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Creating an NLU model

How to create an NLU model

To create a new model, proceed as follows:

  1. Navigate to NLU → NLU Models section.



  2. Click Create. The following dialog box opens:

  1. Click Create NewNext. The following dialog box opens:

  2. Fill in the fields below:

  • Name: Model name.

  • Group: A group of users who can access the model.

  • Domain: Based on the selected domain, an out-of-the-box understanding of intents and entities will be available using Omilia’s pre-tuned xPacks. Regardless of the selected domain, you can add your own data to augment your model’s understanding using machine learning. Available domains are the following:  

    • The Universal domain offers out-of-the-box entities understanding only. In case you want to also include intents, you can build your own fully custom intent understanding.
      The Universal 2.0 domain offers out-of-the-box entities that are not linked to any specific domain. This means that you can use it for any domain you might want to work with and you want to take advantage of this domain’s entity variety.

    • The Custom domain has no out-of-the-box understanding and you can fully customize it by adding your own data and uploading your own custom NLU Logic.

    • Insurance

    • Telecommunications

    • Car Retail

    • Banking

    • COVID

    • Energy

  • Enable US Addresses add-on: If marked, the US Addresses add-on will be activated for your model. This checkbox is available for the Universal 2.0 domain only.

  • Language: The NLU model’s Language. Out-of-the-box understanding is for the language you select.

  • Version: Machine learning software version. The default value is 3.2.0.

  • Type: Machine learning software type. You can train your model with different software types and select the one that works best for you. The performance will increase as the size of the training set increases. Depending on the selected language, the following types are available:

    • Many-shot - Omilia sentence embeddings: Available in English. Works best for Omilia domains (for example, Banking, Insurance, Energy, Telecommunications, and so on) leveraging Transformer encoders with pre-trained domain-specific embeddings.

    • Many-shot - English sentence embeddings: Recommended for non-Omilia domains in English. Leverages Transformer encoders with generic English embeddings.

    • Many-shot - Multilingual sentence embeddings: Suitable for all domains in languages other than English.

  • Training Set: Allows you to add your own custom training data to augment the model’s intent understanding using machine learning.

    Add a file with training data in TXT, CSV, or TSV. This is an optional field.

  • Description: Provide a short description of the NLU model. This field is optional.

  1. Click Create to confirm. The model is created.

Model drill-down page

After having created a model, you are forwarded to the model drill-down page. Depending on the model domain, the drill-down page may look different:

  • For models with a specific domain, you will see the out-of-the-box intents and entities, which are loaded automatically, if available. The out-of-the-box elements are labeled as RB-XP as shown below:

  • For a custom domain model, no out-of-the-box understanding is available. If you are creating a custom domain model and have not uploaded any custom data yet, the model will look as follows:

  • If you have uploaded your custom data by using a TXT, CSV, or TSV file, the uploaded intents and utterances will also be visible. The custom elements are not labeled, as shown below:


The drill-down page header reveals the following information about the model.





The model name.


The language selected for the model.


The model domain.


The model identification number. To copy the ID to the clipboard, click the Copy icon.


The name of the user the model was created by.


The model status. The following statuses are possible:

  • Not Ready: The model is not ready and cannot be deployed. Add your own custom datatrain it and then deploy it.

  • Working: The model is currently being trained with your custom data.

  • Ready: The model is ready to be used. Go ahead, deploy it and test it!

  • Failed: The model training has failed.

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