Datasaur
Visit our websitePricingBlogPlaygroundAPI Docs
  • Welcome to Datasaur
    • Getting started with Datasaur
  • Data Studio Projects
    • Labeling Task Types
      • Span Based
        • OCR Labeling
        • Audio Project
      • Row Based
      • Document Based
      • Bounding Box
      • Conversational
      • Mixed Labeling
      • Project Templates
        • Test Project
    • Creating a Project
      • Data Formats
      • Data Samples
      • Split Files
      • Consensus
      • Dynamic Review Capabilities
    • Pre-Labeled Project
    • Let's Get Labeling!
      • Span Based
        • Span + Line Labeling
      • Row & Document Based
      • Bounding Box Labeling
      • Conversational Labeling
      • Label Sets / Question Sets
        • Dynamic Question Set
      • Multiple Label Sets
    • Reviewing Projects
      • Review Sampling
    • Adding Documents to an Ongoing Project
    • Export Project
  • LLM Projects
    • LLM Labs Introduction
    • Sandbox
      • Direct Access LLMs
      • File Attachment
      • Conversational Prompt
    • Deployment
      • Deployment API
    • Knowledge base
      • External Object Storage
      • File Properties
    • Models
      • Amazon SageMaker JumpStart
      • Amazon Bedrock
      • Open AI
      • Azure OpenAI
      • Vertex AI
      • Custom model
      • Fine-tuning
      • LLM Comparison Table
    • Evaluation
      • Automated Evaluation
        • Multi-application evaluation
        • Custom metrics
      • Ranking (RLHF)
      • Rating
      • Performance Monitoring
    • Dataset
    • Pricing Plan
  • Workspace Management
    • Workspace
    • Role & Permission
    • Analytics
      • Inter-Annotator Agreement (IAA)
        • Cohen's Kappa Calculation
        • Krippendorff's Alpha Calculation
      • Custom Report Builder
      • Project Report
      • Evaluation Metrics
    • Activity
    • File Transformer
      • Import Transformer
      • Export Transformer
      • Upload File Transformer
      • Running File Transformer
    • Label Management
      • Label Set Management
      • Question Set Management
    • Project Management
      • Self-Assignment
        • Self-Unassign
      • Transfer Assignment Ownership
      • Reset Labeling Work
      • Mark Document as Complete
      • Project Status Workflow
        • Read-only Mode
      • Comment Feature
      • Archive Project
    • Automation
      • Action: Create Projects
  • Assisted Labeling
    • ML Assisted Labeling
      • Amazon Comprehend
      • Amazon SageMaker
      • Azure ML
      • CoreNLP NER
      • CoreNLP POS
      • Custom API
      • FewNERD
      • Google Vertex AI
      • Hugging Face
      • LLM Assisted Labeling
        • Prompt Examples
        • Custom Provider
      • LLM Labs (beta)
      • NLTK
      • Sentiment Analysis
      • spaCy
      • SparkNLP NER
      • SparkNLP POS
    • Data Programming
      • Example of Labeling Functions
      • Labeling Function Analysis
      • Inter-Annotator Agreement for Data Programming
    • Predictive Labeling
  • Assisted Review
    • Label Error Detection
  • Building Your Own Model
    • Datasaur Dinamic
      • Datasaur Dinamic with Hugging Face
      • Datasaur Dinamic with Amazon SageMaker Autopilot
  • Advanced
    • Script-Generated Question
    • Shortcuts
    • Extensions
      • Labels
      • Review
      • Document and Row Labeling
      • Bounding Box Labels
      • List of Files
      • Comments
      • Analytics
      • Dictionary
      • Search
      • Labeling Guidelines
      • Metadata
      • Grammar Checker
      • ML Assisted Labeling
      • Data Programming
      • Datasaur Dinamic
      • Predictive Labeling
      • Label Error Detection
      • LLM Sandbox
    • Tokenizers
  • Integrations
    • External Object Storage
      • AWS S3
        • With IRSA
      • Google Cloud Storage
      • Azure Blob Storage
    • SAML
      • Okta
      • Microsoft Entra ID
    • SCIM
      • Okta
      • Microsoft Entra ID
    • Webhook Notifications
      • Webhook Signature
      • Events
      • Custom Headers
    • Robosaur
      • Commands
        • Create Projects
        • Apply Project Tags
        • Export Projects
        • Generate Time Per Task Report
        • Split Document
      • Storage Options
  • API
    • Datasaur APIs
    • Credentials
    • Create Project
      • New mutation (createProject)
      • Python Script Example
    • Adding Documents
    • Labeling
      • Create Label Set
      • Add Label Sets into Existing Project
      • Get List of Label Sets in a Project
      • Add Label Set Item into Project's Label Set
      • Programmatic API Labeling
      • Inserting Span and Arrow Label into Document
    • Export Project
      • Custom Webhook
    • Get Data
      • Get List of Projects
      • Get Document Information
      • Get List of Tags
      • Get Cabinet
      • Export Team Overview
      • Check Job
    • Custom OCR
      • Importable Format
    • Custom ASR
    • Run ML-Assisted Labeling
  • Security and Compliance
    • Security and Compliance
      • 2FA
  • Compatibility & Updates
    • Common Terminology
    • Recommended Machine Specifications
    • Supported Formats
    • Supported Languages
    • Release Notes
      • Version 6
        • 6.111.0
        • 6.110.0
        • 6.109.0
        • 6.108.0
        • 6.107.0
        • 6.106.0
        • 6.105.0
        • 6.104.0
        • 6.103.0
        • 6.102.0
        • 6.101.0
        • 6.100.0
        • 6.99.0
        • 6.98.0
        • 6.97.0
        • 6.96.0
        • 6.95.0
        • 6.94.0
        • 6.93.0
        • 6.92.0
        • 6.91.0
        • 6.90.0
        • 6.89.0
        • 6.88.0
        • 6.87.0
        • 6.86.0
        • 6.85.0
        • 6.84.0
        • 6.83.0
        • 6.82.0
        • 6.81.0
        • 6.80.0
        • 6.79.0
        • 6.78.0
        • 6.77.0
        • 6.76.0
        • 6.75.0
        • 6.74.0
        • 6.73.0
        • 6.72.0
        • 6.71.0
        • 6.70.0
        • 6.69.0
        • 6.68.0
        • 6.67.0
        • 6.66.0
        • 6.65.0
        • 6.64.0
        • 6.63.0
        • 6.62.0
        • 6.61.0
        • 6.60.0
        • 6.59.0
        • 6.58.0
        • 6.57.0
        • 6.56.0
        • 6.55.0
        • 6.54.0
        • 6.53.0
        • 6.52.0
        • 6.51.0
        • 6.50.0
        • 6.49.0
        • 6.48.0
        • 6.47.0
        • 6.46.0
        • 6.45.0
        • 6.44.0
        • 6.43.0
        • 6.42.0
        • 6.41.0
        • 6.40.0
        • 6.39.0
        • 6.38.0
        • 6.37.0
        • 6.36.0
        • 6.35.0
        • 6.34.0
        • 6.33.0
        • 6.32.0
        • 6.31.0
        • 6.30.0
        • 6.29.0
        • 6.28.0
        • 6.27.0
        • 6.26.0
        • 6.25.0
        • 6.24.0
        • 6.23.0
        • 6.22.0
        • 6.21.0
        • 6.20.0
        • 6.19.0
        • 6.18.0
        • 6.17.0
        • 6.16.0
        • 6.15.0
        • 6.14.0
        • 6.13.0
        • 6.12.0
        • 6.11.0
        • 6.10.0
        • 6.9.0
        • 6.8.0
        • 6.7.0
        • 6.6.0
        • 6.5.0
        • 6.4.0
        • 6.3.0
        • 6.2.0
        • 6.1.0
        • 6.0.0
      • Version 5
        • 5.63.0
        • 5.62.0
        • 5.61.0
        • 5.60.0
  • Deployment
    • Self-Hosted
      • AWS Marketplace
        • Data Studio
        • LLM Labs
Powered by GitBook
On this page
  • Setup Datasaur ML Assisted Extension
  • Setup Role in AWS Identity and Access Management (IAM)
  • Start Prediction
  1. Assisted Labeling
  2. ML Assisted Labeling

