], "ErrorList": [] }
For more information, see Syntax Analysis
in the Amazon Comprehend Developer Guide. -
For API details, see BatchDetectSyntax
in Amazon CLI Command Reference.
-
The following code example shows how to use batch-detect-targeted-sentiment
.
- Amazon CLI
-
To detect the sentiment and each named entity for multiple input texts
The following
batch-detect-targeted-sentiment
example analyzes multiple input texts and returns the named entities along with the prevailing sentiment attached to each entity. The pre-trained model's confidence score is also output for each prediction.aws compreh
en
d batch-detect-targeted-sentiment \ --language-code en \ --text-list"That movie was really boring, the original was way more entertaining"
"The trail is extra beautiful today."
"My meal was just okay."
Output:
{ "ResultList": [ { "Index": 0, "Entities": [ { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "Score": 0.9999009966850281, "GroupScore": 1.0, "Text": "movie", "Type": "MOVIE", "MentionSentiment": { "Sentiment": "NEGATIVE", "SentimentScore": { "Positive": 0.13887299597263336, "Negative": 0.8057460188865662, "Neutral": 0.05525200068950653, "Mixed": 0.00012799999967683107 } }, "BeginOffset": 5, "EndOffset": 10 } ] }, { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "Score": 0.9921110272407532, "GroupScore": 1.0, "Text": "original", "Type": "MOVIE", "MentionSentiment": { "Sentiment": "POSITIVE", "SentimentScore": { "Positive": 0.9999989867210388, "Negative": 9.999999974752427e-07, "Neutral": 0.0, "Mixed": 0.0 } }, "BeginOffset": 34, "EndOffset": 42 } ] } ] }, { "Index": 1, "Entities": [ { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "Score": 0.7545599937438965, "GroupScore": 1.0, "Text": "trail", "Type": "OTHER", "MentionSentiment": { "Sentiment": "POSITIVE", "SentimentScore": { "Positive": 1.0, "Negative": 0.0, "Neutral": 0.0, "Mixed": 0.0 } }, "BeginOffset": 4, "EndOffset": 9 } ] }, { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "Score": 0.9999960064888, "GroupScore": 1.0, "Text": "today", "Type": "DATE", "MentionSentiment": { "Sentiment": "NEUTRAL", "SentimentScore": { "Positive": 9.000000318337698e-06, "Negative": 1.9999999949504854e-06, "Neutral": 0.9999859929084778, "Mixed": 3.999999989900971e-06 } }, "BeginOffset": 29, "EndOffset": 34 } ] } ] }, { "Index": 2, "Entities": [ { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "Score": 0.9999880194664001, "GroupScore": 1.0, "Text": "My", "Type": "PERSON", "MentionSentiment": { "Sentiment": "NEUTRAL", "SentimentScore": { "Positive": 0.0, "Negative": 0.0, "Neutral": 1.0, "Mixed": 0.0 } }, "BeginOffset": 0, "EndOffset": 2 } ] }, { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "Score": 0.9995260238647461, "GroupScore": 1.0, "Text": "meal", "Type": "OTHER", "MentionSentiment": { "Sentiment": "NEUTRAL", "SentimentScore": { "Positive": 0.04695599898695946, "Negative": 0.003226999891921878, "Neutral": 0.6091709733009338, "Mixed": 0.34064599871635437 } }, "BeginOffset": 3, "EndOffset": 7 } ] } ] } ], "ErrorList": [] }
For more information, see Targeted Sentiment
in the Amazon Comprehend Developer Guide. -
For API details, see BatchDetectTargetedSentiment
in Amazon CLI Command Reference.
-
The following code example shows how to use classify-document
.
- Amazon CLI
-
To classify document with model-specific endpoint
The following
classify-document
example classifies a document with an endpoint of a custom model. The model in this example was trained on a dataset containing sms messages labeled as spam or non-spam, or, "ham".aws comprehend classify-document \ --endpoint-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint
\ --text"CONGRATULATIONS! TXT 1235550100 to win $5000"
Output:
{ "Classes": [ { "Name": "spam", "Score": 0.9998599290847778 }, { "Name": "ham", "Score": 0.00014001205272506922 } ] }
For more information, see Custom Classification
in the Amazon Comprehend Developer Guide. -
For API details, see ClassifyDocument
in Amazon CLI Command Reference.
-
The following code example shows how to use contains-pii-entities
.
- Amazon CLI
-
To analyze the input text for the presence of PII information
The following
contains-pii-entities
example analyzes the input text for the presence of personally identifiable information (PII) and returns the labels of identified PII entity types such as name, address, bank account number, or phone number.aws compreh
en
d contains-pii-entities \ --language-code en \ --text"Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st. Based on your autopay settings, we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. Customer feedback for Sunshine Spa, 100 Main St, Anywhere. Send comments to Alice at AnySpa@example.com."
Output:
{ "Labels": [ { "Name": "NAME", "Score": 1.0 }, { "Name": "EMAIL", "Score": 1.0 }, { "Name": "BANK_ACCOUNT_NUMBER", "Score": 0.9995794296264648 }, { "Name": "BANK_ROUTING", "Score": 0.9173126816749573 }, { "Name": "CREDIT_DEBIT_NUMBER", "Score": 1.0 } }
For more information, see Personally Identifiable Information (PII)
in the Amazon Comprehend Developer Guide. -
For API details, see ContainsPiiEntities
in Amazon CLI Command Reference.
-
The following code example shows how to use create-dataset
.
- Amazon CLI
-
To create a flywheel dataset
The following
create-dataset
example creates a dataset for a flywheel. This dataset will be used as additional training data as specified by the--dataset-type
tag.aws comprehend create-dataset \ --flywheel-arn
arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity
\ --dataset-nameexample-dataset
\ --dataset-type"TRAIN"
\ --input-data-configfile://inputConfig.json
Contents of
file://inputConfig.json
:{ "DataFormat": "COMPREHEND_CSV", "DocumentClassifierInputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/training-data.csv" } }
Output:
{ "DatasetArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity/dataset/example-dataset" }
For more information, see Flywheel Overview
in Amazon Comprehend Developer Guide. -
For API details, see CreateDataset
in Amazon CLI Command Reference.
-
The following code example shows how to use create-document-classifier
.
- Amazon CLI
-
To create a document classifier to categorize documents
The following
create-document-classifier
example begins the training process for a document classifier model. The training data file,training.csv
, is located at the--input-data-config
tag.training.csv
is a two column document where the labels, or, classifications are provided in the first column and the documents are provided in the second column.aws comprehend create-document-classifier \ --document-classifier-name
example-classifier
\ --data-access-arnarn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE
\ --input-data-config"S3Uri=s3://DOC-EXAMPLE-BUCKET/"
\ --language-codeen
Output:
{ "DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier" }
For more information, see Custom Classification
in the Amazon Comprehend Developer Guide. -
For API details, see CreateDocumentClassifier
in Amazon CLI Command Reference.
-
The following code example shows how to use create-endpoint
.
- Amazon CLI
-
To create an endpoint for a custom model
The following
create-endpoint
example creates an endpoint for synchronous inference for a previously trained custom model.aws comprehend create-endpoint \ --endpoint-name
example-classifier-endpoint-1
\ --model-arnarn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier
\ --desired-inference-units1
Output:
{ "EndpointArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint-1" }
For more information, see Managing Amazon Comprehend endpoints
in the Amazon Comprehend Developer Guide. -
For API details, see CreateEndpoint
in Amazon CLI Command Reference.
-
The following code example shows how to use create-entity-recognizer
.
- Amazon CLI
-
To create a custom entity recognizer
The following
create-entity-recognizer
example begins the training process for a custom entity recognizer model. This example uses a CSV file containing training documents,raw_text.csv
, and a CSV entity list,entity_list.csv
to train the model.entity-list.csv
contains the following columns: text and type.aws comprehend create-entity-recognizer \ --recognizer-name
example-entity-recognizer
--data-access-role-arnarn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role
\ --input-data-config"EntityTypes=[{Type=DEVICE}],Documents={S3Uri=s3://DOC-EXAMPLE-BUCKET/trainingdata/raw_text.csv},EntityList={S3Uri=s3://DOC-EXAMPLE-BUCKET/trainingdata/entity_list.csv}"
--language-codeen
Output:
{ "EntityRecognizerArn": "arn:aws:comprehend:us-west-2:111122223333:example-entity-recognizer/entityrecognizer1" }
For more information, see Custom entity recognition
in the Amazon Comprehend Developer Guide. -
For API details, see CreateEntityRecognizer
in Amazon CLI Command Reference.
-
The following code example shows how to use create-flywheel
.
- Amazon CLI
-
To create a flywheel
The following
create-flywheel
example creates a flywheel to orchestrate the ongoing training of either a document classification or entity recognition model. The flywheel in this example is created to manage an existing trained model specified by the--active-model-arn
tag. When the flywheel is created, a data lake is created at the--input-data-lake
tag.aws comprehend create-flywheel \ --flywheel-name
example-flywheel
\ --active-model-arnarn:aws:comprehend:us-west-2:111122223333:document-classifier/example-model/version/1
\ --data-access-role-arnarn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role
\ --data-lake-s3-uri"s3://DOC-EXAMPLE-BUCKET"
Output:
{ "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel" }
For more information, see Flywheel Overview
in Amazon Comprehend Developer Guide. -
For API details, see CreateFlywheel
in Amazon CLI Command Reference.
-
The following code example shows how to use delete-document-classifier
.
- Amazon CLI
-
To delete a custom document classifier
The following
delete-document-classifier
example deletes a custom document classifier model.aws comprehend delete-document-classifier \ --document-classifier-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-1
This command produces no output.
For more information, see Managing Amazon Comprehend endpoints
in the Amazon Comprehend Developer Guide. -
For API details, see DeleteDocumentClassifier
in Amazon CLI Command Reference.
-
The following code example shows how to use delete-endpoint
.
- Amazon CLI
-
To delete an endpoint for a custom model
The following
delete-endpoint
example deletes a model-specific endpoint. All endpoints must be deleted in order for the model to be deleted.aws comprehend delete-endpoint \ --endpoint-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint-1
This command produces no output.
For more information, see Managing Amazon Comprehend endpoints
in the Amazon Comprehend Developer Guide. -
For API details, see DeleteEndpoint
in Amazon CLI Command Reference.
-
The following code example shows how to use delete-entity-recognizer
.
- Amazon CLI
-
To delete a custom entity recognizer model
The following
delete-entity-recognizer
example deletes a custom entity recognizer model.aws comprehend delete-entity-recognizer \ --entity-recognizer-arn
arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/example-entity-recognizer-1
This command produces no output.
For more information, see Managing Amazon Comprehend endpoints
in the Amazon Comprehend Developer Guide. -
For API details, see DeleteEntityRecognizer
in Amazon CLI Command Reference.
-
The following code example shows how to use delete-flywheel
.
- Amazon CLI
-
To delete a flywheel
The following
delete-flywheel
example deletes a flywheel. The data lake or the model associated with the flywheel is not deleted.aws comprehend delete-flywheel \ --flywheel-arn
arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel-1
This command produces no output.
For more information, see Flywheel overview
in the Amazon Comprehend Developer Guide. -
For API details, see DeleteFlywheel
in Amazon CLI Command Reference.
-
The following code example shows how to use delete-resource-policy
.
- Amazon CLI
-
To delete a resource-based policy
The following
delete-resource-policy
example deletes a resource-based policy from an Amazon Comprehend resource.aws comprehend delete-resource-policy \ --resource-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-1/version/1
This command produces no output.
For more information, see Copying custom models between Amazon accounts
in the Amazon Comprehend Developer Guide. -
For API details, see DeleteResourcePolicy
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-dataset
.
- Amazon CLI
-
To describe a flywheel dataset
The following
describe-dataset
example gets the properties of a flywheel dataset.aws comprehend describe-dataset \ --dataset-arn
arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity/dataset/example-dataset
Output:
{ "DatasetProperties": { "DatasetArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity/dataset/example-dataset", "DatasetName": "example-dataset", "DatasetType": "TRAIN", "DatasetS3Uri": "s3://DOC-EXAMPLE-BUCKET/flywheel-entity/schemaVersion=1/12345678A123456Z/datasets/example-dataset/20230616T203710Z/", "Status": "CREATING", "CreationTime": "2023-06-16T20:37:10.400000+00:00" } }
For more information, see Flywheel Overview
in Amazon Comprehend Developer Guide. -
For API details, see DescribeDataset
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-document-classification-job
.
