Model Training & Maintenance
Guides on how to create, improve and maintain Models in Re:infer, using platform features such as Discover, Explore and Validation
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Model Training & Maintenance
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Getting Started
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Manage Accounts & Users
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Using Analytics
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Automations & Re:infer
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Technical Support
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Videos, FAQs & More
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- Introduction to Model Training: video guide
- Understanding labels, entities and metadata
- Label hierarchy and best practice
- Defining your taxonomy objectives
- Analytics vs. automation use cases
- Turning your objectives into labels
- Building your taxonomy structure
- Taxonomy design best practice
- Importing your taxonomy to Re:infer
- Overview of the model training process
- Understanding the status of your dataset
- Model training and labelling best practice
- Training with sentiment analysis enabled
- Training chat and calls data
- Training support & resources
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- Introduction to 'Refine'
- Refine: video guide
- Precision and recall explained
- Precision and recall in Re:infer
- How does Validation work?
- Understanding and improving model performance
- Why might a label have low average precision?
- Training using 'Check label' and 'Missed label'
- Training using 'Teach label' (Refine)
- Understanding and increasing coverage
- Improving Balance and using 'Rebalance'
- When to stop training your model
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- Entities training guide
- Defining and setting up your entities
- Understanding entities
- Which pre-trained entities are available?
- Enabling, disabling, updating and creating entities
- Entity filtering
- Reviewing and applying entities
- Validation for entities
- Improving entity performance
- Building custom regex entities