Update README.md

README.md

@@ -7,8 +7,37 @@ metrics:
 base_model:
 - distilbert/distilbert-base-uncased
 pipeline_tag: text-classification
+widget:
+  - text: "The product arrived on time and was exactly as described."
 ---
 
+### Categories:
+
+### label_mapping = {
+    "shipping_and_delivery": 0,
+    
+    "customer_service": 1,
+    
+    "price_and_value": 2,
+    
+    "quality_and_performance": 3,
+    
+    "use_and_design": 4,
+    
+    "other": 5
+}
+
+## How to Use:
+
+Here is an example of how to use this model for inference:
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="dnzblgn/Customer-Reviews-Classification")
+result = classifier("The product arrived on time and was exactly as described.")
+print(result)
+
 ### Model Description
 
 This fine-tuned DistilBERT model is specifically designed for document classification. It classifies customer feedback into six predefined categories: Shipping and Delivery, Customer Service, Price and Value, Quality and Performance, Use and Design, and Other. By leveraging the transformer-based architecture of DistilBERT, the model efficiently handles the syntactic patterns of text, providing accurate document classification based on content, style, and structure.
@@ -27,23 +56,9 @@ While the model achieves high accuracy across the six categories, it has limitat
 Users (both direct and downstream) should be aware of the model's single-label prediction limitation. In cases where a document contains features of multiple categories, additional models or multi-label classification techniques should be considered.
 
 ### Training Data
-A custom synthetic dataset was created for this task, focusing on the structural features of text. The dataset provides examples from six categories, helping the model learn from both the syntactic organization and the meaning of the text.
 
-### Categories:
+A custom synthetic dataset was created for this task, focusing on the structural features of text. The dataset provides examples from six categories, helping the model learn from both the syntactic organization and the meaning of the text.
 
-### label_mapping = {
-    "shipping_and_delivery": 0,
-    
-    "customer_service": 1,
-    
-    "price_and_value": 2,
-    
-    "quality_and_performance": 3,
-    
-    "use_and_design": 4,
-    
-    "other": 5
-}
 ### Training Hyperparameters
 Model: distilbert/distilbert-base-uncased