update model card README.md

README.md

@@ -20,11 +20,11 @@ model-index:
     - name: Accuracy
       type: accuracy
       value:
-        accuracy: 0.5615577889447236
+        accuracy: 0.6011306532663316
     - name: F1
       type: f1
       value:
-        f1: 0.5213901124963216
+        f1: 0.5956396413406886
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -34,9 +34,9 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [](https://huggingface.co/) on the preprocessed1024_config dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.9396
-- Accuracy: {'accuracy': 0.5615577889447236}
-- F1: {'f1': 0.5213901124963216}
+- Loss: 1.1353
+- Accuracy: {'accuracy': 0.6011306532663316}
+- F1: {'f1': 0.5956396413406886}
 
 ## Model description
 
@@ -61,13 +61,22 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 1
+- num_epochs: 10
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                         |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|
-| 1.221         | 1.0   | 796  | 0.9396          | {'accuracy': 0.5615577889447236} | {'f1': 0.5213901124963216} |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                          |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------:|
+| 1.224         | 1.0   | 796  | 0.9884          | {'accuracy': 0.5276381909547738} | {'f1': 0.40344173017767304} |
+| 0.96          | 2.0   | 1592 | 0.9255          | {'accuracy': 0.5621859296482412} | {'f1': 0.5134011716404221}  |
+| 0.8878        | 3.0   | 2388 | 0.9308          | {'accuracy': 0.574748743718593}  | {'f1': 0.46867195041352344} |
+| 0.809         | 4.0   | 3184 | 0.8904          | {'accuracy': 0.6067839195979899} | {'f1': 0.5799288651427482}  |
+| 0.7541        | 5.0   | 3980 | 0.8936          | {'accuracy': 0.5954773869346733} | {'f1': 0.5938876317530138}  |
+| 0.6904        | 6.0   | 4776 | 0.8760          | {'accuracy': 0.6118090452261307} | {'f1': 0.6023012293668115}  |
+| 0.6195        | 7.0   | 5572 | 1.0032          | {'accuracy': 0.5917085427135679} | {'f1': 0.5834559014249068}  |
+| 0.5766        | 8.0   | 6368 | 1.0268          | {'accuracy': 0.6023869346733668} | {'f1': 0.5779800559497847}  |
+| 0.4963        | 9.0   | 7164 | 1.0460          | {'accuracy': 0.5992462311557789} | {'f1': 0.5875334711293277}  |
+| 0.4323        | 10.0  | 7960 | 1.1353          | {'accuracy': 0.6011306532663316} | {'f1': 0.5956396413406886}  |
 
 
 ### Framework versions