Per category users can train extraction AIs.
Users can activate one extraction AI per category which will then be used to create Automated Annotations.
Extraction AI details#
Label set in a project used for training.
Statuses of an AI training.
“Queuing for training…”: The extraction AI is waiting in the queue for its training to be started.
“Data loading in progress…”: The training process has started and the Konfuzio server loads the training data into memory.
“AI training in progress…”: The training data is loaded into memory and the actual training takes place.
“AI evaluation in progress…”: “The extraction AI is trained and the evaluation of the trained extraction AI is conducted.”
“Training finished.”: The extraction AI is evaluated and can be used.
In case the extraction AI could not be trained it will have the status “Contact support”.
The description to document the reason for training.
Incremented version per training
Saved status of when training started.
Date and time when training was started.
Loading time (in seconds):#
Displays the average, minimum and maximum loading time across all runs of this AI.
Runtime (in seconds):#
Displays the average, minimum and maximum runtime across all runs of this AI. (If you add the loading and the runtime you get the overall time an AI run on a document has consumed.)
AI quality evaluation on category, label set and label level.
Train extraction AI#
The training process is 100 % automated, so the only setup users need to do is to select the eategory for which an extraction AI should be trained and add a short description. The short description will help to relate the intention behind any change in the project to the quality of the extraction AI.
Visit the tutorial Improve Extraction AI to improve the quality of a extraction AI.
Retrain extraction AI#
If you have new documents uploaded to your project you can train a new version of your extraction AI.
Add those to the Status: Training documents
Train extraction AI, see above.
As you use the same documents with Status: Test documents but increased the number of documents with Status: Training documents the AI quality should improve.
Read more about how to Improve Extraction AI to improve your extraction AI even further.
Extraction AI actions#
Evaluate extraction AIs#
If you change the documents assigned to the status test dataset you can evaluate old extraction AI models. This is helpful to evaluate different extraction AIs on the current test dataset.
Get evaluation as CSV file#
Download the most granular evaluation file. Have a look at Improve Extraction AI to see how to use this CSV.
Activate extraction AI for available categories#
A handy option to update the extraction AI for all related categories, as multiple categories can use one extraction AI even across projects.