license:mit size_categories:1K<n<10K format:json modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
license: mit
configs:
- config_name: nano
  data_files: "support_tickets.json"
  default: true
- config_name: alpha
  data_files: "short_support_tickets.json"
---
<h3>Welcome to support_tickets super small dataset.</h3>

<h4>Dataset Brief</h4>
This dataset contains 60 samples. 

Each tickets resembles a support ticket.<br>
Two main fields are subject and description. Subject is a short title for the ticket,<br>
while description contains extended description of the issue.

The thrid field is a key_phase, which indicate key phrase of the ticket.

You can all split the entire dataset into thress classes:<br>
-access<br>
-user<br>
-disk<br>

Key phrases for each class are as follows:<br>
<i>{<br>
'access': ['grant access', 'revoke access', 'access profile'],<br>
'user': ['add user', 'delete user', 'modify user','create user'],<br>
'disk': ['disk space', 'disk error', 'disk full']<br>
}</i>

<h4>How to use this dataset</h4>
This dataset is designed be used for: <br>
-unsupervised tasks (clustering, dimensionality reduction, etc.)<br>
-supervised (classification)<br>
-llm-based tasks (embdeddings, prompting, RAG, etc.)<br>

<h4>Examples</h4>
<b>1. Support Ticket Classification Task:</b>
  <li> <a href="https://github.com/enoten/support_ticket_analysis/blob/main/support_tickets_classification.ipynb">Code</a> @ GitHub
  <li> Code Walk Through Video: <a href="https://www.youtube.com/watch?v=NlJ1yS0F03M"> Support Ticket Classification</a> @ YouTube

Good luck using this dataset!