update model card README.md

README.md

@@ -20,11 +20,11 @@ model-index:
     - name: Accuracy
       type: accuracy
       value:
-        accuracy: 0.5665829145728644
+        accuracy: 0.5785175879396985
     - name: F1
       type: f1
       value:
-        f1: 0.5549950963329491
+        f1: 0.565251065728165
 ---
 
 <!-- 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: 1.1801
-- Accuracy: {'accuracy': 0.5665829145728644}
-- F1: {'f1': 0.5549950963329491}
+- Loss: 1.1521
+- Accuracy: {'accuracy': 0.5785175879396985}
+- F1: {'f1': 0.565251065728165}
 
 ## Model description
 
@@ -67,16 +67,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy                          | F1                          |
 |:-------------:|:-----:|:----:|:---------------:|:---------------------------------:|:---------------------------:|
-| 1.3412        | 1.0   | 796  | 1.1931          | {'accuracy': 0.4547738693467337}  | {'f1': 0.31154642522501674} |
-| 1.1149        | 2.0   | 1592 | 1.0845          | {'accuracy': 0.4886934673366834}  | {'f1': 0.40829339044510005} |
-| 1.0531        | 3.0   | 2388 | 1.1650          | {'accuracy': 0.48304020100502515} | {'f1': 0.38992060973001436} |
-| 0.917         | 4.0   | 3184 | 0.9950          | {'accuracy': 0.5452261306532663}  | {'f1': 0.50281030200465}    |
-| 0.8633        | 5.0   | 3980 | 1.0152          | {'accuracy': 0.5552763819095478}  | {'f1': 0.511332789082197}   |
-| 0.7747        | 6.0   | 4776 | 1.0201          | {'accuracy': 0.5703517587939698}  | {'f1': 0.523154780871296}   |
-| 0.7133        | 7.0   | 5572 | 1.0345          | {'accuracy': 0.5640703517587939}  | {'f1': 0.5198008328503952}  |
-| 0.659         | 8.0   | 6368 | 1.0702          | {'accuracy': 0.5785175879396985}  | {'f1': 0.5460580312777853}  |
-| 0.5943        | 9.0   | 7164 | 1.1634          | {'accuracy': 0.5734924623115578}  | {'f1': 0.5501266468657362}  |
-| 0.5699        | 10.0  | 7960 | 1.1801          | {'accuracy': 0.5665829145728644}  | {'f1': 0.5549950963329491}  |
+| 1.3433        | 1.0   | 796  | 1.1893          | {'accuracy': 0.4566582914572864}  | {'f1': 0.32080438921262083} |
+| 1.1242        | 2.0   | 1592 | 1.0867          | {'accuracy': 0.48555276381909546} | {'f1': 0.4061780745199038}  |
+| 1.0569        | 3.0   | 2388 | 1.1587          | {'accuracy': 0.49120603015075376} | {'f1': 0.40970823779940124} |
+| 0.9327        | 4.0   | 3184 | 0.9901          | {'accuracy': 0.5452261306532663}  | {'f1': 0.4885626990630958}  |
+| 0.8723        | 5.0   | 3980 | 0.9824          | {'accuracy': 0.5728643216080402}  | {'f1': 0.5365052338942904}  |
+| 0.7803        | 6.0   | 4776 | 1.0071          | {'accuracy': 0.571608040201005}   | {'f1': 0.5246756181464156}  |
+| 0.7198        | 7.0   | 5572 | 1.0233          | {'accuracy': 0.5741206030150754}  | {'f1': 0.5405969058526473}  |
+| 0.6589        | 8.0   | 6368 | 1.0902          | {'accuracy': 0.5816582914572864}  | {'f1': 0.5421523761661359}  |
+| 0.6055        | 9.0   | 7164 | 1.0980          | {'accuracy': 0.5835427135678392}  | {'f1': 0.5601877104043351}  |
+| 0.5722        | 10.0  | 7960 | 1.1521          | {'accuracy': 0.5785175879396985}  | {'f1': 0.565251065728165}   |
 
 
 ### Framework versions