Update model card

README.md

@@ -1,3 +1,65 @@
 ---
 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.