- Amazon CLI
-
To describe a document classification job
The following
describe-document-classification-job
example gets the properties of an asynchronous document classification job.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://DOC-EXAMPLE-BUCKET/jobdata/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-CLN-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole" } }
For more information, see Custom Classification
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeDocumentClassificationJob
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-document-classifier
.
- Amazon CLI
-
To describe a document classifier
The following
describe-document-classifier
example gets the properties of a custom document classifier model.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://DOC-EXAMPLE-BUCKET/trainingdata" }, "OutputDataConfig": {}, "ClassifierMetadata": { "NumberOfLabels": 3, "NumberOfTrainedDocuments": 5016, "NumberOfTestDocuments": 557, "EvaluationMetrics": { "Accuracy": 0.9856, "Precision": 0.9919, "Recall": 0.9459, "F1Score": 0.9673, "MicroPrecision": 0.9856, "MicroRecall": 0.9856, "MicroF1Score": 0.9856, "HammingLoss": 0.0144 } }, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role", "Mode": "MULTI_CLASS" } }
For more information, see Creating and managing custom models
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeDocumentClassifier
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-dominant-language-detection-job
.
- Amazon CLI
-
To describe a dominant language detection detection job.
The following
describe-dominant-language-detection-job
example gets the properties of an asynchronous dominant language detection job.aws comprehend describe-dominant-language-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "DominantLanguageDetectionJobProperties": { "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:dominant-language-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobName": "languageanalysis1", "JobStatus": "IN_PROGRESS", "SubmitTime": "2023-06-09T18:10:38.037000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-LANGUAGE-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" } }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeDominantLanguageDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-endpoint
.
- Amazon CLI
-
To describe a specific endpoint
The following
describe-endpoint
example gets the properties of a model-specific endpoint.aws comprehend describe-endpoint \ --endpoint-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint
Output:
{ "EndpointProperties": { "EndpointArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint, "Status": "IN_SERVICE", "ModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier1", "DesiredModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier1", "DesiredInferenceUnits": 1, "CurrentInferenceUnits": 1, "CreationTime": "2023-06-13T20:32:54.526000+00:00", "LastModifiedTime": "2023-06-13T20:32:54.526000+00:00" } }
For more information, see Managing Amazon Comprehend endpoints
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeEndpoint
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-entities-detection-job
.
- Amazon CLI
-
To describe an entities detection job
The following
describe-entities-detection-job
example gets the properties of an asynchronous entities detection job.aws comprehend describe-entities-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "EntitiesDetectionJobProperties": { "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobName": "example-entity-detector", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-08T21:30:15.323000+00:00", "EndTime": "2023-06-08T21:40:23.509000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/thefolder/111122223333-NER-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::12345678012:role/service-role/AmazonComprehendServiceRole-example-role" } }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeEntitiesDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-entity-recognizer
.
- Amazon CLI
-
To describe an entity recognizer
The following
describe-entity-recognizer
example gets the properties of a custom entity recognizer model.aws comprehend describe-entity-recognizer \
entity-recognizer-arn
arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/business-recongizer-1/version/1
Output:
{ "EntityRecognizerProperties": { "EntityRecognizerArn": "arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/business-recongizer-1/version/1", "LanguageCode": "en", "Status": "TRAINED", "SubmitTime": "2023-06-14T20:44:59.631000+00:00", "EndTime": "2023-06-14T20:59:19.532000+00:00", "TrainingStartTime": "2023-06-14T20:48:52.811000+00:00", "TrainingEndTime": "2023-06-14T20:58:11.473000+00:00", "InputDataConfig": { "DataFormat": "COMPREHEND_CSV", "EntityTypes": [ { "Type": "BUSINESS" } ], "Documents": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata/dataset/", "InputFormat": "ONE_DOC_PER_LINE" }, "EntityList": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata/entity.csv" } }, "RecognizerMetadata": { "NumberOfTrainedDocuments": 1814, "NumberOfTestDocuments": 486, "EvaluationMetrics": { "Precision": 100.0, "Recall": 100.0, "F1Score": 100.0 }, "EntityTypes": [ { "Type": "BUSINESS", "EvaluationMetrics": { "Precision": 100.0, "Recall": 100.0, "F1Score": 100.0 }, "NumberOfTrainMentions": 1520 } ] }, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role", "VersionName": "1" } }
For more information, see Custom entity recognition
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeEntityRecognizer
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-events-detection-job
.
- Amazon CLI
-
To describe an events detection job.
The following
describe-events-detection-job
example gets the properties of an asynchronous events detection job.aws comprehend describe-events-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "EventsDetectionJobProperties": { "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:events-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobName": "events_job_1", "JobStatus": "IN_PROGRESS", "SubmitTime": "2023-06-12T18:45:56.054000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/EventsData", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-EVENTS-123456abcdeb0e11022f22a11EXAMPLE/output/" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role", "TargetEventTypes": [ "BANKRUPTCY", "EMPLOYMENT", "CORPORATE_ACQUISITION", "CORPORATE_MERGER", "INVESTMENT_GENERAL" ] } }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeEventsDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-flywheel-iteration
.
- Amazon CLI
-
To describe a flywheel iteration
The following
describe-flywheel-iteration
example gets the properties of a flywheel iteration.aws comprehend describe-flywheel-iteration \ --flywheel-arn
arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel
\ --flywheel-iteration-id20232222AEXAMPLE
Output:
{ "FlywheelIterationProperties": { "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity", "FlywheelIterationId": "20232222AEXAMPLE", "CreationTime": "2023-06-16T21:10:26.385000+00:00", "EndTime": "2023-06-16T23:33:16.827000+00:00", "Status": "COMPLETED", "Message": "FULL_ITERATION: Flywheel iteration performed all functions successfully.", "EvaluatedModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1", "EvaluatedModelMetrics": { "AverageF1Score": 0.7742663922375772, "AveragePrecision": 0.8287636394041166, "AverageRecall": 0.7427084833645399, "AverageAccuracy": 0.8795394154118689 }, "TrainedModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/Comprehend-Generated-v1-bb52d585", "TrainedModelMetrics": { "AverageF1Score": 0.9767700253081214, "AveragePrecision": 0.9767700253081214, "AverageRecall": 0.9767700253081214, "AverageAccuracy": 0.9858281665190434 }, "EvaluationManifestS3Prefix": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/flywheel-entity/schemaVersion=1/20230616T200543Z/evaluation/20230616T211026Z/" } }
For more information, see Flywheel overview
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeFlywheelIteration
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-flywheel
.
- Amazon CLI
-
To describe a flywheel
The following
describe-flywheel
example gets the properties of a flywheel. In this example, the model associated with the flywheel is a custom classifier model that is trained to classify documents as either spam or nonspam, or, "ham".aws comprehend describe-flywheel \ --flywheel-arn
arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel
Output:
{ "FlywheelProperties": { "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel", "ActiveModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-model/version/1", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role", "TaskConfig": { "LanguageCode": "en", "DocumentClassificationConfig": { "Mode": "MULTI_CLASS", "Labels": [ "ham", "spam" ] } }, "DataLakeS3Uri": "s3://DOC-EXAMPLE-BUCKET/example-flywheel/schemaVersion=1/20230616T200543Z/", "DataSecurityConfig": {}, "Status": "ACTIVE", "ModelType": "DOCUMENT_CLASSIFIER", "CreationTime": "2023-06-16T20:05:43.242000+00:00", "LastModifiedTime": "2023-06-16T20:21:43.567000+00:00" } }
For more information, see Flywheel Overview
in Amazon Comprehend Developer Guide. -
For API details, see DescribeFlywheel
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-key-phrases-detection-job
.
- Amazon CLI
-
To describe a key phrases detection job
The following
describe-key-phrases-detection-job
example gets the properties of an asynchronous key phrases detection job.aws comprehend describe-key-phrases-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "KeyPhrasesDetectionJobProperties": { "JobId": "69aa080c00fc68934a6a98f10EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/69aa080c00fc68934a6a98f10EXAMPLE", "JobName": "example-key-phrases-detection-job", "JobStatus": "COMPLETED", "SubmitTime": 1686606439.177, "EndTime": 1686606806.157, "InputDataConfig": { "S3Uri": "s3://dereksbucket1001/EventsData/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://dereksbucket1002/testfolder/111122223333-KP-69aa080c00fc68934a6a98f10EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-testrole" } }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeKeyPhrasesDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-pii-entities-detection-job
.
- Amazon CLI
-
To describe a PII entities detection job
The following
describe-pii-entities-detection-job
example gets the properties of an asynchronous pii entities detection job.aws comprehend describe-pii-entities-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "PiiEntitiesDetectionJobProperties": { "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobName": "example-pii-entities-job", "JobStatus": "IN_PROGRESS", "SubmitTime": "2023-06-08T21:30:15.323000+00:00", "EndTime": "2023-06-08T21:40:23.509000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/thefolder/111122223333-NER-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::12345678012:role/service-role/AmazonComprehendServiceRole-example-role" } }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see DescribePiiEntitiesDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-resource-policy
.
- Amazon CLI
-
To describe a resource policy attached to a model
The following
describe-resource-policy
example gets the properties of a resource-based policy attached to a model.aws comprehend describe-resource-policy \ --resource-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1
Output:
{ "ResourcePolicy": "{\"Version\":\"2012-10-17\",\"Statement\":[{\"Effect\":\"Allow\",\"Principal\":{\"AWS\":\"arn:aws:iam::444455556666:root\"},\"Action\":\"comprehend:ImportModel\",\"Resource\":\"*\"}]}", "CreationTime": "2023-06-19T18:44:26.028000+00:00", "LastModifiedTime": "2023-06-19T18:53:02.002000+00:00", "PolicyRevisionId": "baa675d069d07afaa2aa3106ae280f61" }
For more information, see Copying custom models between Amazon accounts
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeResourcePolicy
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-sentiment-detection-job
.
- Amazon CLI
-
To describe a sentiment detection job
The following
describe-sentiment-detection-job
example gets the properties of an asynchronous sentiment detection job.aws comprehend describe-sentiment-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "SentimentDetectionJobProperties": { "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:sentiment-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobName": "movie_review_analysis", "JobStatus": "IN_PROGRESS", "SubmitTime": "2023-06-09T23:16:15.956000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/MovieData", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole" } }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeSentimentDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-targeted-sentiment-detection-job
.
- Amazon CLI
-
To describe a targeted sentiment detection job
The following
describe-targeted-sentiment-detection-job
example gets the properties of an asynchronous targeted sentiment detection job.aws comprehend describe-targeted-sentiment-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "TargetedSentimentDetectionJobProperties": { "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:targeted-sentiment-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobName": "movie_review_analysis", "JobStatus": "IN_PROGRESS", "SubmitTime": "2023-06-09T23:16:15.956000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/MovieData", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole" } }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeTargetedSentimentDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use describe-topics-detection-job
.
- Amazon CLI
-
To describe a topics detection job
The following
describe-topics-detection-job
example gets the properties of an asynchronous topics detection job.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://DOC-EXAMPLE-BUCKET", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TOPICS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "NumberOfTopics": 10, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-examplerole" } }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see DescribeTopicsDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use detect-dominant-language
.
- Amazon CLI
-
To detect the dominant language of input text
The following
detect-dominant-language
analyzes the input text and identifies the dominant language. The pre-trained model's confidence score is also output.aws comprehend detect-dominant-language \ --text
"It is a beautiful day in Seattle."
Output:
{ "Languages": [ { "LanguageCode": "en", "Score": 0.9877256155014038 } ] }
For more information, see Dominant Language
in the Amazon Comprehend Developer Guide. -
For API details, see DetectDominantLanguage
in Amazon CLI Command Reference.
-
The following code example shows how to use detect-entities
.
- Amazon CLI
-
To detect named entities in input text
The following
detect-entities
example analyzes the input text and returns the named entities. The pre-trained model's confidence score is also output for each prediction.aws compreh
en
d 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 } ] }
For more information, see Entities
in the Amazon Comprehend Developer Guide. -
For API details, see DetectEntities
in Amazon CLI Command Reference.
-
The following code example shows how to use detect-key-phrases
.
