Training Models

Training Models

Trainer

Using features for Semi-structured documents This article mentions features used in the processing of Semi-structured documents. Your access to those features depends on your license package and pricing plan.

Model Management

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Training Data Management

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TDM for Identification Models

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TDM for Classification Models

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Training Data Curator

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Document Eligibility Filtering

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Labeling Anomaly Detection

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Text Segmentation

Using features for Semi-structured documents This article mentions features used in the processing of Semi-structured documents.

Requirements for Training a New Model

Using features for Semi-structured documents This article mentions features used in the processing of Semi-structured documents.

Training a Structured Model

Hyperscience extracts data from documents and converts them into a machine-readable format. We support Structured, Semi-structured, and Additional documents.

Training a Semi-structured Model

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Training a New Field Identification Model

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Training a New Table Identification Model

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Training a Classification Model

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Canceling or Retrying a Training Job

Using features for Semi-structured documents This article mentions features used in the processing of Semi-structured documents.

Model Validation Tasks

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Retraining Existing Models

Using features for Semi-structured documents This article mentions features used in the processing of Semi-structured documents.

Incremental Training

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Managing Transcription Models

Transcription models are collections of fine-tuning models. Select Transcription models from the drop-down menu at the top of the Models page to view all available fine-tuning models in your instance.

Managing Transcription Models Across Flows

To meet the specific automation needs of your various lines of business, you can configure transcription, or finetuning, models at the flow level.

Evaluating Model Training Results

Using features for Semi-structured documents This article mentions features used in the processing of Semi-structured documents.

Monitoring Model Performance

Using features for Semi-structured documents This article mentions features used in the processing of Semi-structured documents.

Improving Model Performance

After identifying a drop in the model’s performance, the next step is to determine its root cause and address it accordingly.

Model Mitigation and Saturation

This article is intended for users who continue to experience poor model performance even after completing the initial monitoring and retraining steps.

ORCA (Optical Reasoning and Cognition Agent) VLMs

This feature is available in v41.2 and later.

Model Compatibility Logic

Using features for Semi-structured documents This article mentions features used in the processing of Semi-structured documents.

Forward-Compatible Models

Compatibility Across Application, Flow, and Model Versions