Data Machines
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Models Supported by Data Machi...
NLP Models
Emotion Analysis
4min
Emotion models are pre-trained classification models for the task of classifying the emotion in the input document and the specified target words. For example, in the text, "I hate school. School is bad". The model may predict anger and sadness as the most prevalent emotions overall, and also predict the targeted emotion for specific mentions, for example: frustrated would have anger as the most prevalent emotion.
Parameter Name | Parameter Type | Required |
---|---|---|
input | Text | Yes |
Rest API Input Example
Parameter Name | Parameter Type |
---|---|
emotion | JSON String |
top emotion | Text |
emotion mentions | JSON String |
Rest API Output Example
Every model execution output consists of the following standard output parameters
- input
- The input string required for the model to extract the categories
- original input
- This is the input provided to the first step in model which is retained across multiple steps in a Data Machine workflow.
- final result
- The result of the model executed in the final step of the Data Machine workflow
- sessionid
- A unique session id that is generated for every execution of a Data Machine which can be used to retain results across multiple sessions
- status
- The result of the Data Machine execution. If all of the steps in a sequence are successfully executed, a value of "Completed" is provided. If the execution is interrupted at any point, a value of "Terminated" is provided with the reason for Termination.
Updated 17 Dec 2023
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