Train custom classifiers (console)

You can create and train a custom classifier using the console, and then use the custom classifier to analyze your documents.

To train a custom classifier, you need a set of training documents. You label these documents with the categories that you want the document classifier to recognize. For information about preparing your training documents, see Preparing classifier training data.

To create and train a document classifier model

1. Sign in to the AWS Management Console and open the Amazon Comprehend console at https://console.aws.amazon.com/comprehend/

2. From the left menu, choose Customization and then choose Custom Classification.

3. Choose Create new model.

4. Under Model settings, enter a model name for the classifier. The name must be unique within your account and current Region.

   (Optional) Enter a version name. The name must be unique within your account and current Region.

5. Select the language of the training documents. To see the languages that classifiers support, see Training classification models.

6. (Optional) If you want to encrypt the data in the storage volume while Amazon Comprehend processes your training job, choose Classifier encryption. Then choose whether to use a KMS key associated with your current account, or one from another account.

   • If you are using a key associated with the current account, choose the key ID for KMS key ID.

   • If you are using a key associated with a different account, enter the ARN for the key ID under KMS key ARN.

   Note

   For more information on creating and using KMS keys and the associated encryption, see AWS Key Management Service (AWS KMS).

7. Under Data specifications, choose the Training model type to use.

   • Plain text documents: Choose this option to create a plain text model. Train the model using plain text documents.

   • Native documents: Choose this option to create a native document model. Train the model using native documents (PDF, Word, images).

8. Choose the Data format of your training data. For information about the data formats, see Classifier training file formats.

   • CSV file: Choose this option if your training data uses the CSV file format.

   • Augmented manifest: Choose this option if you used Ground Truth to create augmented manifest files for your training data. This format is available if you chose Plain text documents as the training model type.

9. Choose the Classifier mode to use.

   • Single-label mode: Choose this mode if the categories you're assigning to documents are mutually exclusive and you're training your classifier to assign one label to each document. In the Amazon Comprehend API, single-label mode is known as multi-class mode.

   • Multi-label mode: Choose this mode if multiple categories can be applied to a document at the same time, and you are training your classifier to assign one or more labels to each document.

10. If you choose Multi-label mode, you can select the Delimiter for labels. Use this delimiter character to separate labels when there are multiple classes for a training document. The default delimiter is the pipe character.

11. (Optional) If you chose Augmented manifest as the data format, you can input up to five augmented manifest files. Each augmented manifest file contains either a training dataset or a test dataset. You must provide at least one training dataset. Test datasets are optional. Use the following steps to configure the augmented manifest files:

    1. Under Training and test dataset, expand the Input location panel.

    2. In Dataset type, choose Training data or Test data.

    3. For the SageMaker Ground Truth augmented manifest file S3 location, enter the location of the Amazon S3 bucket that contains the manifest file or navigate to it by choosing Browse S3. The IAM role that you're using for access permissions for the training job must have read permissions for the S3 bucket.

    4. For the Attribute names, enter the name of the attribute that contains your annotations. If the file contains annotations from multiple chained labeling jobs, add an attribute for each job.

    5. To add another input location, choose Add input location and then configure the next location.

12. (Optional) If you chose CSV file as the data format, use the following steps to configure the training dataset and optional test dataset:

    1. Under Training dataset, enter the location of the Amazon S3 bucket that contains your training data CSV file or navigate to it by choosing Browse S3. The IAM role that you're using for access permissions for the training job must have read permissions for the S3 bucket.

       (Optional) If you chose Native documents as the training model type, you also provide the URL of the Amazon S3 folder that contains the training example files.

    2. Under Test dataset, select whether you are providing extra data for Amazon Comprehend to test the trained model.

       • Autosplit: Autosplit automatically selects 10% of your training data to reserve for use as testing data.

       • (Optional) Customer provided: Enter the URL of the test data CSV file in Amazon S3. You can also navigate to its location in Amazon S3 and choose Select folder.

         (Optional) If you chose Native documents as the training model type, you also provide the URL of the Amazon S3 folder that contains the test files.

13. (Optional) For Document read mode, you can override the default text extraction actions. This option isn't required for plain-text models, as it applies to text extraction for scanned documents. For more information, see Setting text extraction options.

14. (Optional for plain-text models) For Output data, enter the location of an Amazon S3 bucket to save training output data, such as the confusion matrix. For more information, see Confusion matrix.

    (Optional) If you choose to encrypt the output result from your training job, choose Encryption. Then choose whether to use a KMS key associated with the current account, or one from another account.

    • If you are using a key associated with the current account, choose the key alias for KMS key ID.

    • If you are using a key associated with a different account, enter the ARN for the key alias or ID under KMS key ID.

15. For IAM role, choose Choose an existing IAM role, and then choose an existing IAM role that has read permissions for the S3 bucket that contains your training documents. The role must have a trust policy that begins with comprehend.amazonaws.com to be valid.

    If you don't already have an IAM role with these permissions, choose Create an IAM role to make one. Choose the access permissions to grant this role, and then choose a name suffix to distinguish the role from IAM roles in your account.

    Note

    For encrypted input documents, the IAM role used must also have kms:Decrypt permission. For more information, see Permissions required to use KMS encryption.

16. (Optional) To launch your resources into Amazon Comprehend from a VPC, enter the VPC ID under VPC or choose the ID from the dropdown list.

    1. Choose the subnet under Subnets(s). After you select the first subnet, you can choose additional ones.

    2. Under Security Group(s), choose the security group to use if you specified one. After you select the first security group, you can choose additional ones.

    Note

    When you use a VPC with your classification job, the DataAccessRole used for the Create and Start operations must have permissions to the VPC that accesses the input documents and the output bucket.

17. (Optional) To add a tag to the custom classifier, enter a key-value pair under Tags. Choose Add tag. To remove this pair before creating the classifier, choose Remove tag. For more information, see Tagging your resources.

18. Choose Create.

The console displays the Classifiers page. The new classifier appears in the table, showing Submitted as its status. When the classifier starts processing the training documents, the status changes to Training. When a classifier is ready to use, the status changes to Trained or Trained with warnings. If the status is TRAINED_WITH_WARNINGS, review the skipped files folder in the Classifier training output.

If Amazon Comprehend encountered errors during creation or training, the status changes to In error. You can choose a classifier job in the table to get more information about the classifier, including any error messages.