- Amazon CLI
-
To detect key phrases in input text
The following
detect-key-phrases
example analyzes the input text and identifies the key noun phrases. The pre-trained model's confidence score is also output for each prediction.aws compreh
en
d 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 } ] }
For more information, see Key Phrases
in the Amazon Comprehend Developer Guide. -
For API details, see DetectKeyPhrases
in Amazon CLI Command Reference.
-
The following code example shows how to use detect-pii-entities
.
- Amazon CLI
-
To detect pii entities in input text
The following
detect-pii-entities
example analyzes the input text and identifies entities that contain personally identifiable information (PII). The pre-trained model's confidence score is also output for each prediction.aws compreh
en
d 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 } ] }
For more information, see Personally Identifiable Information (PII)
in the Amazon Comprehend Developer Guide. -
For API details, see DetectPiiEntities
in Amazon CLI Command Reference.
-
The following code example shows how to use detect-sentiment
.
- Amazon CLI
-
To detect the sentiment of an input text
The following
detect-sentiment
example analyzes the input text and returns an inference of the prevailing sentiment (POSITIVE
,NEUTRAL
,MIXED
, orNEGATIVE
).aws compreh
en
d 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 } }
For more information, see Sentiment
in the Amazon Comprehend Developer Guide -
For API details, see DetectSentiment
in Amazon CLI Command Reference.
-
The following code example shows how to use detect-syntax
.
- Amazon CLI
-
To detect the parts of speech in an input text
The following
detect-syntax
example analyzes the syntax of the input text and returns the different parts of speech. The pre-trained model's confidence score is also output for each prediction.aws compreh
en
d 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 } } ] }
For more information, see Syntax Analysis
in the Amazon Comprehend Developer Guide. -
For API details, see DetectSyntax
in Amazon CLI Command Reference.
-
The following code example shows how to use detect-targeted-sentiment
.
- Amazon CLI
-
To detect the targeted sentiment of named entities in an input text
The following
detect-targeted-sentiment
example analyzes the input text and returns the named entities in addition to the targeted sentiment associated with each entity. The pre-trained models confidence score for each prediction is also output.aws compreh
en
d detect-targeted-sentiment \ --language-code en \ --text"I do not enjoy January because it is too cold but August is the perfect temperature"
Output:
{ "Entities": [ { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "Score": 0.9999979734420776, "GroupScore": 1.0, "Text": "I", "Type": "PERSON", "MentionSentiment": { "Sentiment": "NEUTRAL", "SentimentScore": { "Positive": 0.0, "Negative": 0.0, "Neutral": 1.0, "Mixed": 0.0 } }, "BeginOffset": 0, "EndOffset": 1 } ] }, { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "Score": 0.9638869762420654, "GroupScore": 1.0, "Text": "January", "Type": "DATE", "MentionSentiment": { "Sentiment": "NEGATIVE", "SentimentScore": { "Positive": 0.0031610000878572464, "Negative": 0.9967250227928162, "Neutral": 0.00011100000119768083, "Mixed": 1.9999999949504854e-06 } }, "BeginOffset": 15, "EndOffset": 22 } ] }, { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { { "Score": 0.9664419889450073, "GroupScore": 1.0, "Text": "August", "Type": "DATE", "MentionSentiment": { "Sentiment": "POSITIVE", "SentimentScore": { "Positive": 0.9999549984931946, "Negative": 3.999999989900971e-06, "Neutral": 4.099999932805076e-05, "Mixed": 0.0 } }, "BeginOffset": 50, "EndOffset": 56 } ] }, { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "Score": 0.9803199768066406, "GroupScore": 1.0, "Text": "temperature", "Type": "ATTRIBUTE", "MentionSentiment": { "Sentiment": "POSITIVE", "SentimentScore": { "Positive": 1.0, "Negative": 0.0, "Neutral": 0.0, "Mixed": 0.0 } }, "BeginOffset": 77, "EndOffset": 88 } ] } ] }
For more information, see Targeted Sentiment
in the Amazon Comprehend Developer Guide. -
For API details, see DetectTargetedSentiment
in Amazon CLI Command Reference.
-
The following code example shows how to use import-model
.
- Amazon CLI
-
To import a model
The following
import-model
example imports a model from a different Amazon account. The document classifier model in account444455556666
has a resource-based policy allowing account111122223333
to import the model.aws comprehend import-model \ --source-model-arn
arn:aws:comprehend:us-west-2:444455556666:document-classifier/example-classifier
Output:
{ "ModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier" }
For more information, see Copying custom models between Amazon accounts
in the Amazon Comprehend Developer Guide. -
For API details, see ImportModel
in Amazon CLI Command Reference.
-
The following code example shows how to use list-datasets
.
- Amazon CLI
-
To list all flywheel datasets
The following
list-datasets
example lists all datasets associated with a flywheel.aws comprehend list-datasets \ --flywheel-arn
arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity
Output:
{ "DatasetPropertiesList": [ { "DatasetArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity/dataset/example-dataset-1", "DatasetName": "example-dataset-1", "DatasetType": "TRAIN", "DatasetS3Uri": "s3://DOC-EXAMPLE-BUCKET/flywheel-entity/schemaVersion=1/20230616T200543Z/datasets/example-dataset-1/20230616T203710Z/", "Status": "CREATING", "CreationTime": "2023-06-16T20:37:10.400000+00:00" }, { "DatasetArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity/dataset/example-dataset-2", "DatasetName": "example-dataset-2", "DatasetType": "TRAIN", "DatasetS3Uri": "s3://DOC-EXAMPLE-BUCKET/flywheel-entity/schemaVersion=1/20230616T200543Z/datasets/example-dataset-2/20230616T200607Z/", "Description": "TRAIN Dataset created by Flywheel creation.", "Status": "COMPLETED", "NumberOfDocuments": 5572, "CreationTime": "2023-06-16T20:06:07.722000+00:00" } ] }
For more information, see Flywheel Overview
in Amazon Comprehend Developer Guide. -
For API details, see ListDatasets
in Amazon CLI Command Reference.
-
The following code example shows how to use list-document-classification-jobs
.
- Amazon CLI
-
To list of all document classification jobs
The following
list-document-classification-jobs
example lists all document classification jobs.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://DOC-EXAMPLE-BUCKET/jobdata/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/1234567890101-CLN-e758dd56b824aa717ceab551f11749fb/output/output.tar.gz" }, "DataAccessRoleArn": "arn:aws:iam::1234567890101:role/service-role/AmazonComprehendServiceRole-example-role" }, { "JobId": "123456abcdeb0e11022f22a1EXAMPLE2", "JobArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classification-job/123456abcdeb0e11022f22a1EXAMPLE2", "JobName": "exampleclassificationjob2", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-14T17:22:39.829000+00:00", "EndTime": "2023-06-14T17:28:46.107000+00:00", "DocumentClassifierArn": "arn:aws:comprehend:us-west-2:1234567890101:document-classifier/mymodel/version/12", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/jobdata/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/1234567890101-CLN-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz" }, "DataAccessRoleArn": "arn:aws:iam::1234567890101:role/service-role/AmazonComprehendServiceRole-example-role" } ] }
For more information, see Custom Classification
in the Amazon Comprehend Developer Guide. -
For API details, see ListDocumentClassificationJobs
in Amazon CLI Command Reference.
-
The following code example shows how to use list-document-classifier-summaries
.
- Amazon CLI
-
To list the summaries of all created document classifiers
The following
list-document-classifier-summaries
example lists all created document classifier summaries.aws comprehend list-document-classifier-summaries
Output:
{ "DocumentClassifierSummariesList": [ { "DocumentClassifierName": "example-classifier-1", "NumberOfVersions": 1, "LatestVersionCreatedAt": "2023-06-13T22:07:59.825000+00:00", "LatestVersionName": "1", "LatestVersionStatus": "TRAINED" }, { "DocumentClassifierName": "example-classifier-2", "NumberOfVersions": 2, "LatestVersionCreatedAt": "2023-06-13T21:54:59.589000+00:00", "LatestVersionName": "2", "LatestVersionStatus": "TRAINED" } ] }
For more information, see Creating and managing custom models
in the Amazon Comprehend Developer Guide. -
For API details, see ListDocumentClassifierSummaries
in Amazon CLI Command Reference.
-
The following code example shows how to use list-document-classifiers
.
- Amazon CLI
-
To list of all document classifiers
The following
list-document-classifiers
example lists all trained and in-training document classifier models.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://DOC-EXAMPLE-BUCKET/trainingdata" }, "OutputDataConfig": {}, "ClassifierMetadata": { "NumberOfLabels": 3, "NumberOfTrainedDocuments": 5016, "NumberOfTestDocuments": 557, "EvaluationMetrics": { "Accuracy": 0.9856, "Precision": 0.9919, "Recall": 0.9459, "F1Score": 0.9673, "MicroPrecision": 0.9856, "MicroRecall": 0.9856, "MicroF1Score": 0.9856, "HammingLoss": 0.0144 } }, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-testorle", "Mode": "MULTI_CLASS" }, { "DocumentClassifierArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier2", "LanguageCode": "en", "Status": "TRAINING", "SubmitTime": "2023-06-13T21:20:28.690000+00:00", "InputDataConfig": { "DataFormat": "COMPREHEND_CSV", "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata" }, "OutputDataConfig": {}, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-testorle", "Mode": "MULTI_CLASS" } ] }
For more information, see Creating and managing custom models
in the Amazon Comprehend Developer Guide. -
For API details, see ListDocumentClassifiers
in Amazon CLI Command Reference.
-
The following code example shows how to use list-dominant-language-detection-jobs
.
- Amazon CLI
-
To list all dominant language detection jobs
The following
list-dominant-language-detection-jobs
example lists all in-progress and completed asynchronous dominant language detection jobs.aws comprehend list-dominant-language-detection-jobs
Output:
{ "DominantLanguageDetectionJobPropertiesList": [ { "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:dominant-language-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobName": "languageanalysis1", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-09T18:10:38.037000+00:00", "EndTime": "2023-06-09T18:18:45.498000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-LANGUAGE-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" }, { "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:dominant-language-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobName": "languageanalysis2", "JobStatus": "STOPPED", "SubmitTime": "2023-06-09T18:16:33.690000+00:00", "EndTime": "2023-06-09T18:24:40.608000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-LANGUAGE-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" } ] }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see ListDominantLanguageDetectionJobs
in Amazon CLI Command Reference.
-
The following code example shows how to use list-endpoints
.
- Amazon CLI
-
To list of all endpoints
The following
list-endpoints
example lists all active model-specific endpoints.aws comprehend list-endpoints
Output:
{ "EndpointPropertiesList": [ { "EndpointArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/ExampleClassifierEndpoint", "Status": "IN_SERVICE", "ModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier1", "DesiredModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier1", "DesiredInferenceUnits": 1, "CurrentInferenceUnits": 1, "CreationTime": "2023-06-13T20:32:54.526000+00:00", "LastModifiedTime": "2023-06-13T20:32:54.526000+00:00" }, { "EndpointArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/ExampleClassifierEndpoint2", "Status": "IN_SERVICE", "ModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier2", "DesiredModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier2", "DesiredInferenceUnits": 1, "CurrentInferenceUnits": 1, "CreationTime": "2023-06-13T20:32:54.526000+00:00", "LastModifiedTime": "2023-06-13T20:32:54.526000+00:00" } ] }
For more information, see Managing Amazon Comprehend endpoints
in the Amazon Comprehend Developer Guide. -
For API details, see ListEndpoints
in Amazon CLI Command Reference.
-
The following code example shows how to use list-entities-detection-jobs
.
- Amazon CLI
-
To list all entities detection jobs
The following
list-entities-detection-jobs
example lists all asynchronous entities detection jobs.aws comprehend list-entities-detection-jobs
Output:
{ "EntitiesDetectionJobPropertiesList": [ { "JobId": "468af39c28ab45b83eb0c4ab9EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:entities-detection-job/468af39c28ab45b83eb0c4ab9EXAMPLE", "JobName": "example-entities-detection", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-08T20:57:46.476000+00:00", "EndTime": "2023-06-08T21:05:53.718000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-NER-468af39c28ab45b83eb0c4ab9EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" }, { "JobId": "809691caeaab0e71406f80a28EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:entities-detection-job/809691caeaab0e71406f80a28EXAMPLE", "JobName": "example-entities-detection-2", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-08T21:30:15.323000+00:00", "EndTime": "2023-06-08T21:40:23.509000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-NER-809691caeaab0e71406f80a28EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" }, { "JobId": "e00597c36b448b91d70dea165EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:entities-detection-job/e00597c36b448b91d70dea165EXAMPLE", "JobName": "example-entities-detection-3", "JobStatus": "STOPPED", "SubmitTime": "2023-06-08T22:19:28.528000+00:00", "EndTime": "2023-06-08T22:27:33.991000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-NER-e00597c36b448b91d70dea165EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" } ] }
For more information, see Entities
in the Amazon Comprehend Developer Guide. -
For API details, see ListEntitiesDetectionJobs
in Amazon CLI Command Reference.
