---
library_name: transformers
tags: []
---

# Model Card for Model ID: masonmidd/ticket-gpt2

Task: IT Support Ticket Analysis — text generation for ticket categorization, triage, and troubleshooting suggestion.



## Training Setup:
Base model: gpt2 (pretrained, fine-tuned — not trained from scratch)
Framework: Hugging Face transformers + Trainer API
Epochs: 1
Batch size: 2 per device
Learning rate: 5e-5
Max token length: 128
Platform: Google Colab
Input format: Ticket: <issue text>\nCategory: <labels>

### Evaluation Metrics
The model was evaluated on 4 hand-labeled IT support tickets across these categories: Network Issue, Hardware Issue, Software Issue, and Access Issue.
Metric: Accuracy
Value:Computed via sklearn.metrics.accuracy_score


### Intended Uses and Limitations

## Uses
Automated ticket triage: Categorize incoming IT support tickets and suggest urgency level and routing team.
Employee self-service: Provide first-pass troubleshooting suggestions to employees before escalating to IT staff.
Help desk workflow automation: Reduce manual ticket sorting and improve response times.


### Limitations:
The model was trained for only 1 epoch on a relatively small batch size; outputs may be inconsistent or hallucinated.
The evaluation set is very small (4 examples) — accuracy metrics should not be treated as production benchmarks.
Keyword-based label extraction is used post-generation; the model does not natively output structured labels.
Not suitable for sensitive or high-stakes IT decisions without human review.
Performance degrades on ticket types not well-represented in the GitHub issues training data (e.g., HR, facilities, or non-software issues).
The base GPT-2 model has a knowledge cutoff and no real-time awareness of your organization's systems or policies.
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## How to Get Started with the Model

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## Training Details

### Training Data

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## Evaluation

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#### Summary



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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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