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README.md

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+---
+language: 
+  - en
+  - de
+tags:
+  - text-classification
+  - ticket classification
+  - multilingual
+  - email-intent-detection
+  - customer-support
+  - xlm-roberta
+license: apache-2.0
+datasets: 
+  - private
+model-index:
+  - name: xlm-roberta-ticket-classifier
+    results:
+      - task:
+          type: text-classification
+          name: Email Ticket Classification
+        dataset:
+          name: german-english-email-ticket-classification
+          type: private
+        metrics:
+          - name: Accuracy (Type)
+            type: accuracy
+            value: 0.8573
+          - name: Accuracy (Queue)
+            type: accuracy
+            value: 0.5189
+          - name: F1 Score (Type)
+            type: f1
+            value: 0.8573
+          - name: F1 Score (Queue)
+            type: f1
+            value: 0.5209
+
+---
+
+# XLM-RoBERTa Ticket Classifier
+
+A multilingual email/ticket classifier fine-tuned from `xlm-roberta-base` to categorize customer support tickets in English and German. It predicts both routing category and issue type, helping automate ticket triage, intent detection, and prioritization in multilingual helpdesk environments.
+
+
+## Model Details
+
+- **Base model**: `xlm-roberta-base`
+- **Languages**: English 🇬🇧 & German 🇩🇪
+- **Task**: Multi-class text classification
+- **Training data**: [german-english-email-ticket-classification](https://huggingface.co/datasets/ale-dp/german-english-email-ticket-classification)
+- **Tokenizer**: SentencePiece BPE tokenizer
+- **Framework**: 🤗 Transformers
+
+## Classification Schema
+
+This model performs **multi-head classification**, predicting both:
+
+### 🎯 Queue (Routing Category)
+- Billing and Payments  
+- Customer Service  
+- General Inquiry  
+- Human Resources  
+- IT Support  
+- Product Support  
+- Returns and Exchanges  
+- Sales and Pre-Sales  
+- Service Outages and Maintenance  
+- Technical Support  
+
+### 🛠️ Type (Issue Nature)
+- Incident 
+- Request  
+- Problem  
+- Change  
+
+## 📈 Model Performance Summary
+
+| Metric                  | Value   |
+|-------------------------|---------|
+| **Accuracy (Type)**      | 85.73%  |
+| **Accuracy (Queue)**     | 51.89%  |
+| **F1 Score (Type)**      | 85.73%  |
+| **F1 Score (Queue)**     | 52.09%  |
+
+This model demonstrates strong performance on **type classification**, while **queue prediction** reflects the inherent complexity of routing logic across overlapping categories.
+
+🔍 _More detailed metrics, visualizations, and training curves available on the [W&B dashboard](https://wandb.ai/alikhalaji-/bilingual_ticket_classifier)_
+
+## Intended Uses
+
+- Classify incoming tickets into predefined categories
+- Automate support ticket routing
+- Detect customer intent in multilingual environments
+- Integrate with helpdesk platforms like Zendesk or Freshdesk
+
+## 🚀 Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+model_id = "ale-dp/xlm-roberta-ticket-classifier"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+text = "Hallo, Die Data-Analytics-Plattform funktioniert nicht richtig und es werden unkorrekte Investment-Analyse-Fehlermeldungen generiert. Dies könnte auf einen Software-Fehler hindeuten."
+
+result = classifier(text)
+print(result)
+```
+### Created by:  
+***[ᴀʟɪ ᴋʜᴀʟᴀᴊɪ](https://github.com/alikhalajii)***
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{xlm-roberta-ticket-classifier,
+  author = {Ali Khalaji},
+  title = {XLM-RoBERTa Ticket Classifier},
+  year = {2025},
+  url = {https://huggingface.co/ale-dp/xlm-roberta-ticket-classifier}
+}
+