Update README.md

README.md

@@ -18,7 +18,7 @@ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co
 
 ## Model description
 
-More information needed
+We have finetuned Base Bert model for text classification task. We used intent-detection dataset for traning our model. 
 
 ## Intended uses & limitations
 
@@ -51,3 +51,37 @@ The following hyperparameters were used during training:
 - Pytorch 2.6.0+cu124
 - Datasets 2.14.4
 - Tokenizers 0.21.1
+
+### Results
+
+
+Generating detailed evaluation metrics on the validation set...
+
+Classification Report:
+                       precision    recall  f1-score   support
+
+100_NIGHT_TRIAL_OFFER       1.00      1.00      1.00         4
+   ABOUT_SOF_MATTRESS       1.00      1.00      1.00         2
+         CANCEL_ORDER       1.00      1.00      1.00         2
+        CHECK_PINCODE       1.00      1.00      1.00         2
+                  COD       1.00      1.00      1.00         2
+           COMPARISON       0.33      0.50      0.40         2
+    DELAY_IN_DELIVERY       1.00      1.00      1.00         2
+         DISTRIBUTORS       1.00      1.00      1.00         7
+                  EMI       1.00      1.00      1.00         5
+        ERGO_FEATURES       1.00      1.00      1.00         2
+             LEAD_GEN       1.00      1.00      1.00         4
+        MATTRESS_COST       1.00      0.80      0.89         5
+               OFFERS       1.00      1.00      1.00         2
+         ORDER_STATUS       1.00      0.75      0.86         4
+       ORTHO_FEATURES       1.00      1.00      1.00         4
+              PILLOWS       1.00      1.00      1.00         2
+     PRODUCT_VARIANTS       0.50      0.50      0.50         4
+      RETURN_EXCHANGE       1.00      0.67      0.80         3
+   SIZE_CUSTOMIZATION       0.50      0.50      0.50         2
+             WARRANTY       0.67      1.00      0.80         2
+   WHAT_SIZE_TO_ORDER       0.80      1.00      0.89         4
+
+             accuracy                           0.89        66
+            macro avg       0.90      0.89      0.89        66
+         weighted avg       0.91      0.89      0.90        66