Update README.md

README.md

@@ -1,3 +1,99 @@
 ---
 license: mit
+language:
+- en
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- agriculture
+
+
+
+widget:
+- text: "paddy pest"
+  example_title: "Example- pest"
+- text: "how do I apply for PM-Kisan"
+  example_title: "Example- scheme"
+- text: "Will it rain today"
+  example_title: "Example- weather"
 ---
+
+# Agri-flow Classification Model
+
+This model classifies grievances into five distinct buckets:
+
+- agricultural_scheme
+- agriculture
+- pest
+- seed
+- weather
+- price
+- non_agri
+
+## Description of the Buckets
+
+1. **agricultural_scheme**: 
+   The farmer query is about schemes in Odisha
+
+2. **agriculture**: 
+   General agri queries
+
+3. **pest**: 
+   The farmer query is about pests
+   
+4. **seed**:
+   The farmer query is about seed varieties
+
+5. **weather** :
+  The farmer query is asking about the weather for a district /place
+  e.g. :  'What's the weather forecast for Sundargarh?'
+
+6. **price** :
+   The farmer query is asking about the price of some crop 
+   e.g.  'Price for paddy'
+
+6. **non_agri** :
+   The farmer query is just some salutation or unrelated to agri
+
+
+## Training Metrics
+
+Epoch 1/1000 - Loss: 0.8210 - Accuracy: 0.7443 - F1 Score: 0.7360
+Validation Accuracy: 0.9037
+Validation F1 Score: 0.9022
+Epoch 2/1000 - Loss: 0.2868 - Accuracy: 0.9199 - F1 Score: 0.9197
+Validation Accuracy: 0.9241
+Validation F1 Score: 0.9236
+Epoch 3/1000 - Loss: 0.1620 - Accuracy: 0.9536 - F1 Score: 0.9534
+Validation Accuracy: 0.9408
+Validation F1 Score: 0.9407
+Epoch 4/1000 - Loss: 0.0975 - Accuracy: 0.9698 - F1 Score: 0.9698
+Validation Accuracy: 0.9457
+Validation F1 Score: 0.9461
+Epoch 5/1000 - Loss: 0.0722 - Accuracy: 0.9777 - F1 Score: 0.9777
+Validation Accuracy: 0.9518
+Validation F1 Score: 0.9520
+Epoch 6/1000 - Loss: 0.0570 - Accuracy: 0.9801 - F1 Score: 0.9801
+Validation Accuracy: 0.9574
+Validation F1 Score: 0.9573
+Epoch 7/1000 - Loss: 0.0426 - Accuracy: 0.9838 - F1 Score: 0.9838
+Validation Accuracy: 0.9601
+Validation F1 Score: 0.9601
+Epoch 8/1000 - Loss: 0.0403 - Accuracy: 0.9850 - F1 Score: 0.9850
+Validation Accuracy: 0.9646
+Validation F1 Score: 0.9646
+Epoch 9/1000 - Loss: 0.0340 - Accuracy: 0.9853 - F1 Score: 0.9853
+Validation Accuracy: 0.9623
+Validation F1 Score: 0.9624
+Epoch 10/1000 - Loss: 0.0307 - Accuracy: 0.9857 - F1 Score: 0.9857
+Validation Accuracy: 0.9640
+Validation F1 Score: 0.9640
+Epoch 11/1000 - Loss: 0.0297 - Accuracy: 0.9873 - F1 Score: 0.9873
+Validation Accuracy: 0.9618
+Validation F1 Score: 0.9618
+Epoch 12/1000 - Loss: 0.0279 - Accuracy: 0.9867 - F1 Score: 0.9867
+Validation Accuracy: 0.9607
+Validation F1 Score: 0.9607
+