Update README.md

README.md

@@ -1,48 +1,54 @@
+---
+library_name: transformers
+tags:
+  - text-classification
+  - customer-support
+---
+
 # Model Card for Ticket Classifier
 
+A fine-tuned DistilBERT model that automatically classifies customer support tickets into four categories: Billing Question, Feature Request, General Inquiry, and Technical Issue.
+
 ## Model Details
 
 ### Model Description
 
-This model is a fine-tuned version of DistilBERT for text classification, specifically designed to classify customer support tickets into predefined categories. It has been trained on a dataset of customer inquiries to assist in automating ticket categorization.
+This model is a fine-tuned version of `distilbert-base-uncased` that has been trained to classify customer support tickets into predefined categories. It can help support teams automatically route tickets to the appropriate department.
 
-- **Model type:** DistilBERT-based Text Classifier
+- **Developed by:** [Your Name/Organization]
+- **Model type:** Text Classification (DistilBERT)
 - **Language(s):** English
-- **License:** Apache 2.0
-- **Finetuned from model:** distilbert-base-uncased
-
-### Evaluation Results
+- **License:** [Your License]
+- **Finetuned from model:** `distilbert-base-uncased`
 
-- **eval_loss:** 0.2139
-- **eval_accuracy:** 95.93%
-- **eval_runtime:** 99.37 seconds
-- **eval_samples_per_second:** 3.71
-- **eval_steps_per_second:** 1.86
-- **epoch:** 3
+## Uses
 
-## Training Details
+### Direct Use
 
-### Training Hyperparameters
-
-- **Training regime:** fp16 mixed precision
-- **Epochs:** 3
-- **Batch size:** 2
-- **Learning rate:** 2e-5
-- **Weight decay:** 0.01
-
-## How to Get Started with the Model
+This model can be directly used to classify incoming customer support tickets. It takes a text description of the customer's issue and classifies it into one of four categories:
+- Billing Question (0)
+- Feature Request (1)
+- General Inquiry (2)
+- Technical Issue (3)
 
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from transformers import pipeline
+import torch
+
+# Define class mapping
+id_to_label = {0: 'Billing Question', 1: 'Feature Request', 2: 'General Inquiry', 3: 'Technical Issue'}
 
-# Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("your_username/ticket_classifier")
-model = AutoModelForSequenceClassification.from_pretrained("your_username/ticket_classifier")
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("YOUR_MODEL_PATH")
+model = AutoModelForSequenceClassification.from_pretrained("YOUR_MODEL_PATH")
 
-# Initialize pipeline
-classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+# Prepare input
+text = "I was charged twice for my subscription this month"
+inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
 
-# Classify a sample text
-result = classifier("Sample customer support ticket text.")
-print(result)
+# Run inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    prediction = torch.argmax(outputs.logits, dim=1).item()
+    
+print(f"Predicted class: {id_to_label[prediction]}")