Update README.md

README.md

@@ -14,29 +14,73 @@ pipeline_tag: zero-shot-classification
 library_name: transformers
 ---
 
-# Model Trained Using AutoTrain
-
 - Problem type: Text Classification
 
-## Validation Metrics
-loss: 0.28241145610809326
+# LogicSpine/roberta-base-Address-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.
+
+## Model Usage
+
+### Installation
 
-f1_macro: 0.8093855588593053
+To use this model, you need to install the `transformers` library:
 
-f1_micro: 0.9515418502202643
+```bash
+pip install transformers torch
+```
 
-f1_weighted: 0.949198754683482
+### Loading the Model
 
-precision_macro: 0.8090277777777778
+You can easily load and use this model for zero-shot classification using Hugging Face's pipeline API.
 
-precision_micro: 0.9515418502202643
+```
+from transformers import pipeline
 
-precision_weighted: 0.9473201174743024
+# Load the zero-shot classification pipeline with the custom model
+classifier = pipeline("zero-shot-classification", 
+                      model="LogicSpine/roberta-base-Address-classifier")
 
-recall_macro: 0.8100845864661653
+# Define your input text and candidate labels
+text = "Delhi, India"
+candidate_labels = ["Country", "Department", "Laboratory", "College", "District", "Academy"]
 
-recall_micro: 0.9515418502202643
+# Perform classification
+result = classifier(text, candidate_labels)
 
-recall_weighted: 0.9515418502202643
+# Print the classification result
+print(result)
+```
+
+## Example Output
+
+```
+{'labels': ['Country',
+            'District',
+            'Academy',
+            'College',
+            'Department',
+            'Laboratory'],
+ 'scores': [0.19237062335014343,
+            0.1802321970462799,
+            0.16583585739135742,
+            0.16354037821292877,
+            0.1526614874601364,
+            0.14535939693450928],
+ 'sequence': 'Delhi, India'}
+```
+
+## Validation Metrics
 
-accuracy: 0.9515418502202643
+**loss:** `0.28241145610809326`
+**f1_macro:** `0.8093855588593053`
+**f1_micro:** `0.9515418502202643`
+**f1_weighted:** `0.949198754683482`
+**precision_macro:** `0.8090277777777778`
+**precision_micro:** `0.9515418502202643`
+**precision_weighted:** `0.9473201174743024`
+**recall_macro:** `0.8100845864661653`
+**recall_micro:** `0.9515418502202643`
+**recall_weighted:** `0.9515418502202643`
+**accuracy:** `0.9515418502202643`