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  • Tonic Textual guide
  • Getting started with Textual
  • Previewing Textual detection and redaction
  • Entity types that Textual detects
    • Built-in entity types
    • Managing custom entity types
  • Language support in Textual
  • Datasets - Create redacted files
    • Datasets workflow for text redaction
    • Creating and managing datasets
    • Assigning tags to datasets
    • Displaying the file manager
    • Adding and removing dataset files
    • Reviewing the sensitivity detection results
    • Configuring the redaction
      • Configuring added and excluded values for built-in entity types
      • Working with custom entity types
      • Selecting the handling option for entity types
      • Configuring synthesis options
      • Configuring handling of file components
    • Adding manual overrides to PDF files
      • Editing an individual PDF file
      • Creating templates to apply to PDF files
    • Sharing dataset access
    • Previewing the original and redacted data in a file
    • Downloading redacted data
  • Pipelines - Prepare LLM content
    • Pipelines workflow for LLM preparation
    • Viewing pipeline lists and details
    • Assigning tags to pipelines
    • Setting up pipelines
      • Creating and editing pipelines
      • Supported file types for pipelines
      • Creating custom entity types from a pipeline
      • Configuring file synthesis for a pipeline
      • Configuring an Amazon S3 pipeline
      • Configuring a Databricks pipeline
      • Configuring an Azure pipeline
      • Configuring a Sharepoint pipeline
      • Selecting files for an uploaded file pipeline
    • Starting a pipeline run
    • Sharing pipeline access
    • Viewing pipeline results
      • Viewing pipeline files, runs, and statistics
      • Displaying details for a processed file
      • Structure of the pipeline output file JSON
    • Downloading and using pipeline output
  • Textual Python SDK
    • Installing the Textual SDK
    • Creating and revoking Textual API keys
    • Obtaining JWT tokens for authentication
    • Instantiating the SDK client
    • Datasets and redaction
      • Create and manage datasets
      • Redact individual strings
      • Redact individual files
      • Transcribe and redact an audio file
      • Configure entity type handling for redaction
      • Record and review redaction requests
    • Pipelines and parsing
      • Create and manage pipelines
      • Parse individual files
  • Textual REST API
    • About the Textual REST API
    • REST API authentication
    • Redaction
      • Redact text strings
  • Datasets
    • Manage datasets
    • Manage dataset files
  • Snowflake Native App and SPCS
    • About the Snowflake Native App
    • Setting up the app
    • Using the app
    • Using Textual with Snowpark Container Services directly
  • Install and administer Textual
    • Textual architecture
    • Setting up and managing a Textual Cloud pay-as-you-go subscription
    • Deploying a self-hosted instance
      • System requirements
      • Deploying with Docker Compose
      • Deploying on Kubernetes with Helm
    • Configuring Textual
      • How to configure Textual environment variables
      • Configuring the number of textual-ml workers
      • Configuring the number of jobs to run concurrently
      • Configuring the format of Textual logs
      • Setting a custom certificate
      • Configuring endpoint URLs for calls to AWS
      • Enabling PDF and image processing
      • Setting the S3 bucket for file uploads and redactions
      • Required IAM role permissions for Amazon S3
      • Configuring model preferences
    • Viewing model specifications
    • Managing user access to Textual
      • Textual organizations
      • Creating a new account in an existing organization
      • Single sign-on (SSO)
        • Viewing the list of SSO groups in Textual
        • Azure
        • GitHub
        • Google
        • Keycloak
        • Okta
      • Managing Textual users
      • Managing permissions
        • About permissions and permission sets
        • Built-in permission sets and available permissions
        • Viewing the lists of permission sets
        • Configuring custom permission sets
        • Configuring access to global permission sets
        • Setting initial access to all global permissions
    • Textual monitoring
      • Downloading a usage report
      • Tracking user access to Textual
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  • Configuring whether to use an auxiliary model
  • Entity types that the auxiliary model detects
  • Indicating whether to use the auxiliary model
  • Configuring model use for GPU
  • Indicating whether to use the auxiliary model for GPU
  • Indicating whether to use the date synthesis model for GPU

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  1. Install and administer Textual
  2. Configuring Textual

Configuring model preferences

On a self-hosted instance, you can configure settings to determine whether to the auxiliary model, and model use on GPU.

Configuring whether to use an auxiliary model

To improve overall inference, you can configure whether Textual uses the en_core_web_sm auxiliary NER model.

Entity types that the auxiliary model detects

The auxiliary model detects the following types:

  • EVENT

  • LANGUAGE

  • LAW

  • NRP

  • NUMERIC_VALUE

  • PRODUCT

  • WORK_OF_ART

Indicating whether to use the auxiliary model

To configure whether to use the auxiliary model, you use the environment variable TEXTUAL_AUX_MODEL.

The available values are:

  • en_core_web_sm - This is the default value.

  • none - Indicates to not use the auxiliary model.

Configuring model use for GPU

When you use a textual-ml-gpu container on accelerated hardware, you can configure:

  • Whether to use the auxiliary model,

  • Whether to use the date synthesis model

Indicating whether to use the auxiliary model for GPU

By default, on GPU, Textual does not use the auxiliary model, and TEXTUAL_AUX_MODEL_GPU is false.

To use the auxiliary model for GPU, based on the configuration of TEXTUAL_AUX_MODEL, set TEXTUAL_AUX_MODEL_GPU to true.

When TEXTUAL_AUX_MODEL_GPU is true, and TEXTUAL_MULTI_LINGUAL is true, Textual also loads the multilingual models on GPU.

Indicating whether to use the date synthesis model for GPU

By default, on GPU, Textual loads the date synthesis model on GPU.

Note that this model requires 600MB of GPU RAM for each machine learning worker.

Last updated 4 months ago

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To configure whether to use the auxiliary model for GPU, you configure the TEXTUAL_AUX_MODEL_GPU.

To not load the date synthesis model on GPU, set the TEXTUAL_DATE_SYNTH_GPU to false.

environment variable
environment variable