200 lines
5.7 KiB
Markdown
200 lines
5.7 KiB
Markdown
---
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library_name: transformers
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tags: []
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---
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# Model Card for Model ID: masonmidd/ticket-gpt2
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Task: IT Support Ticket Analysis — text generation for ticket categorization, triage, and troubleshooting suggestion.
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## Training Setup:
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Base model: gpt2 (pretrained, fine-tuned — not trained from scratch)
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Framework: Hugging Face transformers + Trainer API
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Epochs: 1
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Batch size: 2 per device
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Learning rate: 5e-5
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Max token length: 128
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Platform: Google Colab
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Input format: Ticket: <issue text>\nCategory: <labels>
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### Evaluation Metrics
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The model was evaluated on 4 hand-labeled IT support tickets across these categories: Network Issue, Hardware Issue, Software Issue, and Access Issue.
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Metric: Accuracy
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Value:Computed via sklearn.metrics.accuracy_score
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### Intended Uses and Limitations
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## Uses
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Automated ticket triage: Categorize incoming IT support tickets and suggest urgency level and routing team.
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Employee self-service: Provide first-pass troubleshooting suggestions to employees before escalating to IT staff.
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Help desk workflow automation: Reduce manual ticket sorting and improve response times.
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### Limitations:
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The model was trained for only 1 epoch on a relatively small batch size; outputs may be inconsistent or hallucinated.
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The evaluation set is very small (4 examples) — accuracy metrics should not be treated as production benchmarks.
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Keyword-based label extraction is used post-generation; the model does not natively output structured labels.
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Not suitable for sensitive or high-stakes IT decisions without human review.
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Performance degrades on ticket types not well-represented in the GitHub issues training data (e.g., HR, facilities, or non-software issues).
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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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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed] |