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README.md

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----
-license: mit
----
+---
+language: en
+license: apache-2.0
+tags:
+- text-classification
+- distilbert
+- ticket-classification
+- customer-support
+- it-support
+datasets:
+- custom
+metrics:
+- accuracy
+- f1
+widget:
+- text: "I can't log into my account, password reset not working"
+- text: "System is very slow, taking forever to load pages"
+- text: "How do I integrate with Salesforce API?"
+---
+
+# TicketCat: IT Support Ticket Classification Model
+
+This is a fine-tuned DistilBERT model for classifying IT support tickets into 9 categories.
+
+## Model Description
+
+This model was fine-tuned on customer support tickets to automatically categorize incoming IT support requests. It uses DistilBERT as the base model and was trained to classify tickets into 9 distinct categories.
+
+## Intended Use
+
+This model is designed to automatically categorize IT support tickets to help route them to the appropriate support team or department.
+
+## Categories
+
+The model classifies tickets into the following categories:
+
+- Account Access / Login Issues
+- Billing & Payments
+- Bug / Defect Reports
+- Feature Requests
+- General Inquiries / Other
+- How-To / Product Usage Questions
+- Integration Issues
+- Performance Problems
+- Security & Compliance
+
+## Usage
+
+```python
+from transformers import DistilBertForSequenceClassification, DistilBertTokenizer
+import torch
+
+# Load model and tokenizer
+model_name = "YOUR_HF_USERNAME/ticketcat-distilbert"  # Replace with your actual model name
+tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+model = DistilBertForSequenceClassification.from_pretrained(model_name)
+
+# Classify a ticket
+ticket_text = "I can't access my account, password reset link is not working"
+inputs = tokenizer(ticket_text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    probs = torch.softmax(outputs.logits, dim=1)
+    predicted_class = torch.argmax(probs, dim=1).item()
+    confidence = probs.max().item()
+
+# Map to category name
+categories = {'0': 'Account Access / Login Issues', '1': 'Billing & Payments', '2': 'Bug / Defect Reports', '3': 'Feature Requests', '4': 'General Inquiries / Other', '5': 'How-To / Product Usage Questions', '6': 'Integration Issues', '7': 'Performance Problems', '8': 'Security & Compliance'}
+predicted_category = categories[str(predicted_class)]
+
+print(f"Category: {predicted_category}")
+print(f"Confidence: {confidence:.4f}")
+```
+
+## Training Details
+
+- **Base Model**: distilbert-base-uncased
+- **Framework**: Hugging Face Transformers
+- **Task**: Multi-class text classification
+- **Number of Classes**: 9
+- **Max Sequence Length**: 128 tokens
+- **Training Approach**: Fine-tuning with class weights for imbalanced data
+
+## Limitations
+
+- The model was trained on IT/customer support tickets and may not perform well on other domains
+- Performance may vary on tickets that don't fit clearly into one category
+- Low confidence predictions (< 0.6) may need human review
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@misc{ticketcat2024,
+  author = {TicketCat Team},
+  title = {TicketCat: IT Support Ticket Classification},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/YOUR_USERNAME/ticketcat-distilbert}}
+}
+```