update model card README.md

README.md

@@ -21,7 +21,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.7251289609432572
+      value: 0.7137067059690494
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,17 +31,17 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the silicone dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.8735
-- Accuracy: 0.7251
-- Micro-precision: 0.7251
-- Micro-recall: 0.7251
-- Micro-f1: 0.7251
-- Macro-precision: 0.4896
-- Macro-recall: 0.4091
-- Macro-f1: 0.4149
-- Weighted-precision: 0.6970
-- Weighted-recall: 0.7251
-- Weighted-f1: 0.6990
+- Loss: 0.9634
+- Accuracy: 0.7137
+- Micro-precision: 0.7137
+- Micro-recall: 0.7137
+- Micro-f1: 0.7137
+- Macro-precision: 0.3472
+- Macro-recall: 0.2856
+- Macro-f1: 0.2791
+- Weighted-precision: 0.6730
+- Weighted-recall: 0.7137
+- Weighted-f1: 0.6783
 
 ## Model description
 
@@ -66,14 +66,13 @@ 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: 2
+- num_epochs: 1
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Micro-precision | Micro-recall | Micro-f1 | Macro-precision | Macro-recall | Macro-f1 | Weighted-precision | Weighted-recall | Weighted-f1 |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
-| 0.8614        | 1.0   | 5960  | 0.8909          | 0.7262   | 0.7262          | 0.7262       | 0.7262   | 0.4169          | 0.3872       | 0.3846   | 0.6902             | 0.7262          | 0.7003      |
-| 0.8243        | 2.0   | 11920 | 0.8735          | 0.7251   | 0.7251          | 0.7251       | 0.7251   | 0.4896          | 0.4091       | 0.4149   | 0.6970             | 0.7251          | 0.6990      |
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro-precision | Micro-recall | Micro-f1 | Macro-precision | Macro-recall | Macro-f1 | Weighted-precision | Weighted-recall | Weighted-f1 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
+| 0.9579        | 1.0   | 2980 | 0.9634          | 0.7137   | 0.7137          | 0.7137       | 0.7137   | 0.3472          | 0.2856       | 0.2791   | 0.6730             | 0.7137          | 0.6783      |
 
 
 ### Framework versions