-
The following code example shows how to use list-entity-recognizer-summaries
.
- Amazon CLI
-
To list of summaries for all created entity recognizers
The following
list-entity-recognizer-summaries
example lists all entity recognizer summaries.aws comprehend list-entity-recognizer-summaries
Output:
{ "EntityRecognizerSummariesList": [ { "RecognizerName": "entity-recognizer-3", "NumberOfVersions": 2, "LatestVersionCreatedAt": "2023-06-15T23:15:07.621000+00:00", "LatestVersionName": "2", "LatestVersionStatus": "STOP_REQUESTED" }, { "RecognizerName": "entity-recognizer-2", "NumberOfVersions": 1, "LatestVersionCreatedAt": "2023-06-14T22:55:27.805000+00:00", "LatestVersionName": "2" "LatestVersionStatus": "TRAINED" }, { "RecognizerName": "entity-recognizer-1", "NumberOfVersions": 1, "LatestVersionCreatedAt": "2023-06-14T20:44:59.631000+00:00", "LatestVersionName": "1", "LatestVersionStatus": "TRAINED" } ] }
For more information, see Custom entity recognition
in the Amazon Comprehend Developer Guide. -
For API details, see ListEntityRecognizerSummaries
in Amazon CLI Command Reference.
-
The following code example shows how to use list-entity-recognizers
.
- Amazon CLI
-
To list of all custom entity recognizers
The following
list-entity-recognizers
example lists all created custom entity recognizers.aws comprehend list-entity-recognizers
Output:
{ "EntityRecognizerPropertiesList": [ { "EntityRecognizerArn": "arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/EntityRecognizer/version/1", "LanguageCode": "en", "Status": "TRAINED", "SubmitTime": "2023-06-14T20:44:59.631000+00:00", "EndTime": "2023-06-14T20:59:19.532000+00:00", "TrainingStartTime": "2023-06-14T20:48:52.811000+00:00", "TrainingEndTime": "2023-06-14T20:58:11.473000+00:00", "InputDataConfig": { "DataFormat": "COMPREHEND_CSV", "EntityTypes": [ { "Type": "BUSINESS" } ], "Documents": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata/dataset/", "InputFormat": "ONE_DOC_PER_LINE" }, "EntityList": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata/entity.csv" } }, "RecognizerMetadata": { "NumberOfTrainedDocuments": 1814, "NumberOfTestDocuments": 486, "EvaluationMetrics": { "Precision": 100.0, "Recall": 100.0, "F1Score": 100.0 }, "EntityTypes": [ { "Type": "BUSINESS", "EvaluationMetrics": { "Precision": 100.0, "Recall": 100.0, "F1Score": 100.0 }, "NumberOfTrainMentions": 1520 } ] }, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole", "VersionName": "1" }, { "EntityRecognizerArn": "arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/entityrecognizer3", "LanguageCode": "en", "Status": "TRAINED", "SubmitTime": "2023-06-14T22:57:51.056000+00:00", "EndTime": "2023-06-14T23:14:13.894000+00:00", "TrainingStartTime": "2023-06-14T23:01:33.984000+00:00", "TrainingEndTime": "2023-06-14T23:13:02.984000+00:00", "InputDataConfig": { "DataFormat": "COMPREHEND_CSV", "EntityTypes": [ { "Type": "DEVICE" } ], "Documents": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata/raw_txt.csv", "InputFormat": "ONE_DOC_PER_LINE" }, "EntityList": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/trainingdata/entity_list.csv" } }, "RecognizerMetadata": { "NumberOfTrainedDocuments": 4616, "NumberOfTestDocuments": 3489, "EvaluationMetrics": { "Precision": 98.54227405247813, "Recall": 100.0, "F1Score": 99.26578560939794 }, "EntityTypes": [ { "Type": "DEVICE", "EvaluationMetrics": { "Precision": 98.54227405247813, "Recall": 100.0, "F1Score": 99.26578560939794 }, "NumberOfTrainMentions": 2764 } ] }, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole" } ] }
For more information, see Custom entity recognition
in the Amazon Comprehend Developer Guide. -
For API details, see ListEntityRecognizers
in Amazon CLI Command Reference.
-
The following code example shows how to use list-events-detection-jobs
.
- Amazon CLI
-
To list all events detection jobs
The following
list-events-detection-jobs
example lists all asynchronous events detection jobs.aws comprehend list-events-detection-jobs
Output:
{ "EventsDetectionJobPropertiesList": [ { "JobId": "aa9593f9203e84f3ef032ce18EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:1111222233333:events-detection-job/aa9593f9203e84f3ef032ce18EXAMPLE", "JobName": "events_job_1", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-12T19:14:57.751000+00:00", "EndTime": "2023-06-12T19:21:04.962000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-SOURCE-BUCKET/EventsData/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/1111222233333-EVENTS-aa9593f9203e84f3ef032ce18EXAMPLE/output/" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::1111222233333:role/service-role/AmazonComprehendServiceRole-example-role", "TargetEventTypes": [ "BANKRUPTCY", "EMPLOYMENT", "CORPORATE_ACQUISITION", "CORPORATE_MERGER", "INVESTMENT_GENERAL" ] }, { "JobId": "4a990a2f7e82adfca6e171135EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:1111222233333:events-detection-job/4a990a2f7e82adfca6e171135EXAMPLE", "JobName": "events_job_2", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-12T19:55:43.702000+00:00", "EndTime": "2023-06-12T20:03:49.893000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-SOURCE-BUCKET/EventsData/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/1111222233333-EVENTS-4a990a2f7e82adfca6e171135EXAMPLE/output/" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::1111222233333:role/service-role/AmazonComprehendServiceRole-example-role", "TargetEventTypes": [ "BANKRUPTCY", "EMPLOYMENT", "CORPORATE_ACQUISITION", "CORPORATE_MERGER", "INVESTMENT_GENERAL" ] } ] }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see ListEventsDetectionJobs
in Amazon CLI Command Reference.
-
The following code example shows how to use list-flywheel-iteration-history
.
- Amazon CLI
-
To list all flywheel iteration history
The following
list-flywheel-iteration-history
example lists all iterations of a flywheel.aws comprehend list-flywheel-iteration-history --flywheel-arn
arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel
Output:
{ "FlywheelIterationPropertiesList": [ { "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel", "FlywheelIterationId": "20230619TEXAMPLE", "CreationTime": "2023-06-19T04:00:32.594000+00:00", "EndTime": "2023-06-19T04:00:49.248000+00:00", "Status": "COMPLETED", "Message": "FULL_ITERATION: Flywheel iteration performed all functions successfully.", "EvaluatedModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1", "EvaluatedModelMetrics": { "AverageF1Score": 0.7742663922375772, "AverageF1Score": 0.9876464664646313, "AveragePrecision": 0.9800000253081214, "AverageRecall": 0.9445600253081214, "AverageAccuracy": 0.9997281665190434 }, "EvaluationManifestS3Prefix": "s3://DOC-EXAMPLE-BUCKET/example-flywheel/schemaVersion=1/20230619TEXAMPLE/evaluation/20230619TEXAMPLE/" }, { "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel-2", "FlywheelIterationId": "20230616TEXAMPLE", "CreationTime": "2023-06-16T21:10:26.385000+00:00", "EndTime": "2023-06-16T23:33:16.827000+00:00", "Status": "COMPLETED", "Message": "FULL_ITERATION: Flywheel iteration performed all functions successfully.", "EvaluatedModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/spamvshamclassify/version/1", "EvaluatedModelMetrics": { "AverageF1Score": 0.7742663922375772, "AverageF1Score": 0.9767700253081214, "AveragePrecision": 0.9767700253081214, "AverageRecall": 0.9767700253081214, "AverageAccuracy": 0.9858281665190434 }, "EvaluationManifestS3Prefix": "s3://DOC-EXAMPLE-BUCKET/example-flywheel-2/schemaVersion=1/20230616TEXAMPLE/evaluation/20230616TEXAMPLE/" } ] }
For more information, see Flywheel overview
in the Amazon Comprehend Developer Guide. -
For API details, see ListFlywheelIterationHistory
in Amazon CLI Command Reference.
-
The following code example shows how to use list-flywheels
.
- Amazon CLI
-
To list all flywheels
The following
list-flywheels
example lists all created flywheels.aws comprehend list-flywheels
Output:
{ "FlywheelSummaryList": [ { "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel-1", "ActiveModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier/version/1", "DataLakeS3Uri": "s3://DOC-EXAMPLE-BUCKET/example-flywheel-1/schemaVersion=1/20230616T200543Z/", "Status": "ACTIVE", "ModelType": "DOCUMENT_CLASSIFIER", "CreationTime": "2023-06-16T20:05:43.242000+00:00", "LastModifiedTime": "2023-06-19T04:00:43.027000+00:00", "LatestFlywheelIteration": "20230619T040032Z" }, { "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel-2", "ActiveModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/exampleclassifier2/version/1", "DataLakeS3Uri": "s3://DOC-EXAMPLE-BUCKET/example-flywheel-2/schemaVersion=1/20220616T200543Z/", "Status": "ACTIVE", "ModelType": "DOCUMENT_CLASSIFIER", "CreationTime": "2022-06-16T20:05:43.242000+00:00", "LastModifiedTime": "2022-06-19T04:00:43.027000+00:00", "LatestFlywheelIteration": "20220619T040032Z" } ] }
For more information, see Flywheel overview
in the Amazon Comprehend Developer Guide. -
For API details, see ListFlywheels
in Amazon CLI Command Reference.
-
The following code example shows how to use list-key-phrases-detection-jobs
.
- Amazon CLI
-
To list all key phrases detection jobs
The following
list-key-phrases-detection-jobs
example lists all in-progress and completed asynchronous key phrases detection jobs.aws comprehend list-key-phrases-detection-jobs
Output:
{ "KeyPhrasesDetectionJobPropertiesList": [ { "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobName": "keyphrasesanalysis1", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-08T22:31:43.767000+00:00", "EndTime": "2023-06-08T22:39:52.565000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-SOURCE-BUCKET/AsyncBatchJobs/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-KP-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" }, { "JobId": "123456abcdeb0e11022f22a33EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/123456abcdeb0e11022f22a33EXAMPLE", "JobName": "keyphrasesanalysis2", "JobStatus": "STOPPED", "SubmitTime": "2023-06-08T22:57:52.154000+00:00", "EndTime": "2023-06-08T23:05:48.385000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-KP-123456abcdeb0e11022f22a33EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" }, { "JobId": "123456abcdeb0e11022f22a44EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/123456abcdeb0e11022f22a44EXAMPLE", "JobName": "keyphrasesanalysis3", "JobStatus": "FAILED", "Message": "NO_READ_ACCESS_TO_INPUT: The provided data access role does not have proper access to the input data.", "SubmitTime": "2023-06-09T16:47:04.029000+00:00", "EndTime": "2023-06-09T16:47:18.413000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-KP-123456abcdeb0e11022f22a44EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" } ] }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see ListKeyPhrasesDetectionJobs
in Amazon CLI Command Reference.
-
The following code example shows how to use list-pii-entities-detection-jobs
.
- Amazon CLI
-
To list all pii entities detection jobs
The following
list-pii-entities-detection-jobs
example lists all in-progress and completed asynchronous pii detection jobs.aws comprehend list-pii-entities-detection-jobs
Output:
{ "PiiEntitiesDetectionJobPropertiesList": [ { "JobId": "6f9db0c42d0c810e814670ee4EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/6f9db0c42d0c810e814670ee4EXAMPLE", "JobName": "example-pii-detection-job", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-09T21:02:46.241000+00:00", "EndTime": "2023-06-09T21:12:52.602000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-SOURCE-BUCKET/111122223333-PII-6f9db0c42d0c810e814670ee4EXAMPLE/output/" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role", "Mode": "ONLY_OFFSETS" }, { "JobId": "d927562638cfa739331a99b3cEXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/d927562638cfa739331a99b3cEXAMPLE", "JobName": "example-pii-detection-job-2", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-09T21:20:58.211000+00:00", "EndTime": "2023-06-09T21:31:06.027000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/AsyncBatchJobs/", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-PII-d927562638cfa739331a99b3cEXAMPLE/output/" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role", "Mode": "ONLY_OFFSETS" } ] }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see ListPiiEntitiesDetectionJobs
in Amazon CLI Command Reference.
