Datasaur
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  • 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
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On this page
  • Introduction
  • Key Features
  • Supported Service Providers
  1. Building Your Own Model

Datasaur Dinamic

Datasaur Dinamic enables data labeling to train a model natively. It creates a ready-to-use model directly from Datasaur.

Last updated 6 months ago

Introduction

Datasaur Dinamic is an ideal solution for users who prefer to obtain a trained model directly rather than labeled data. It is integrated with Amazon SageMaker and Hugging Face AutoTrain. It supports row-based tasks with Amazon SageMaker and Hugging Face AutoTrain, and token-based tasks exclusively with Hugging Face AutoTrain.

Key Features

Using Datasaur Dinamic offers several benefits:

  1. Automated Model Training: Streamline the process of training machine learning models using labeled data.

  2. Efficient Labeling: Use the trained models for ML-assisted labeling, improving the efficiency and accuracy of future labeling tasks.

  3. Integration with Applications: Deploy the trained models directly into applications, enhancing functionality with precise data insights.

Supported Service Providers

Please ensure that you have a minimum of 10 rows or sentences of labeled data for effective utilization of Datasaur Dinamic.

Service Providers
Supported Project Type
Configurations

Row Based

Row Based and Span Based

Amazon SageMaker
Amazon SageMaker Configuration
Hugging Face Auto Train
Hugging Face Configuration