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---
license: gpl-3.0
language:
- en
metrics:
- f1
---
# Model Card
## Model Details
- Model Name: IssueReportClassifier-NLBSE22
- Base Model: RoBERTa
- Dataset: NLBSE22
- Model Type: Fine-tuned
- Model Version: 1.0
- Model Date: 2023-03-21
## Model Description
IssueReportClassifier-NLBSE22 is a RoBERTa model which is fine-tuned on the NLBSE22 dataset.
The model is trained to classify issue reports from GitHub into three categories: bug, enhancement, and question.
The model is trained on a dataset of labeled issue reports and is designed to predict the category of a new issue report based on its text content (title and body).
## Dataset
| Category | Training Set | Test Set |
|------------|--------------|-------------|
| bug | 361,239 (50%) | 40,152 (49.9%) |
| enhancement | 299,287 (41.4%) | 33,290 (41.3%) |
| question | 62,373 (8.6%) | 7,076 (8.8%) |
## Metrics
The model is evaluated using the following metrics:
- Accuracy
- Precision
- Recall
- F1 Score (micro and macro average)
## References
- [NLBSE22 Dataset](https://nlbse2022.github.io/tools/)
## Cite our work
```
@inproceedings{Colavito-2022,
title = {Issue Report Classification Using Pre-trained Language Models},
booktitle = {2022 IEEE/ACM 1st International Workshop on Natural Language-Based Software Engineering (NLBSE)},
author = {Colavito, Giuseppe and Lanubile, Filippo and Novielli, Nicole},
year = {2022},
month = may,
pages = {29--32},
doi = {10.1145/3528588.3528659},
abstract = {This paper describes our participation in the tool competition organized in the scope of the 1st International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on fine-tuned BERT-based language models for the automatic classification of GitHub issues. We experimented with different pre-trained models, achieving the best performance with fine-tuned RoBERTa (F1 = .8591).},
keywords = {Issue classification, BERT, deep learning, labeling unstructured data,
software maintenance and evolution},
}
```
I hope this helps. Let me know if you have any other questions.
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