-
The following code example shows how to use list-sentiment-detection-jobs
.
- Amazon CLI
-
To list all sentiment detection jobs
The following
list-sentiment-detection-jobs
example lists all in-progress and completed asynchronous sentiment detection jobs.aws comprehend list-sentiment-detection-jobs
Output:
{ "SentimentDetectionJobPropertiesList": [ { "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:sentiment-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobName": "example-sentiment-detection-job", "JobStatus": "IN_PROGRESS", "SubmitTime": "2023-06-09T22:42:20.545000+00:00", "EndTime": "2023-06-09T22:52:27.416000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/MovieData", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" }, { "JobId": "123456abcdeb0e11022f22a1EXAMPLE2", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:sentiment-detection-job/123456abcdeb0e11022f22a1EXAMPLE2", "JobName": "example-sentiment-detection-job-2", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-09T23:16:15.956000+00:00", "EndTime": "2023-06-09T23:26:00.168000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/MovieData2", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TS-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" } ] }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see ListSentimentDetectionJobs
in Amazon CLI Command Reference.
-
The following code example shows how to use list-tags-for-resource
.
- Amazon CLI
-
To list tags for resource
The following
list-tags-for-resource
example lists the tags for an Amazon Comprehend resource.aws comprehend list-tags-for-resource \ --resource-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1
Output:
{ "ResourceArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1", "Tags": [ { "Key": "Department", "Value": "Finance" }, { "Key": "location", "Value": "Seattle" } ] }
For more information, see Tagging your resources
in the Amazon Comprehend Developer Guide. -
For API details, see ListTagsForResource
in Amazon CLI Command Reference.
-
The following code example shows how to use list-targeted-sentiment-detection-jobs
.
- Amazon CLI
-
To list all targeted sentiment detection jobs
The following
list-targeted-sentiment-detection-jobs
example lists all in-progress and completed asynchronous targeted sentiment detection jobs.aws comprehend list-targeted-sentiment-detection-jobs
Output:
{ "TargetedSentimentDetectionJobPropertiesList": [ { "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:targeted-sentiment-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobName": "example-targeted-sentiment-detection-job", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-09T22:42:20.545000+00:00", "EndTime": "2023-06-09T22:52:27.416000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/MovieData", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-IOrole" }, { "JobId": "123456abcdeb0e11022f22a1EXAMPLE2", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:targeted-sentiment-detection-job/123456abcdeb0e11022f22a1EXAMPLE2", "JobName": "example-targeted-sentiment-detection-job-2", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-09T23:16:15.956000+00:00", "EndTime": "2023-06-09T23:26:00.168000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET/MovieData2", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/111122223333-TS-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" } ] }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see ListTargetedSentimentDetectionJobs
in Amazon CLI Command Reference.
-
The following code example shows how to use list-topics-detection-jobs
.
- Amazon CLI
-
To list all topic detection jobs
The following
list-topics-detection-jobs
example lists all in-progress and completed asynchronous topics detection jobs.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://DOC-EXAMPLE-BUCKET", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a11EXAMPLE/output/output.tar.gz" }, "NumberOfTopics": 10, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" }, { "JobId": "123456abcdeb0e11022f22a1EXAMPLE2", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a1EXAMPLE2", "JobName": "topic-analysis-2", "JobStatus": "COMPLETED", "SubmitTime": "2023-06-09T18:44:43.414000+00:00", "EndTime": "2023-06-09T18:50:50.872000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a1EXAMPLE2/output/output.tar.gz" }, "NumberOfTopics": 10, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" }, { "JobId": "123456abcdeb0e11022f22a1EXAMPLE3", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:topics-detection-job/123456abcdeb0e11022f22a1EXAMPLE3", "JobName": "topic-analysis-2", "JobStatus": "IN_PROGRESS", "SubmitTime": "2023-06-09T18:50:56.737000+00:00", "InputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-BUCKET", "InputFormat": "ONE_DOC_PER_LINE" }, "OutputDataConfig": { "S3Uri": "s3://DOC-EXAMPLE-DESTINATION-BUCKET/thefolder/111122223333-TOPICS-123456abcdeb0e11022f22a1EXAMPLE3/output/output.tar.gz" }, "NumberOfTopics": 10, "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role" } ] }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see ListTopicsDetectionJobs
in Amazon CLI Command Reference.
-
The following code example shows how to use put-resource-policy
.
- Amazon CLI
-
To attach a resource-based policy
The following
put-resource-policy
example attaches a resource-based policy to a model so that can be imported by another Amazon account. The policy is attached to the model in account111122223333
and allows account444455556666
import the model.aws comprehend put-resource-policy \ --resource-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1
\ --resource-policy '{"Version":"2012-10-17","Statement":[{"Effect":"Allow","Action":"comprehend:ImportModel","Resource":"*","Principal":{"AWS":["arn:aws:iam::444455556666:root"]}}]}
'Ouput:
{ "PolicyRevisionId": "aaa111d069d07afaa2aa3106aEXAMPLE" }
For more information, see Copying custom models between Amazon accounts
in the Amazon Comprehend Developer Guide. -
For API details, see PutResourcePolicy
in Amazon CLI Command Reference.
-
The following code example shows how to use start-document-classification-job
.
- Amazon CLI
-
To start document classification job
The following
start-document-classification-job
example starts a document classification job with a custom model on all of the files at the address specified by the--input-data-config
tag. In this example, the input S3 bucket containsSampleSMStext1.txt
,SampleSMStext2.txt
, andSampleSMStext3.txt
. The model was previously trained on document classifications of spam and non-spam, or, "ham", SMS messages. When the job is complete,output.tar.gz
is put at the location specified by the--output-data-config
tag.output.tar.gz
containspredictions.jsonl
which lists the classification of each document. The Json output is printed on one line per file, but is formatted here for readability.aws comprehend start-document-classification-job \ --job-name
exampleclassificationjob
\ --input-data-config"S3Uri=s3://DOC-EXAMPLE-BUCKET-INPUT/jobdata/"
\ --output-data-config"S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/"
\ --data-access-role-arnarn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role
\ --document-classifier-arnarn:aws:comprehend:us-west-2:111122223333:document-classifier/mymodel/version/12
Contents of
SampleSMStext1.txt
:"CONGRATULATIONS! TXT 2155550100 to win $5000"
Contents of
SampleSMStext2.txt
:"Hi, when do you want me to pick you up from practice?"
Contents of
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" }
Contents of
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}]}
For more information, see Custom Classification
in the Amazon Comprehend Developer Guide. -
For API details, see StartDocumentClassificationJob
in Amazon CLI Command Reference.
-
The following code example shows how to use start-dominant-language-detection-job
.
- Amazon CLI
-
To start an asynchronous language detection job
The following
start-dominant-language-detection-job
example starts an asynchronous language detection job for all of the files located at the address specified by the--input-data-config
tag. The S3 bucket in this example containsSampletext1.txt
. When the job is complete, the folder,output
, is placed in the location specified by the--output-data-config
tag. The folder containsoutput.txt
which contains the dominant language of each of the text files as well as the pre-trained model's confidence score for each prediction.aws comprehend start-dominant-language-detection-job \ --job-name
example_language_analysis_job
\ --language-codeen
\ --input-data-config"S3Uri=s3://DOC-EXAMPLE-BUCKET/"
\ --output-data-config"S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/"
\ --data-access-role-arnarn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role
\ --language-codeen
Contents of Sampletext1.txt:
"Physics is the natural science that involves the study of matter and its motion and behavior through space and time, along with related concepts such as energy and force."
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:dominant-language-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobStatus": "SUBMITTED" }
Contents of
output.txt
:{"File": "Sampletext1.txt", "Languages": [{"LanguageCode": "en", "Score": 0.9913753867149353}], "Line": 0}
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StartDominantLanguageDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use start-entities-detection-job
.
- Amazon CLI
-
Example 1: To start a standard entity detection job using the pre-trained model
The following
start-entities-detection-job
example starts an asynchronous entities detection job for all files located at the address specified by the--input-data-config
tag. The S3 bucket in this example containsSampletext1.txt
,Sampletext2.txt
, andSampletext3.txt
. When the job is complete, the folder,output
, is placed in the location specified by the--output-data-config
tag. The folder containsoutput.txt
which lists all of the named entities detected within each text file as well as the pre-trained model's confidence score for each prediction. The Json output is printed on one line per input file, but is formatted here for readability.aws comprehend start-entities-detection-job \ --job-name
entitiestest
\ --language-codeen
\ --input-data-config"S3Uri=s3://DOC-EXAMPLE-BUCKET/"
\ --output-data-config"S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/"
\ --data-access-role-arnarn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role
\ --language-codeen
Contents of
Sampletext1.txt
:"Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st."
Contents of
Sampletext2.txt
:"Dear Max, based on your autopay settings for your account example1.org account, we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. "
Contents of
Sampletext3.txt
:"Jane, please submit any customer feedback from this weekend to AnySpa, 123 Main St, Anywhere and send comments to Alice at AnySpa@example.com."
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobStatus": "SUBMITTED" }
Contents of
output.txt
with line indents for readability:{ "Entities": [ { "BeginOffset": 6, "EndOffset": 15, "Score": 0.9994006636420306, "Text": "Zhang Wei", "Type": "PERSON" }, { "BeginOffset": 22, "EndOffset": 26, "Score": 0.9976647915128143, "Text": "John", "Type": "PERSON" }, { "BeginOffset": 33, "EndOffset": 67, "Score": 0.9984608700836206, "Text": "AnyCompany Financial Services, LLC", "Type": "ORGANIZATION" }, { "BeginOffset": 88, "EndOffset": 107, "Score": 0.9868521019555556, "Text": "1111-XXXX-1111-XXXX", "Type": "OTHER" }, { "BeginOffset": 133, "EndOffset": 139, "Score": 0.998242565709204, "Text": "$24.53", "Type": "QUANTITY" }, { "BeginOffset": 155, "EndOffset": 164, "Score": 0.9993039263159287, "Text": "July 31st", "Type": "DATE" } ], "File": "SampleText1.txt", "Line": 0 } { "Entities": [ { "BeginOffset": 5, "EndOffset": 8, "Score": 0.9866232147545232, "Text": "Max", "Type": "PERSON" }, { "BeginOffset": 156, "EndOffset": 166, "Score": 0.9797723450933329, "Text": "XXXXXX1111", "Type": "OTHER" }, { "BeginOffset": 191, "EndOffset": 200, "Score": 0.9247838572396843, "Text": "XXXXX0000", "Type": "OTHER" } ], "File": "SampleText2.txt", "Line": 0 } { "Entities": [ { "Score": 0.9990532994270325, "Type": "PERSON", "Text": "Jane", "BeginOffset": 0, "EndOffset": 4 }, { "Score": 0.9519651532173157, "Type": "DATE", "Text": "this weekend", "BeginOffset": 47, "EndOffset": 59 }, { "Score": 0.5566426515579224, "Type": "ORGANIZATION", "Text": "AnySpa", "BeginOffset": 63, "EndOffset": 69 }, { "Score": 0.8059805631637573, "Type": "LOCATION", "Text": "123 Main St, Anywhere", "BeginOffset": 71, "EndOffset": 92 }, { "Score": 0.998830258846283, "Type": "PERSON", "Text": "Alice", "BeginOffset": 114, "EndOffset": 119 }, { "Score": 0.997818112373352, "Type": "OTHER", "Text": "AnySpa@example.com", "BeginOffset": 123, "EndOffset": 138 } ], "File": "SampleText3.txt", "Line": 0 }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. Example 2: To start a custom entity detection job
The following
start-entities-detection-job
example starts an asynchronous custom entities detection job for all files located at the address specified by the--input-data-config
tag. In this example, the S3 bucket in this example containsSampleFeedback1.txt
,SampleFeedback2.txt
, andSampleFeedback3.txt
. The entity recognizer model was trained on customer support Feedbacks to recognize device names. When the job is complete, an the folder,output
, is put at the location specified by the--output-data-config
tag. The folder containsoutput.txt
, which lists all of the named entities detected within each text file as well as the pre-trained model's confidence score for each prediction. The Json output is printed on one line per file, but is formatted here for readability.aws comprehend start-entities-detection-job \ --job-name
customentitiestest
\ --entity-recognizer-arn"arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/entityrecognizer"
\ --language-codeen
\ --input-data-config"S3Uri=s3://DOC-EXAMPLE-BUCKET/jobdata/"
\ --output-data-config"S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/"
\ --data-access-role-arn"arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-IOrole"
Contents of
SampleFeedback1.txt
:"I've been on the AnyPhone app have had issues for 24 hours when trying to pay bill. Cannot make payment. Sigh. | Oh man! Lets get that app up and running. DM me, and we can get to work!"
