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: \nCategory:
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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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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