User permissions required: 'Review and label'
What's covered in this article
Overview
Like training labels, training entities is the process by which a user teaches the platform which entities apply on a given verbatim using various training modes.
Like with labels, the ‘Teach’, ’Check’, and ’Missed’ modes are available to help train and improve the performance of entities and can be accessed on the ’Explore’ page using the training dropdown.
A dropdown menu containing the entity training modes
The following table summarises when to use each entity training mode:
| Teach Entity | Check Entity | Missed Entity |
- Show predictions for a label where the model is most confused if it applies or not - For training entities on unreviewed verbatims | - Shows verbatims where the platform thinks the entity may have been misapplied - For training entities on reviewed verbatims to try to find and correct any inconsistencies | - Shows verbatims that the platform thinks may be missing the selected entity - For training entities on reviewed verbatims to try to find and correct any inconsistencies |
Using Teach Entity
Using Teach Entity boosts entity performance, because the model is being given new information on verbatims it is unsure about, as opposed to ones that it already has highly confident predictions for.
You should use 'Teach Entity' when:
- There is a performance warning next to an entity (as seen below – when the min. 25 examples has not been provided)
- The F1 score on a given entity is low
- There may not always be obvious context within the text for an entity, or there is lots of variation within the entity values for a given type
Example of the validation page – an entity with a performance warning
An example of training an entity in ‘Teach Entity’ mode
Using Check Entity
Using check entity helps identify inconsistencies in the reviewed set, while improving the model's understanding of the entity, by ensuring that the model has correct and consistent examples to make predictions. This will improve the recall of an entity
You should use 'Check Entity' when:
- There is low recall, but high precision
- The predictions the platform makes are very accurate, but a lot of the time where the entity has been applied, it doesn’t catch these examples
Example scenario of when to use check entity
An example of training an entity in ‘Check Entity’ mode
(For more details on calculations for entity validation, please see here)
Using Missed Entity
Using missed entity helps find examples in the reviewed set that should have the selected entity but do not. It will also help identify partially labelled verbatims which can be detrimental to the model's ability to predict an entity. This will improve the precision of an entity and ensure the model has correct and consistent examples to make predictions from.
You should use 'Missed Entity' when:
- There is high recall, but low precision
- We’re incorrectly predicting entities a lot, but when we do predict them correctly -we catch many of the examples that should be there
Example scenario of when to use missed entity
An example of training an entity in ‘Missed Entity’ mode
(For more details on calculations for entity validation, please see here)
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