🧐Analyzers
Different from Evaluators, Analyzers do not have criteria and can be leveraged as a great tool to understand your product behavior. Ownlayer offers text classification and sentiment analysis
Last updated
Different from Evaluators, Analyzers do not have criteria and can be leveraged as a great tool to understand your product behavior. Ownlayer offers text classification and sentiment analysis
Last updated
To create a new Analyzer, click on the 'Analyzers' from the left panel and click on the 'Create analyzer' button. Fill out the form to create the desired analyzer. At minimum, this includes:
Name of the analyzer.
Description of what the analyzer does (optional).
Select the analyzer type
| Name | Description |
|---|---|
On Analyzer detail page, you can specify additional settings
If you anticipate the response may belong to more than one labels, you'd want to enable the multi-label toggle.
You can choose which variable to analyze
You can add Triggers on the Analyzer's detail page to automatically analyze an inference.
Select the Analyzer you just created.
Navigate to the 'Triggers' section within the Analyzer detail page, and click on 'Add tag'
Ensure that the tags specified in the Analyzers' triggers are included when you stream your inference data.
Follow the full API documentation for detailed instructions on how to implement triggers in your inference stream.
| Name | Description |
|---|---|
Input
The complete data submitted to the LLM.
Output
The generated response from the LLM
User Prompt
The user's query or input within the overall submission
Context
The context informtion within the submission
System Prompt
The instruction provided to the LLM as part of the entire input
Text Classification
Uses a text classification model based on provided labels. It assesses the likelihood of the input text belonging to each label category. Multi-label is available in settings
Positive/Negative Analysis
Uses a fine-tuned RoBERTa model for sentiment analysis, returning a score between 0 to 1 for positive, negative, and neutral.
Emotion Identificaiton
Uses a fine-tuned DistilRoBERTa model for comprehensive sentiment analysis. It assesses the emotional tone of input text, providing scores for 7 distinct sentiment categories:
Anger 🤬
Disgust 🤢
Fear 😨
Joy 😀
Neutral 😐
Sadness 😭
Surprise 😲
