update model card README.md

README.md

@@ -21,16 +21,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.7192068453790534
+      value: 0.7193353093271111
     - name: Recall
       type: recall
-      value: 0.8313138686131387
+      value: 0.8325912408759124
     - name: F1
       type: f1
-      value: 0.7712075299216197
+      value: 0.7718307000033834
     - name: Accuracy
       type: accuracy
-      value: 0.9063193640796039
+      value: 0.9057438991228902
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -40,11 +40,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the jnlpba dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3149
-- Precision: 0.7192
-- Recall: 0.8313
-- F1: 0.7712
-- Accuracy: 0.9063
+- Loss: 0.3674
+- Precision: 0.7193
+- Recall: 0.8326
+- F1: 0.7718
+- Accuracy: 0.9057
 
 ## Model description
 
@@ -69,15 +69,17 @@ 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: 3
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.2578        | 1.0   | 1160 | 0.2873          | 0.7118    | 0.8236 | 0.7636 | 0.9028   |
-| 0.1953        | 2.0   | 2320 | 0.3022          | 0.7149    | 0.825  | 0.7660 | 0.9048   |
-| 0.1572        | 3.0   | 3480 | 0.3149          | 0.7192    | 0.8313 | 0.7712 | 0.9063   |
+| 0.2584        | 1.0   | 1160 | 0.2930          | 0.7052    | 0.8246 | 0.7603 | 0.9019   |
+| 0.1966        | 2.0   | 2320 | 0.3023          | 0.7175    | 0.8247 | 0.7674 | 0.9056   |
+| 0.1577        | 3.0   | 3480 | 0.3171          | 0.7165    | 0.8228 | 0.7659 | 0.9047   |
+| 0.131         | 4.0   | 4640 | 0.3413          | 0.7201    | 0.8292 | 0.7708 | 0.9054   |
+| 0.1073        | 5.0   | 5800 | 0.3674          | 0.7193    | 0.8326 | 0.7718 | 0.9057   |
 
 
 ### Framework versions