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roberta_fever/README.md

@@ -12,18 +12,18 @@ metrics:
   epoch:
   - 0
   train_loss:
-  - 0.23494349420070648
+  - 0.0019978578202426434
   val_loss:
-  - 0.7196927666664124
+  - 2.2035093307495117
   train_acc:
   - 1.0
   val_acc:
-  - 0.743881106376648
+  - 0.7333915829658508
   train_f1_score:
   - 1.0
   val_f1_score:
-  - 0.743881106376648
-  best_metric: 0.7196927666664124
+  - 0.7333915829658508
+  best_metric: 2.2035093307495117
 model-index:
 - name: nli-fever
   results:
@@ -35,7 +35,7 @@ model-index:
       type: fever
     metrics:
     - type: acc
-      value: '0.74'
+      value: '0.73'
       name: Accuracy
       verified: false
 ---
@@ -61,9 +61,9 @@ The model was trained on the FEVER (Fact Extraction and VERification) dataset.
 ## Training procedure
 
 The model was trained for [0] epochs
-with a final loss of 0.7196927666664124, an
-accuracy of 0.743881106376648, and
-F1 score of 0.743881106376648.
+with a final loss of 2.2035093307495117, an
+accuracy of 0.7333915829658508, and
+F1 score of 0.7333915829658508.
 
 ## How to use
 
@@ -120,7 +120,6 @@ Limitations and bias
 
 ## Plots
 ![Labels distribution plots](roberta_fever/label_distribution.png)
-![loss plots
 ![loss plots](roberta_fever/loss_plot.png)
 ![accuracy plots](roberta_fever/accuracy_plot.png)
 ![f1 score plots](roberta_fever/f1_score_plot.png)