license:mit region:us
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license: mit
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# Synthetic IT Call Center Dataset

## Description

This dataset comprises 9081 items, each an example of a potential sentence generated by an IT call center agent during the course of processing tickets. The responses are designed to span a broad spectrum of quality, from highly effective to poor, simulating real-world variations in agent performance. Each response has been evaluated and scored on a scale from 0 to 10 by a simulated call center manager, with the scores reflecting the perceived effectiveness of the response in addressing customer concerns. Additionally, each item includes a rationale provided by the simulated manager, offering insights into the scoring decision.

All items are synthetically generated by gpt-4. No actual call center data is used in this dataset. 

## Dataset Structure

The dataset is structured as follows:

- **sentence**: The text of the sentence generated to simulate an IT call center agent's journaling during the course of processing a ticket.
- **ranking**: A numerical value ranging from 0 to 10, assigned by the simulated manager, indicating the quality of the response. A score of 0 indicates a poor sentnce, while a score of 10 represents a high quality sentence.
- **is_grammatically_correct**: A boolean value that is false if there are grammar errors detected and true if otherwise
- **reasoning**: A textual explanation provided by the simulated manager, detailing the reasons behind the assigned score.

## Use Cases

This dataset is intended for use in training and evaluating machine learning models designed to assess the quality of customer service responses. Potential applications include:

- Automated quality control systems for call centers, enabling real-time feedback to agents.
- Training tools for customer service representatives, offering examples of effective communication.
- Research in natural language processing, particularly in understanding nuances in customer service interactions.


## Future

I intend to increase the size of the dataset in the future