Update README.md

README.md

@@ -1,36 +1,84 @@
-
 ---
 tags:
-- autotrain
 - text-classification
 base_model: cross-encoder/nli-roberta-base
 widget:
-- text: "I love AutoTrain"
+- text: I love AutoTrain
+license: mit
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: zero-shot-classification
+library_name: transformers
 ---
 
-# Model Trained Using AutoTrain
 
-- Problem type: Text Classification
+# LogicSpine/address-large-text-classifier
 
-## Validation Metrics
-loss: 1.3794080018997192
+## Model Description
+`LogicSpine/address-large-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
+
+### Installation
 
-f1_macro: 0.21842933805832918
+To use this model, you need to install the `transformers` library:
 
-f1_micro: 0.4551574223406493
+```bash
+pip install transformers torch
+```
 
-f1_weighted: 0.306703002026862
+### Loading the Model
 
-precision_macro: 0.19546905037281545
+You can easily load and use this model for zero-shot classification using Hugging Face's pipeline API.
 
-precision_micro: 0.4551574223406493
+```
+from transformers import pipeline
 
-precision_weighted: 0.2510467302490216
+# Load the zero-shot classification pipeline with the custom model
+classifier = pipeline("zero-shot-classification", 
+                      model="LogicSpine/address-large-text-classifier")
 
-recall_macro: 0.2811753463927377
+# Define your input text and candidate labels
+text = "Delhi, India"
+candidate_labels = ["Country", "Department", "Laboratory", "College", "District", "Academy"]
 
-recall_micro: 0.4551574223406493
+# Perform classification
+result = classifier(text, candidate_labels)
 
-recall_weighted: 0.4551574223406493
+# 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: 0.4551574223406493
+**loss:** 1.3794080018997192
+**f1_macro:** 0.21842933805832918
+**f1_micro:** 0.4551574223406493
+**f1_weighted:** 0.306703002026862
+**precision_macro:** 0.19546905037281545
+**precision_micro:** 0.4551574223406493
+**precision_weighted:** 0.2510467302490216
+**recall_macro:** 0.2811753463927377
+**recall_micro:** 0.4551574223406493
+**recall_weighted:** 0.4551574223406493
+**accuracy:** 0.4551574223406493