---
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)