Contents of
SampleFeedback2.txt
:"Hi, I have a discrepancy with my new bill. Could we get it sorted out? A rep added stuff I didnt sign up for when I did my AnyPhone 10 upgrade. | We can absolutely get this sorted!"
Contents of
SampleFeedback3.txt
:"Is the by 1 get 1 free AnySmartPhone promo still going on? | Hi Christian! It ended yesterday, send us a DM if you have any questions and we can take a look at your options!"
Output:
{ "JobId": "019ea9edac758806850fa8a79ff83021", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:entities-detection-job/019ea9edac758806850fa8a79ff83021", "JobStatus": "SUBMITTED" }
Contents of
output.txt
with line indents for readability:{ "Entities": [ { "BeginOffset": 17, "EndOffset": 25, "Score": 0.9999728210205924, "Text": "AnyPhone", "Type": "DEVICE" } ], "File": "SampleFeedback1.txt", "Line": 0 } { "Entities": [ { "BeginOffset": 123, "EndOffset": 133, "Score": 0.9999892116761524, "Text": "AnyPhone 10", "Type": "DEVICE" } ], "File": "SampleFeedback2.txt", "Line": 0 } { "Entities": [ { "BeginOffset": 23, "EndOffset": 35, "Score": 0.9999971389852362, "Text": "AnySmartPhone", "Type": "DEVICE" } ], "File": "SampleFeedback3.txt", "Line": 0 }
For more information, see Custom entity recognition
in the Amazon Comprehend Developer Guide. -
For API details, see StartEntitiesDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use start-events-detection-job
.
- Amazon CLI
-
To start an asynchronous events detection job
The following
start-events-detection-job
example starts an asynchronous events detection job for all files located at the address specified by the--input-data-config
tag. Possible target event types includeBANKRUPCTY
,EMPLOYMENT
,CORPORATE_ACQUISITION
,INVESTMENT_GENERAL
,CORPORATE_MERGER
,IPO
,RIGHTS_ISSUE
,SECONDARY_OFFERING
,SHELF_OFFERING
,TENDER_OFFERING
, andSTOCK_SPLIT
. The S3 bucket in this example containsSampleText1.txt
,SampleText2.txt
, andSampleText3.txt
. When the job is complete, the folder,output
, is placed in the location specified by the--output-data-config
tag. The folder containsSampleText1.txt.out
,SampleText2.txt.out
, andSampleText3.txt.out
. The JSON output is printed on one line per file, but is formatted here for readability.aws comprehend start-events-detection-job \ --job-name
events-detection-1
\ --input-data-config"S3Uri=s3://DOC-EXAMPLE-BUCKET/EventsData"
\ --output-data-config"S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/"
\ --data-access-role-arnarn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-servicerole
\ --language-codeen
\ --target-event-types"BANKRUPTCY"
"EMPLOYMENT"
"CORPORATE_ACQUISITION"
"CORPORATE_MERGER"
"INVESTMENT_GENERAL"
Contents of
SampleText1.txt
:"Company AnyCompany grew by increasing sales and through acquisitions. After purchasing competing firms in 2020, AnyBusiness, a part of the AnyBusinessGroup, gave Jane Does firm a going rate of one cent a gallon or forty-two cents a barrel."
Contents of
SampleText2.txt
:"In 2021, AnyCompany officially purchased AnyBusiness for 100 billion dollars, surprising and exciting the shareholders."
Contents of
SampleText3.txt
:"In 2022, AnyCompany stock crashed 50. Eventually later that year they filed for bankruptcy."
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:events-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobStatus": "SUBMITTED" }
Contents of
SampleText1.txt.out
with line indents for readability:{ "Entities": [ { "Mentions": [ { "BeginOffset": 8, "EndOffset": 18, "Score": 0.99977, "Text": "AnyCompany", "Type": "ORGANIZATION", "GroupScore": 1 }, { "BeginOffset": 112, "EndOffset": 123, "Score": 0.999747, "Text": "AnyBusiness", "Type": "ORGANIZATION", "GroupScore": 0.979826 }, { "BeginOffset": 171, "EndOffset": 175, "Score": 0.999615, "Text": "firm", "Type": "ORGANIZATION", "GroupScore": 0.871647 } ] }, { "Mentions": [ { "BeginOffset": 97, "EndOffset": 102, "Score": 0.987687, "Text": "firms", "Type": "ORGANIZATION", "GroupScore": 1 } ] }, { "Mentions": [ { "BeginOffset": 103, "EndOffset": 110, "Score": 0.999458, "Text": "in 2020", "Type": "DATE", "GroupScore": 1 } ] }, { "Mentions": [ { "BeginOffset": 160, "EndOffset": 168, "Score": 0.999649, "Text": "John Doe", "Type": "PERSON", "GroupScore": 1 } ] } ], "Events": [ { "Type": "CORPORATE_ACQUISITION", "Arguments": [ { "EntityIndex": 0, "Role": "INVESTOR", "Score": 0.99977 } ], "Triggers": [ { "BeginOffset": 56, "EndOffset": 68, "Score": 0.999967, "Text": "acquisitions", "Type": "CORPORATE_ACQUISITION", "GroupScore": 1 } ] }, { "Type": "CORPORATE_ACQUISITION", "Arguments": [ { "EntityIndex": 1, "Role": "INVESTEE", "Score": 0.987687 }, { "EntityIndex": 2, "Role": "DATE", "Score": 0.999458 }, { "EntityIndex": 3, "Role": "INVESTOR", "Score": 0.999649 } ], "Triggers": [ { "BeginOffset": 76, "EndOffset": 86, "Score": 0.999973, "Text": "purchasing", "Type": "CORPORATE_ACQUISITION", "GroupScore": 1 } ] } ], "File": "SampleText1.txt", "Line": 0 }
Contents of
SampleText2.txt.out
:{ "Entities": [ { "Mentions": [ { "BeginOffset": 0, "EndOffset": 7, "Score": 0.999473, "Text": "In 2021", "Type": "DATE", "GroupScore": 1 } ] }, { "Mentions": [ { "BeginOffset": 9, "EndOffset": 19, "Score": 0.999636, "Text": "AnyCompany", "Type": "ORGANIZATION", "GroupScore": 1 } ] }, { "Mentions": [ { "BeginOffset": 45, "EndOffset": 56, "Score": 0.999712, "Text": "AnyBusiness", "Type": "ORGANIZATION", "GroupScore": 1 } ] }, { "Mentions": [ { "BeginOffset": 61, "EndOffset": 80, "Score": 0.998886, "Text": "100 billion dollars", "Type": "MONETARY_VALUE", "GroupScore": 1 } ] } ], "Events": [ { "Type": "CORPORATE_ACQUISITION", "Arguments": [ { "EntityIndex": 3, "Role": "AMOUNT", "Score": 0.998886 }, { "EntityIndex": 2, "Role": "INVESTEE", "Score": 0.999712 }, { "EntityIndex": 0, "Role": "DATE", "Score": 0.999473 }, { "EntityIndex": 1, "Role": "INVESTOR", "Score": 0.999636 } ], "Triggers": [ { "BeginOffset": 31, "EndOffset": 40, "Score": 0.99995, "Text": "purchased", "Type": "CORPORATE_ACQUISITION", "GroupScore": 1 } ] } ], "File": "SampleText2.txt", "Line": 0 }
Contents of
SampleText3.txt.out
:{ "Entities": [ { "Mentions": [ { "BeginOffset": 9, "EndOffset": 19, "Score": 0.999774, "Text": "AnyCompany", "Type": "ORGANIZATION", "GroupScore": 1 }, { "BeginOffset": 66, "EndOffset": 70, "Score": 0.995717, "Text": "they", "Type": "ORGANIZATION", "GroupScore": 0.997626 } ] }, { "Mentions": [ { "BeginOffset": 50, "EndOffset": 65, "Score": 0.999656, "Text": "later that year", "Type": "DATE", "GroupScore": 1 } ] } ], "Events": [ { "Type": "BANKRUPTCY", "Arguments": [ { "EntityIndex": 1, "Role": "DATE", "Score": 0.999656 }, { "EntityIndex": 0, "Role": "FILER", "Score": 0.995717 } ], "Triggers": [ { "BeginOffset": 81, "EndOffset": 91, "Score": 0.999936, "Text": "bankruptcy", "Type": "BANKRUPTCY", "GroupScore": 1 } ] } ], "File": "SampleText3.txt", "Line": 0 }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StartEventsDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use start-flywheel-iteration
.
- Amazon CLI
-
To start a flywheel iteration
The following
start-flywheel-iteration
example starts a flywheel iteration. This operation uses any new datasets in the flywheel to train a new model version.aws comprehend start-flywheel-iteration \ --flywheel-arn
arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel
Output:
{ "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel", "FlywheelIterationId": "12345123TEXAMPLE" }
For more information, see Flywheel overview
in the Amazon Comprehend Developer Guide. -
For API details, see StartFlywheelIteration
in Amazon CLI Command Reference.
-
The following code example shows how to use start-key-phrases-detection-job
.
- Amazon CLI
-
To start a key phrases detection job
The following
start-key-phrases-detection-job
example starts an asynchronous key phrases detection job for all files located at the address specified by the--input-data-config
tag. The S3 bucket in this example containsSampletext1.txt
,Sampletext2.txt
, andSampletext3.txt
. When the job is completed, the folder,output
, is placed in the location specified by the--output-data-config
tag. The folder contains the fileoutput.txt
which contains all the key phrases detected within each text file and the pre-trained model's confidence score for each prediction. The Json output is printed on one line per file, but is formatted here for readability.aws comprehend start-key-phrases-detection-job \ --job-name
keyphrasesanalysistest1
\ --language-codeen
\ --input-data-config"S3Uri=s3://DOC-EXAMPLE-BUCKET/"
\ --output-data-config"S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/"
\ --data-access-role-arn"arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role"
\ --language-codeen
Contents of
Sampletext1.txt
:"Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st."
Contents of
Sampletext2.txt
:"Dear Max, based on your autopay settings for your account Internet.org account, we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. "
Contents of
Sampletext3.txt
:"Jane, please submit any customer feedback from this weekend to Sunshine Spa, 123 Main St, Anywhere and send comments to Alice at AnySpa@example.com."
