update model card README.md

README.md

@@ -20,7 +20,7 @@ model-index:
     metrics:
     - name: Accuracy
       type: accuracy
-      value: 0.7383935151068534
+      value: 0.7258658806190126
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,17 +30,17 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the silicone dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.8422
-- Accuracy: 0.7384
-- Micro-precision: 0.7384
-- Micro-recall: 0.7384
-- Micro-f1: 0.7384
-- Macro-precision: 0.4914
-- Macro-recall: 0.4392
-- Macro-f1: 0.4373
-- Weighted-precision: 0.7095
-- Weighted-recall: 0.7384
-- Weighted-f1: 0.7133
+- Loss: 0.9158
+- Accuracy: 0.7259
+- Micro-precision: 0.7259
+- Micro-recall: 0.7259
+- Micro-f1: 0.7259
+- Macro-precision: 0.3430
+- Macro-recall: 0.3267
+- Macro-f1: 0.3195
+- Weighted-precision: 0.6825
+- Weighted-recall: 0.7259
+- Weighted-f1: 0.6938
 
 ## Model description
 
@@ -65,14 +65,13 @@ 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 |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
-| 0.8271        | 1.0   | 5960  | 0.8467          | 0.7410   | 0.7410          | 0.7410       | 0.7410   | 0.4798          | 0.4163       | 0.4203   | 0.7098             | 0.7410          | 0.7165      |
-| 0.7774        | 2.0   | 11920 | 0.8422          | 0.7384   | 0.7384          | 0.7384       | 0.7384   | 0.4914          | 0.4392       | 0.4373   | 0.7095             | 0.7384          | 0.7133      |
+| 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
+| 0.9087        | 1.0   | 2980 | 0.9158          | 0.7259   | 0.7259          | 0.7259       | 0.7259   | 0.3430          | 0.3267       | 0.3195   | 0.6825             | 0.7259          | 0.6938      |
 
 
 ### Framework versions