Upload roberta_fever_plus_adv_samples_and_lora/README.md with huggingface_hub

roberta_fever_plus_adv_samples_and_lora/README.md

@@ -13,31 +13,38 @@ metrics:
   - 0
   - 1
   - 2
+  - 3
   train_loss:
   - 1.0910345315933228
   - 0.9730978608131409
   - 1.033179521560669
+  - 0.7346034049987793
   val_loss:
   - 1.020356297492981
   - 1.1563981771469116
   - 1.0029528141021729
+  - 0.9826301336288452
   train_acc:
   - 0.5
   - 0.6153846383094788
   - 0.5
+  - 0.5384615659713745
   val_acc:
   - 0.47814685106277466
   - 0.46328672766685486
   - 0.4943181872367859
+  - 0.46241259574890137
   train_f1_score:
   - 0.5
   - 0.6153846383094788
   - 0.5
+  - 0.5384615659713745
   val_f1_score:
   - 0.47814685106277466
   - 0.46328672766685486
   - 0.4943181872367859
-  best_metric: 1.0029528141021729
+  - 0.46241259574890137
+  best_metric: 0.9826301336288452
 model-index:
 - name: nli-fever
   results:
@@ -49,7 +56,7 @@ model-index:
       type: fever
     metrics:
     - type: acc
-      value: '0.49'
+      value: '0.46'
       name: Accuracy
       verified: false
 ---
@@ -74,10 +81,10 @@ The model was trained on the FEVER (Fact Extraction and VERification) dataset.
 
 ## Training procedure
 
-The model was trained for [0, 1, 2] epochs
-with a final loss of 1.0029528141021729, an
-accuracy of 0.4943181872367859, and
-F1 score of 0.4943181872367859.
+The model was trained for [0, 1, 2, 3] epochs
+with a final loss of 0.9826301336288452, an
+accuracy of 0.46241259574890137, and
+F1 score of 0.46241259574890137.
 
 ## How to use