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobStatus": "SUBMITTED" }
Contents of
output.txt
with line indents for readibility:{ "File": "SampleText1.txt", "KeyPhrases": [ { "BeginOffset": 6, "EndOffset": 15, "Score": 0.9748965572679326, "Text": "Zhang Wei" }, { "BeginOffset": 22, "EndOffset": 26, "Score": 0.9997344722354619, "Text": "John" }, { "BeginOffset": 28, "EndOffset": 62, "Score": 0.9843791074032948, "Text": "Your AnyCompany Financial Services" }, { "BeginOffset": 64, "EndOffset": 107, "Score": 0.8976122401721824, "Text": "LLC credit card account 1111-XXXX-1111-XXXX" }, { "BeginOffset": 112, "EndOffset": 129, "Score": 0.9999612982629748, "Text": "a minimum payment" }, { "BeginOffset": 133, "EndOffset": 139, "Score": 0.99975728947036, "Text": "$24.53" }, { "BeginOffset": 155, "EndOffset": 164, "Score": 0.9940866241449973, "Text": "July 31st" } ], "Line": 0 } { "File": "SampleText2.txt", "KeyPhrases": [ { "BeginOffset": 0, "EndOffset": 8, "Score": 0.9974021100118472, "Text": "Dear Max" }, { "BeginOffset": 19, "EndOffset": 40, "Score": 0.9961120519515884, "Text": "your autopay settings" }, { "BeginOffset": 45, "EndOffset": 78, "Score": 0.9980620070116009, "Text": "your account Internet.org account" }, { "BeginOffset": 97, "EndOffset": 109, "Score": 0.999919660140754, "Text": "your payment" }, { "BeginOffset": 113, "EndOffset": 125, "Score": 0.9998370719754205, "Text": "the due date" }, { "BeginOffset": 131, "EndOffset": 166, "Score": 0.9955068678502509, "Text": "your bank account number XXXXXX1111" }, { "BeginOffset": 172, "EndOffset": 200, "Score": 0.8653433315829526, "Text": "the routing number XXXXX0000" } ], "Line": 0 } { "File": "SampleText3.txt", "KeyPhrases": [ { "BeginOffset": 0, "EndOffset": 4, "Score": 0.9142947833681668, "Text": "Jane" }, { "BeginOffset": 20, "EndOffset": 41, "Score": 0.9984325676596763, "Text": "any customer feedback" }, { "BeginOffset": 47, "EndOffset": 59, "Score": 0.9998782448150636, "Text": "this weekend" }, { "BeginOffset": 63, "EndOffset": 75, "Score": 0.99866741830757, "Text": "Sunshine Spa" }, { "BeginOffset": 77, "EndOffset": 88, "Score": 0.9695803485466054, "Text": "123 Main St" }, { "BeginOffset": 108, "EndOffset": 116, "Score": 0.9997065928550928, "Text": "comments" }, { "BeginOffset": 120, "EndOffset": 125, "Score": 0.9993466833825161, "Text": "Alice" }, { "BeginOffset": 129, "EndOffset": 144, "Score": 0.9654563612885667, "Text": "AnySpa@example.com" } ], "Line": 0 }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StartKeyPhrasesDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use start-pii-entities-detection-job
.
- Amazon CLI
-
To start an asynchronous PII detection job
The following
start-pii-entities-detection-job
example starts an asynchronous personal identifiable information (PII) entities detection job for all files located at the address specified by the--input-data-config
tag. The S3 bucket in this example containsSampletext1.txt
,Sampletext2.txt
, andSampletext3.txt
. When the job is complete, the folder,output
, is placed in the location specified by the--output-data-config
tag. The folder containsSampleText1.txt.out
,SampleText2.txt.out
, andSampleText3.txt.out
which list the named entities within each text file. The Json output is printed on one line per file, but is formatted here for readability.aws comprehend start-pii-entities-detection-job \ --job-name
entities_test
\ --language-codeen
\ --input-data-config"S3Uri=s3://DOC-EXAMPLE-BUCKET/"
\ --output-data-config"S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/"
\ --data-access-role-arnarn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role
\ --language-codeen
\ --modeONLY_OFFSETS
Contents of
Sampletext1.txt
:"Hello Zhang Wei, I am John. Your AnyCompany Financial Services, LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st."
Contents of
Sampletext2.txt
:"Dear Max, based on your autopay settings for your account Internet.org account, we will withdraw your payment on the due date from your bank account number XXXXXX1111 with the routing number XXXXX0000. "
Contents of
Sampletext3.txt
:"Jane, please submit any customer feedback from this weekend to Sunshine Spa, 123 Main St, Anywhere and send comments to Alice at AnySpa@example.com."
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:pii-entities-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobStatus": "SUBMITTED" }
Contents of
SampleText1.txt.out
with line indents for readability:{ "Entities": [ { "BeginOffset": 6, "EndOffset": 15, "Type": "NAME", "Score": 0.9998490510222595 }, { "BeginOffset": 22, "EndOffset": 26, "Type": "NAME", "Score": 0.9998937958019426 }, { "BeginOffset": 88, "EndOffset": 107, "Type": "CREDIT_DEBIT_NUMBER", "Score": 0.9554297245278491 }, { "BeginOffset": 155, "EndOffset": 164, "Type": "DATE_TIME", "Score": 0.9999720462925257 } ], "File": "SampleText1.txt", "Line": 0 }
Contents of
SampleText2.txt.out
with line indents for readability:{ "Entities": [ { "BeginOffset": 5, "EndOffset": 8, "Type": "NAME", "Score": 0.9994390774924007 }, { "BeginOffset": 58, "EndOffset": 70, "Type": "URL", "Score": 0.9999958276922101 }, { "BeginOffset": 156, "EndOffset": 166, "Type": "BANK_ACCOUNT_NUMBER", "Score": 0.9999721058045592 }, { "BeginOffset": 191, "EndOffset": 200, "Type": "BANK_ROUTING", "Score": 0.9998968945989909 } ], "File": "SampleText2.txt", "Line": 0 }
Contents of
SampleText3.txt.out
with line indents for readability:{ "Entities": [ { "BeginOffset": 0, "EndOffset": 4, "Type": "NAME", "Score": 0.999949934606805 }, { "BeginOffset": 77, "EndOffset": 88, "Type": "ADDRESS", "Score": 0.9999035300466904 }, { "BeginOffset": 120, "EndOffset": 125, "Type": "NAME", "Score": 0.9998203838716296 }, { "BeginOffset": 129, "EndOffset": 144, "Type": "EMAIL", "Score": 0.9998313473105228 } ], "File": "SampleText3.txt", "Line": 0 }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StartPiiEntitiesDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use start-sentiment-detection-job
.
- Amazon CLI
-
To start an asynchronous sentiment analysis job
The following
start-sentiment-detection-job
example starts an asynchronous sentiment analysis detection job for all files located at the address specified by the--input-data-config
tag. The S3 bucket folder in this example containsSampleMovieReview1.txt
,SampleMovieReview2.txt
, andSampleMovieReview3.txt
. When the job is complete, the folder,output
, is placed at the location specified by the--output-data-config
tag. The folder contains the file,output.txt
, which contains the prevailing sentiments for each text file and the pre-trained model's confidence score for each prediction. The Json output is printed on one line per file, but is formatted here for readability.aws comprehend start-sentiment-detection-job \ --job-name
example-sentiment-detection-job
\ --language-codeen
\ --input-data-config"S3Uri=s3://DOC-EXAMPLE-BUCKET/MovieData"
\ --output-data-config"S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/"
\ --data-access-role-arnarn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role
Contents of
SampleMovieReview1.txt
:"The film, AnyMovie2, is fairly predictable and just okay."
Contents of
SampleMovieReview2.txt
:"AnyMovie2 is the essential sci-fi film that I grew up watching when I was a kid. I highly recommend this movie."
Contents of
SampleMovieReview3.txt
:"Don't get fooled by the 'awards' for AnyMovie2. All parts of the film were poorly stolen from other modern directors."
Output:
{ "JobId": "0b5001e25f62ebb40631a9a1a7fde7b3", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:sentiment-detection-job/0b5001e25f62ebb40631a9a1a7fde7b3", "JobStatus": "SUBMITTED" }
Contents of
output.txt
with line of indents for readability:{ "File": "SampleMovieReview1.txt", "Line": 0, "Sentiment": "MIXED", "SentimentScore": { "Mixed": 0.6591159105300903, "Negative": 0.26492202281951904, "Neutral": 0.035430654883384705, "Positive": 0.04053137078881264 } } { "File": "SampleMovieReview2.txt", "Line": 0, "Sentiment": "POSITIVE", "SentimentScore": { "Mixed": 0.000008718466233403888, "Negative": 0.00006134175055194646, "Neutral": 0.0002941041602753103, "Positive": 0.9996358156204224 } } { "File": "SampleMovieReview3.txt", "Line": 0, "Sentiment": "NEGATIVE", "SentimentScore": { "Mixed": 0.004146667663007975, "Negative": 0.9645107984542847, "Neutral": 0.016559595242142677, "Positive": 0.014782938174903393 } } }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StartSentimentDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use start-targeted-sentiment-detection-job
.
- Amazon CLI
-
To start an asynchronous targeted sentiment analysis job
The following
start-targeted-sentiment-detection-job
example starts an asynchronous targeted sentiment analysis detection job for all files located at the address specified by the--input-data-config
tag. The S3 bucket folder in this example containsSampleMovieReview1.txt
,SampleMovieReview2.txt
, andSampleMovieReview3.txt
. When the job is complete,output.tar.gz
is placed at the location specified by the--output-data-config
tag.output.tar.gz
contains the filesSampleMovieReview1.txt.out
,SampleMovieReview2.txt.out
, andSampleMovieReview3.txt.out
, which each contain all of the named entities and associated sentiments for a single input text file.aws comprehend start-targeted-sentiment-detection-job \ --job-name
targeted_movie_review_analysis1
\ --language-codeen
\ --input-data-config"S3Uri=s3://DOC-EXAMPLE-BUCKET/MovieData"
\ --output-data-config"S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/"
\ --data-access-role-arnarn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role
Contents of
SampleMovieReview1.txt
:"The film, AnyMovie, is fairly predictable and just okay."
Contents of
SampleMovieReview2.txt
:"AnyMovie is the essential sci-fi film that I grew up watching when I was a kid. I highly recommend this movie."
Contents of
SampleMovieReview3.txt
:"Don't get fooled by the 'awards' for AnyMovie. All parts of the film were poorly stolen from other modern directors."
Output:
{ "JobId": "0b5001e25f62ebb40631a9a1a7fde7b3", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:targeted-sentiment-detection-job/0b5001e25f62ebb40631a9a1a7fde7b3", "JobStatus": "SUBMITTED" }
Contents of
SampleMovieReview1.txt.out
with line indents for readability:{ "Entities": [ { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "BeginOffset": 4, "EndOffset": 8, "Score": 0.994972, "GroupScore": 1, "Text": "film", "Type": "MOVIE", "MentionSentiment": { "Sentiment": "NEUTRAL", "SentimentScore": { "Mixed": 0, "Negative": 0, "Neutral": 1, "Positive": 0 } } } ] }, { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "BeginOffset": 10, "EndOffset": 18, "Score": 0.631368, "GroupScore": 1, "Text": "AnyMovie", "Type": "ORGANIZATION", "MentionSentiment": { "Sentiment": "POSITIVE", "SentimentScore": { "Mixed": 0.001729, "Negative": 0.000001, "Neutral": 0.000318, "Positive": 0.997952 } } } ] } ], "File": "SampleMovieReview1.txt", "Line": 0 }
Contents of
SampleMovieReview2.txt.out
line indents for readability:{ "Entities": [ { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "BeginOffset": 0, "EndOffset": 8, "Score": 0.854024, "GroupScore": 1, "Text": "AnyMovie", "Type": "MOVIE", "MentionSentiment": { "Sentiment": "POSITIVE", "SentimentScore": { "Mixed": 0, "Negative": 0, "Neutral": 0.000007, "Positive": 0.999993 } } }, { "BeginOffset": 104, "EndOffset": 109, "Score": 0.999129, "GroupScore": 0.502937, "Text": "movie", "Type": "MOVIE", "MentionSentiment": { "Sentiment": "POSITIVE", "SentimentScore": { "Mixed": 0, "Negative": 0, "Neutral": 0, "Positive": 1 } } }, { "BeginOffset": 33, "EndOffset": 37, "Score": 0.999823, "GroupScore": 0.999252, "Text": "film", "Type": "MOVIE", "MentionSentiment": { "Sentiment": "POSITIVE", "SentimentScore": { "Mixed": 0, "Negative": 0, "Neutral": 0.000001, "Positive": 0.999999 } } } ] }, { "DescriptiveMentionIndex": [ 0, 1, 2 ], "Mentions": [ { "BeginOffset": 43, "EndOffset": 44, "Score": 0.999997, "GroupScore": 1, "Text": "I", "Type": "PERSON", "MentionSentiment": { "Sentiment": "NEUTRAL", "SentimentScore": { "Mixed": 0, "Negative": 0, "Neutral": 1, "Positive": 0 } } }, { "BeginOffset": 80, "EndOffset": 81, "Score": 0.999996, "GroupScore": 0.52523, "Text": "I", "Type": "PERSON", "MentionSentiment": { "Sentiment": "NEUTRAL", "SentimentScore": { "Mixed": 0, "Negative": 0, "Neutral": 1, "Positive": 0 } } }, { "BeginOffset": 67, "EndOffset": 68, "Score": 0.999994, "GroupScore": 0.999499, "Text": "I", "Type": "PERSON", "MentionSentiment": { "Sentiment": "NEUTRAL", "SentimentScore": { "Mixed": 0, "Negative": 0, "Neutral": 1, "Positive": 0 } } } ] }, { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "BeginOffset": 75, "EndOffset": 78, "Score": 0.999978, "GroupScore": 1, "Text": "kid", "Type": "PERSON", "MentionSentiment": { "Sentiment": "NEUTRAL", "SentimentScore": { "Mixed": 0, "Negative": 0, "Neutral": 1, "Positive": 0 } } } ] } ], "File": "SampleMovieReview2.txt", "Line": 0 }
Contents of
SampleMovieReview3.txt.out
with line indents for readibility:{ "Entities": [ { "DescriptiveMentionIndex": [ 1 ], "Mentions": [ { "BeginOffset": 64, "EndOffset": 68, "Score": 0.992953, "GroupScore": 0.999814, "Text": "film", "Type": "MOVIE", "MentionSentiment": { "Sentiment": "NEUTRAL", "SentimentScore": { "Mixed": 0.000004, "Negative": 0.010425, "Neutral": 0.989543, "Positive": 0.000027 } } }, { "BeginOffset": 37, "EndOffset": 45, "Score": 0.999782, "GroupScore": 1, "Text": "AnyMovie", "Type": "ORGANIZATION", "MentionSentiment": { "Sentiment": "POSITIVE", "SentimentScore": { "Mixed": 0.000095, "Negative": 0.039847, "Neutral": 0.000673, "Positive": 0.959384 } } } ] }, { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "BeginOffset": 47, "EndOffset": 50, "Score": 0.999991, "GroupScore": 1, "Text": "All", "Type": "QUANTITY", "MentionSentiment": { "Sentiment": "NEUTRAL", "SentimentScore": { "Mixed": 0.000001, "Negative": 0.000001, "Neutral": 0.999998, "Positive": 0 } } } ] }, { "DescriptiveMentionIndex": [ 0 ], "Mentions": [ { "BeginOffset": 106, "EndOffset": 115, "Score": 0.542083, "GroupScore": 1, "Text": "directors", "Type": "PERSON", "MentionSentiment": { "Sentiment": "NEUTRAL", "SentimentScore": { "Mixed": 0, "Negative": 0, "Neutral": 1, "Positive": 0 } } } ] } ], "File": "SampleMovieReview3.txt", "Line": 0 }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StartTargetedSentimentDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use start-topics-detection-job
.
