# Train custom entity recognizers (API)
<a name="train-cer-model"></a>

To create and train a custom entity recognition model, use the Amazon Comprehend [CreateEntityRecognizer](https://docs.aws.amazon.com/comprehend/latest/APIReference/API_CreateEntityRecognizer.html) API operation

**Topics**
+ [Training custom entity recognizers using the AWS Command Line Interface](#get-started-api-cer-cli)
+ [Training custom entity recognizers using the AWS SDK for Java](#get-started-api-cer-java)
+ [Training custom entity recognizers using Python (Boto3)](#cer-python)

## Training custom entity recognizers using the AWS Command Line Interface
<a name="get-started-api-cer-cli"></a>

The following examples demonstrate using the `CreateEntityRecognizer` operation and other associated APIs with the AWS CLI. 

The examples are formatted for Unix, Linux, and macOS. For Windows, replace the backslash (\$1) Unix continuation character at the end of each line with a caret (^).

Create a custom entity recognizer using the `create-entity-recognizer` CLI command. For information about the input-data-config parameter, see [CreateEntityRecognizer](https://docs.aws.amazon.com/comprehend/latest/APIReference/API_CreateEntityRecognizer.html) in the *Amazon Comprehend API Reference*.

```
aws comprehend create-entity-recognizer \
     --language-code en \
     --recognizer-name test-6 \
     --data-access-role-arn "arn:aws:iam::account number:role/service-role/AmazonComprehendServiceRole-role" \
     --input-data-config "EntityTypes=[{Type=PERSON}],Documents={S3Uri=s3://Bucket Name/Bucket Path/documents},
                Annotations={S3Uri=s3://Bucket Name/Bucket Path/annotations}" \
     --region region
```

List all entity recognizers in a Region using the `list-entity-recognizers` CLI command..

```
aws comprehend list-entity-recognizers \
     --region region
```

Check Job Status of custom entity recognizers using the `describe-entity-recognizer` CLI command..

```
aws comprehend describe-entity-recognizer \
     --entity-recognizer-arn arn:aws:comprehend:region:account number:entity-recognizer/test-6 \
     --region region
```

## Training custom entity recognizers using the AWS SDK for Java
<a name="get-started-api-cer-java"></a>

This example creates a custom entity recognizer and trains the model, using Java

For Amazon Comprehend examples that use Java, see [Amazon Comprehend Java examples](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/example_code/comprehend).

## Training custom entity recognizers using Python (Boto3)
<a name="cer-python"></a>

Instantiate Boto3 SDK: 

```
import boto3
import uuid
comprehend = boto3.client("comprehend", region_name="region")
```

Create entity recognizer: 

```
response = comprehend.create_entity_recognizer(
    RecognizerName="Recognizer-Name-Goes-Here-{}".format(str(uuid.uuid4())),
    LanguageCode="en",
    DataAccessRoleArn="Role ARN",
    InputDataConfig={
        "EntityTypes": [
            {
                "Type": "ENTITY_TYPE"
            }
        ],
        "Documents": {
            "S3Uri": "s3://Bucket Name/Bucket Path/documents"
        },
        "Annotations": {
            "S3Uri": "s3://Bucket Name/Bucket Path/annotations"
        }
    }
)
recognizer_arn = response["EntityRecognizerArn"]
```

List all recognizers: 

```
response = comprehend.list_entity_recognizers()
```

Wait for recognizer to reach TRAINED status: 

```
while True:
    response = comprehend.describe_entity_recognizer(
        EntityRecognizerArn=recognizer_arn
    )

    status = response["EntityRecognizerProperties"]["Status"]
    if "IN_ERROR" == status:
        sys.exit(1)
    if "TRAINED" == status:
        break

    time.sleep(10)
```