Intent Entity Model Builder
Introduction: Identification of Intent and entity has a huge variety of use cases in industry wherever there is a need to understand the intention behind the utterances from users and automate certain processes.
Following are the terminology used in this plugin.
Utterance: Anything the user says. For example, if a user types “What's the weather outside today in SanFrancisco ”, the entire sentence is the utterance.
Intent: An intent is the user’s intention. For example, if a user types “What's the weather outside today in San Francisco”, the user’s intent is to get the weather reports. Intents are given a name, often a verb and a noun, such as “getWeather”.
Entity: An entity modifies a intent. For example, if a user types “What's the weather outside today in San Francisco”, the entities are “today” and “San Francisco”. Entities are given a name, such as “dateTime” and “location”. Entities are sometimes referred to as slots.
Description
This step builds a model for Intent Classification and Entity Extraction.
Configurations
No. | Field Name | Description |
---|---|---|
1 | Step name | Specify the name of the step. Step names should be unique within a workflow. |
Input Fields: | ||
2 | Build using AE Model Version | Select the Python version you will use for building the model. Note: The Python version you select must be same as the version you have saved in the python folder or added to the environment variable. |
3 | Use custom configuration file to build model? | Select this checkbox to enable ‘Custom Configuration FileName’ field below to provide a custom configuration file to build the model. |
4 | Custom Configuration FileName | This field is editable if the checkbox Use custom configuration files to build model? Is selected. A default configuration file is used to build the intent entity model. However, you may specify the path of a custom configuration file (.yml) here to build the model. |
5 | JSON Filename | Specify path of a JSON Filename containing Intent and Entities data. Sample JSON file contents: { "nlu_data": { "common_examples": [ { "text": "i'm looking for a place to eat", "intent": "restaurant_search", "entities": [] }, { "text": "i'm looking for a place in the north of town", "intent": "restaurant_search", "entities": [ { "start": 31, "end": 36, "value": "north", "entity": "location" } ] } } |
6 | Button: Browse | Click to browse for a JSON filename. |
7 | Model Directory Name | Specify or Browse for a Directory for the built Model file. |
8 | Button: Browse | Click to browse for a Model Directory. |
Output Field | ||
9 | Model Directory Field Name | Specify a fieldname to hold the complete path of the model (including the directory and model filename). The default value is outputModelDirectoryFieldName. |