To meet the specific automation needs of your various lines of business, you can configure transcription, or finetuning, models at the flow level.
This flexibility allows you to:
enter dedicated transcription automation and accuracy settings for each flow,
create flow-specific transcription models, and
share transcription models across flows.
If you share transcription models across flows, training data is shared among the flows using the same model.
Entering flow-specific settings
You can enter transcription automation settings for each of your flows in the General Transcription, Structured Document Transcription, and Semi-structured Document Transcription sections of a flow's settings. For more details, see Flow Settings.
If multiple flows are using the same model and those flows have different training settings, the model uses the settings from the flow where those settings were most recently changed.
Creating and assigning transcription models
It is not currently possible to assign and create transcription models in the application. Instead, the model a flow uses is determined by the trascription_model property in the flow's JSON.
If you would like to create new models for your flows, contact your Hyperscience representative for assistance. Your Hyperscience representative can create a model by entering a value for transcription_model in your flow's code (e.g., transcription_model=invoices).
When a new model is created, the system obtains training data from the submissions processed through the model's flow or flows. It does not run finetuning for any flows using the model until the minimum required amount of training data is gathered, unless:
You run a script that migrates past QA data generated from the model's flows. Contact your Hyperscience representative for more information about the script.
You've just upgraded, and the system-level training data from the upgrade falls within the set period of records to use.
If a flow is modified to use a model that already exists, the flow begins using the model, and any submissions processed through the flow are used to train the model. By default, any training data created from the flow before the change of model assignment is not used to train the new model. However, you can run a script that migrates past QA data generated from the flow to the new model. Contact your Hyperscience representative for more information about the script.