Path: blob/develop/awscli/examples/comprehend/batch-detect-entities.rst
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**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 comprehend batch-detect-entities \
--language-code en \
--text-list "Dear Jane, Your AnyCompany Financial Services LLC credit card account 1111-XXXX-1111-XXXX has a minimum payment of $24.53 that is due by July 31st." "Please send customer feedback to Sunshine Spa, 123 Main St, Anywhere or to Alice at [email protected]."
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": "[email protected]",
"BeginOffset": 84,
"EndOffset": 99
}
]
}
],
"ErrorList": []
}
For more information, see `Entities <https://docs.aws.amazon.com/comprehend/latest/dg/how-entities.html>`__ in the *Amazon Comprehend Developer Guide*.