- Amazon CLI
-
To start a topics detection analysis job
The following
start-topics-detection-job
example starts an asynchronous topics detection job for all files located at the address specified by the--input-data-config
tag. When the job is complete, the folder,output
, is placed at the location specified by the--ouput-data-config
tag.output
contains topic-terms.csv and doc-topics.csv. The first output file, topic-terms.csv, is a list of topics in the collection. For each topic, the list includes, by default, the top terms by topic according to their weight. The second file,doc-topics.csv
, lists the documents associated with a topic and the proportion of the document that is concerned with the topic.aws comprehend start-topics-detection-job \ --job-name
example_topics_detection_job
\ --language-codeen
\ --input-data-config"S3Uri=s3://DOC-EXAMPLE-BUCKET/"
\ --output-data-config"S3Uri=s3://DOC-EXAMPLE-DESTINATION-BUCKET/testfolder/"
\ --data-access-role-arnarn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role
\ --language-codeen
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE", "JobArn": "arn:aws:comprehend:us-west-2:111122223333:key-phrases-detection-job/123456abcdeb0e11022f22a11EXAMPLE", "JobStatus": "SUBMITTED" }
For more information, see Topic Modeling
in the Amazon Comprehend Developer Guide. -
For API details, see StartTopicsDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use stop-dominant-language-detection-job
.
- Amazon CLI
-
To stop an asynchronous dominant language detection job
The following
stop-dominant-language-detection-job
example stops an in-progress, asynchronous dominant language detection job. If the current job state isIN_PROGRESS
the job is marked for termination and put into theSTOP_REQUESTED
state. If the job completes before it can be stopped, it is put into theCOMPLETED
state.aws comprehend stop-dominant-language-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE, "JobStatus": "STOP_REQUESTED" }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StopDominantLanguageDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use stop-entities-detection-job
.
- Amazon CLI
-
To stop an asynchronous entities detection job
The following
stop-entities-detection-job
example stops an in-progress, asynchronous entities detection job. If the current job state isIN_PROGRESS
the job is marked for termination and put into theSTOP_REQUESTED
state. If the job completes before it can be stopped, it is put into theCOMPLETED
state.aws comprehend stop-entities-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE, "JobStatus": "STOP_REQUESTED" }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StopEntitiesDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use stop-events-detection-job
.
- Amazon CLI
-
To stop an asynchronous events detection job
The following
stop-events-detection-job
example stops an in-progress, asynchronous events detection job. If the current job state isIN_PROGRESS
the job is marked for termination and put into theSTOP_REQUESTED
state. If the job completes before it can be stopped, it is put into theCOMPLETED
state.aws comprehend stop-events-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE, "JobStatus": "STOP_REQUESTED" }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StopEventsDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use stop-key-phrases-detection-job
.
- Amazon CLI
-
To stop an asynchronous key phrases detection job
The following
stop-key-phrases-detection-job
example stops an in-progress, asynchronous key phrases detection job. If the current job state isIN_PROGRESS
the job is marked for termination and put into theSTOP_REQUESTED
state. If the job completes before it can be stopped, it is put into theCOMPLETED
state.aws comprehend stop-key-phrases-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE, "JobStatus": "STOP_REQUESTED" }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StopKeyPhrasesDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use stop-pii-entities-detection-job
.
- Amazon CLI
-
To stop an asynchronous pii entities detection job
The following
stop-pii-entities-detection-job
example stops an in-progress, asynchronous pii entities detection job. If the current job state isIN_PROGRESS
the job is marked for termination and put into theSTOP_REQUESTED
state. If the job completes before it can be stopped, it is put into theCOMPLETED
state.aws comprehend stop-pii-entities-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE, "JobStatus": "STOP_REQUESTED" }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StopPiiEntitiesDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use stop-sentiment-detection-job
.
- Amazon CLI
-
To stop an asynchronous sentiment detection job
The following
stop-sentiment-detection-job
example stops an in-progress, asynchronous sentiment detection job. If the current job state isIN_PROGRESS
the job is marked for termination and put into theSTOP_REQUESTED
state. If the job completes before it can be stopped, it is put into theCOMPLETED
state.aws comprehend stop-sentiment-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE, "JobStatus": "STOP_REQUESTED" }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StopSentimentDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use stop-targeted-sentiment-detection-job
.
- Amazon CLI
-
To stop an asynchronous targeted sentiment detection job
The following
stop-targeted-sentiment-detection-job
example stops an in-progress, asynchronous targeted sentiment detection job. If the current job state isIN_PROGRESS
the job is marked for termination and put into theSTOP_REQUESTED
state. If the job completes before it can be stopped, it is put into theCOMPLETED
state.aws comprehend stop-targeted-sentiment-detection-job \ --job-id
123456abcdeb0e11022f22a11EXAMPLE
Output:
{ "JobId": "123456abcdeb0e11022f22a11EXAMPLE, "JobStatus": "STOP_REQUESTED" }
For more information, see Async analysis for Amazon Comprehend insights
in the Amazon Comprehend Developer Guide. -
For API details, see StopTargetedSentimentDetectionJob
in Amazon CLI Command Reference.
-
The following code example shows how to use stop-training-document-classifier
.
- Amazon CLI
-
To stop the training of a document classifier model
The following
stop-training-document-classifier
example stops the training of a document classifier model while in-progress.aws comprehend stop-training-document-classifier --document-classifier-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier
This command produces no output.
For more information, see Creating and managing custom models
in the Amazon Comprehend Developer Guide. -
For API details, see StopTrainingDocumentClassifier
in Amazon CLI Command Reference.
-
The following code example shows how to use stop-training-entity-recognizer
.
- Amazon CLI
-
To stop the training of an entity recognizer model
The following
stop-training-entity-recognizer
example stops the training of an entity recognizer model while in-progress.aws comprehend stop-training-entity-recognizer --entity-recognizer-arn
"arn:aws:comprehend:us-west-2:111122223333:entity-recognizer/examplerecognizer1"
This command produces no output.
For more information, see Creating and managing custom models
in the Amazon Comprehend Developer Guide. -
For API details, see StopTrainingEntityRecognizer
in Amazon CLI Command Reference.
-
The following code example shows how to use tag-resource
.
- Amazon CLI
-
Example 1: To tag a resource
The following
tag-resource
example adds a single tag to an Amazon Comprehend resource.aws comprehend tag-resource \ --resource-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1
\ --tagsKey=Location,Value=Seattle
This command has no output.
For more information, see Tagging your resources
in the Amazon Comprehend Developer Guide. Example 2: To add multiple tags to a resource
The following
tag-resource
example adds multiple tags to an Amazon Comprehend resource.aws comprehend tag-resource \ --resource-arn
"arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1"
\ --tagsKey=location,Value=Seattle
Key=Department,Value=Finance
This command has no output.
For more information, see Tagging your resources
in the Amazon Comprehend Developer Guide. -
For API details, see TagResource
in Amazon CLI Command Reference.
-
The following code example shows how to use untag-resource
.
- Amazon CLI
-
Example 1: To remove a single tag from a resource
The following
untag-resource
example removes a single tag from an Amazon Comprehend resource.aws comprehend untag-resource \ --resource-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1
--tag-keysLocation
This command produces no output.
For more information, see Tagging your resources
in the Amazon Comprehend Developer Guide. Example 2: To remove multiple tags from a resource
The following
untag-resource
example removes multiple tags from an Amazon Comprehend resource.aws comprehend untag-resource \ --resource-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/1
--tag-keysLocation
Department
This command produces no output.
For more information, see Tagging your resources
in the Amazon Comprehend Developer Guide. -
For API details, see UntagResource
in Amazon CLI Command Reference.
-
The following code example shows how to use update-endpoint
.
- Amazon CLI
-
Example 1: To update an endpoint's inference units
The following
update-endpoint
example updates information about an endpoint. In this example, the number of inference units is increased.aws comprehend update-endpoint \ --endpoint-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint
--desired-inference-units2
This command produces no output.
For more information, see Managing Amazon Comprehend endpoints
in the Amazon Comprehend Developer Guide. Example 2: To update an endpoint's actie model
The following
update-endpoint
example updates information about an endpoint. In this example, the active model is changed.aws comprehend update-endpoint \ --endpoint-arn
arn:aws:comprehend:us-west-2:111122223333:document-classifier-endpoint/example-classifier-endpoint
--active-model-arnarn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier-new
This command produces no output.
For more information, see Managing Amazon Comprehend endpoints
in the Amazon Comprehend Developer Guide. -
For API details, see UpdateEndpoint
in Amazon CLI Command Reference.
-
The following code example shows how to use update-flywheel
.
- Amazon CLI
-
To update a flywheel configuration
The following
update-flywheel
example updates a flywheel configuration. In this example, the active model for the flywheel is updated.aws comprehend update-flywheel \ --flywheel-arn
arn:aws:comprehend:us-west-2:111122223333:flywheel/example-flywheel-1
\ --active-model-arnarn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/new-example-classifier-model
Output:
{ "FlywheelProperties": { "FlywheelArn": "arn:aws:comprehend:us-west-2:111122223333:flywheel/flywheel-entity", "ActiveModelArn": "arn:aws:comprehend:us-west-2:111122223333:document-classifier/example-classifier/version/new-example-classifier-model", "DataAccessRoleArn": "arn:aws:iam::111122223333:role/service-role/AmazonComprehendServiceRole-example-role", "TaskConfig": { "LanguageCode": "en", "DocumentClassificationConfig": { "Mode": "MULTI_CLASS" } }, "DataLakeS3Uri": "s3://DOC-EXAMPLE-BUCKET/flywheel-entity/schemaVersion=1/20230616T200543Z/", "DataSecurityConfig": {}, "Status": "ACTIVE", "ModelType": "DOCUMENT_CLASSIFIER", "CreationTime": "2023-06-16T20:05:43.242000+00:00", "LastModifiedTime": "2023-06-19T04:00:43.027000+00:00", "LatestFlywheelIteration": "20230619T040032Z" } }
For more information, see Flywheel overview
in the Amazon Comprehend Developer Guide. -
For API details, see UpdateFlywheel
in Amazon CLI Command Reference.
-