| --- |
| language: |
| - en |
| library_name: transformers |
| pipeline_tag: text-classification |
| base_model: answerdotai/ModernBERT-base |
| datasets: |
| - AliArshad/Bugzilla_Eclipse_Bug_Reports_Dataset |
| metrics: |
| - accuracy |
| - f1 |
| tags: |
| - modernbert |
| - bug-triage |
| - bug-severity |
| - sequence-classification |
| license: apache-2.0 |
| --- |
| |
| # ModernBERT Bug Severity Classifier |
|
|
| This model is a fully fine-tuned version of |
| `answerdotai/ModernBERT-base` for classifying short bug descriptions |
| into six severity levels: |
|
|
| - blocker |
| - critical |
| - major |
| - normal |
| - minor |
| - trivial |
|
|
| ## Intended use |
|
|
| This model is an educational demonstration of automated bug-severity |
| classification. |
|
|
| It should not be used as the sole authority for production severity |
| decisions. High-impact predictions should be reviewed by a human. |
|
|
| ## Training dataset |
|
|
| The model was trained using: |
|
|
| `AliArshad/Bugzilla_Eclipse_Bug_Reports_Dataset` |
|
|
| Only the `Short Description` field was used as the model input. The |
| `Severity Label` field was used as the target label. |
|
|
| ## Base model |
|
|
| `answerdotai/ModernBERT-base` |
|
|
| ## Training approach |
|
|
| This model was trained using full fine-tuning. It is not a LoRA or |
| adapter-only model. |
|
|
| Training included: |
|
|
| - Removing missing and empty descriptions |
| - Converting severity names into numeric labels |
| - Stratified training, validation, and test splits |
| - Batched tokenization |
| - Dynamic padding and attention masks |
| - Hugging Face Trainer |
| - Macro F1 checkpoint selection |
|
|
| ## Evaluation |
|
|
| - Test accuracy: 0.871 |
| - Test macro F1: 0.32951261884727445 |
|
|
| Performance should also be examined separately for each severity using |
| the classification report and confusion matrix. |
|
|
| ## Limitations |
|
|
| - Severity cannot always be determined from a short description alone. |
| - The training dataset may contain noisy or inconsistent labels. |
| - The severity classes are imbalanced. |
| - Historical bug reports may not represent current software practices. |
| - Softmax confidence is not guaranteed to be a calibrated probability. |
| - Human review is recommended for blocker and critical predictions. |
|
|
| ## License status |
|
|
| The ModernBERT base model uses the Apache 2.0 license. The training |
| dataset's Hugging Face page does not currently declare a dataset |
| license. Confirm the applicable dataset and source-data terms before |
| making this fine-tuned model public. |
|
|
| ## Example usage |
|
|
| Install the required library: |
|
|
| pip install transformers torch |
| |
| Run inference: |
|
|
| from transformers import pipeline |
| |
| classifier = pipeline( |
| "text-classification", |
| model="abhishes/modernbert-bug-severity", |
| revision="v1.0.0" |
| ) |
| |
| bug_reports = [ |
| "Application crashes immediately and all data is lost.", |
| "There is a spelling mistake in the documentation." |
| ] |
| |
| results = classifier(bug_reports) |
| |
| print(results) |
