Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language: en
+license: apache-2.0
+library_name: transformers
+tags:
+- text-classification
+- github
+- multi-label
+- issue-classification
+datasets:
+- anasnadeem/github-issues
+pipeline_tag: text-classification
+---
+
+## Model Card for the Automatic Issue Classifier (AIC)
+
+[cite_start]This model card is for a RoBERTa-based model fine-tuned to classify GitHub issue reports into three categories: **Bug**, **Enhancement**, and **Question**[cite: 13].
+
+### Model Details
+
+[cite_start]The Automatic Issue Classifier (AIC) is a transformer-based model designed for the multi-label classification of GitHub issue reports[cite: 17]. [cite_start]It addresses the challenge that issue submitters can tag a single issue with more than one label[cite: 16].
+
+[cite_start]The model is a fine-tuned version of `roberta-base` [cite: 18, 195][cite_start], which is an optimized variant of BERT[cite: 125]. [cite_start]It was developed to overcome the limitations of traditional keyword-based approaches, which often fail to capture the contextual relationships between words in issue reports[cite: 15].
+
+[cite_start]The model and an associated industry tool were developed to automatically assign labels to newly reported issues, helping to streamline software maintenance workflows[cite: 21].
+
+***
+### Intended Use
+
+This model is intended to be used for automatically labeling new issue reports in GitHub repositories[cite: 21]. Its primary benefits include:
+* [cite_start]Helping development teams to effectively track and prioritize issues[cite: 36].
+* [cite_start]Routing issues to the correct developer or team member[cite: 70, 264].
+* Assisting project managers in optimizing resource allocation[cite: 71].
+
+```python
+from transformers import pipeline
+
+# Load the pipeline
+# Replace 'YourUsername/aic-roberta-base' with the actual model repo on the Hub
+classifier = pipeline("text-classification", model="YourUsername/aic-roberta-base", return_all_scores=True)
+
+# Example issue text (concatenated title and body)
+issue_text = """
+Title: USBhost: additional functions for keyboard appreciated
+Body: for USBhost it would be fine to have additional functions to read the USB keyboard: kbhit() getch() getche() getchar() gets() scanf()
+"""
+
+# Get predictions
+predictions = classifier(issue_text)
+print(predictions)
+
+# Expected output would be a list of scores for each label:
+# [[{'label': 'bug', 'score': 0.8}, {'label': 'enhancement', 'score': 0.75}, {'label': 'question', 'score': 0.05}]]