Amazon Comprehend

Enable your integration with models from Amazon Comprehend.

Last updated 13 days ago

Supported Labeling Types: Row Labeling

We have simplified the process of connecting our system with the Amazon Comprehend model. If you already have data and training pipelines in Amazon Comprehend, you can utilize your trained model to enhance the performance.

To use the Amazon Comprehend model, follow these steps:

Setup Datasaur ML Assisted Extension

Please make sure you have access to our ML Assisted Extension with AWS Comprehend as a provider.

  1. for Row Labeling

  2. Click "Manage Extension" on your right bar.

  3. Pop Up Manage Extension will appear and you can enable the Datasaur ML Assisted.

  1. Datasaur ML Assisted is now enabled and select Amazon Comprehend as Provider

Setup Role in AWS Identity and Access Management (IAM)

Before we start to create Role in the IAM Page, please create these policies first for your permissions when creating a new Role.

  1. Go to your IAM Page, navigate to the Policies section, and then create a new policy.

  2. You can download the examples below.

Now you can continue to create a new Role with the policy.

  1. Go to the IAM Page and navigate to the Roles section

  2. Click on "Create Roles" then select "AWS Account" as the role type and insert the Account ID and External ID. These values are automatically generated within the Datasaur ML Assisted and can be copied and pasted into the AWS platform.

  1. After finishing the first step, you can see the "Add permissions" step and search the policy you created before. Add them to the permissions.

  1. Provide a role name and click “Create role.”

  2. The role will be created successfully.

  3. View your role and copy the Role ARN.

  4. Paste this information into the ML-Assisted Labeling extension.

  5. Navigate to the Amazon Comprehend page and retrieve the Endpoint ARN. Copy the obtained Endpoint ARN and paste it into the extension.

Start Prediction

Once you have configured the above options, you can predict labels and obtain predicted labels from your own model by clicking “Predict Labels”.

Create a custom project
253B
comprehend-policy (1).txt
Manage Extension Pop Up
ML Assisted with Amazon Comprehend
Select trusted entity
Add Permissions
Role ARN
Endpoint Details
Prediction Finish!
Image of ML Assisted with Amazon Comprehend
Image of Select trusted entity Menu
Image of Add permissions page
Image of Role Creation
Image of Role Menu to copy Role ARN
Image of Endpoint Details
Image of Prediction result