Update README.md

README.md

@@ -1,6 +1,5 @@
 ---
 tags:
-- autotrain
 - text-classification
 base_model: cross-encoder/nli-roberta-base
 widget:
@@ -14,10 +13,10 @@ pipeline_tag: zero-shot-classification
 library_name: transformers
 ---
 
-# LogicSpine/roberta-base-Address-classifier
+# LogicSpine/address-base-text-classifier
 
 ## Model Description
-`LogicSpine/roberta-base-Address-classifier` is a fine-tuned version of the `cross-encoder/nli-roberta-base` model, specifically designed for address classification tasks using zero-shot learning. It allows you to classify text related to addresses and locations without the need for direct training on every possible label.
+`LogicSpine/address-base-text-classifier` is a fine-tuned version of the `cross-encoder/nli-roberta-base` model, specifically designed for address classification tasks using zero-shot learning. It allows you to classify text related to addresses and locations without the need for direct training on every possible label.
 
 ## Model Usage
 
@@ -38,7 +37,7 @@ from transformers import pipeline
 
 # Load the zero-shot classification pipeline with the custom model
 classifier = pipeline("zero-shot-classification", 
-                      model="LogicSpine/roberta-base-Address-classifier")
+                      model="LogicSpine/address-base-text-classifier")
 
 # Define your input text and candidate labels
 text = "Delhi, India"