End of training

README.md

@@ -25,13 +25,13 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.8178665324159534
+      value: 0.8202296075899624
     - name: Recall
       type: recall
-      value: 0.8541052078161054
+      value: 0.8535064404007361
     - name: F1
       type: f1
-      value: 0.8355931476904174
+      value: 0.8365372228504359
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,11 +41,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract) on the source_data dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1362
-- Accuracy Score: 0.9578
-- Precision: 0.8179
-- Recall: 0.8541
-- F1: 0.8356
+- Loss: 0.1352
+- Accuracy Score: 0.9575
+- Precision: 0.8202
+- Recall: 0.8535
+- F1: 0.8365
 
 ## Model description
 
@@ -79,8 +79,8 @@ No additional optimizer arguments
 
 | Training Loss | Epoch  | Step | Validation Loss | Accuracy Score | Precision | Recall | F1     |
 |:-------------:|:------:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
-| 0.1056        | 0.9994 | 863  | 0.1355          | 0.9556         | 0.8094    | 0.8475 | 0.8280 |
-| 0.075         | 1.9988 | 1726 | 0.1362          | 0.9578         | 0.8179    | 0.8541 | 0.8356 |
+| 0.108         | 0.9994 | 863  | 0.1354          | 0.9557         | 0.8133    | 0.8463 | 0.8294 |
+| 0.0778        | 1.9988 | 1726 | 0.1352          | 0.9575         | 0.8202    | 0.8535 | 0.8365 |
 
 
 ### Framework versions