End of training

README.md

@@ -25,13 +25,13 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9688679245283018
+      value: 0.9722117202268431
     - name: Recall
       type: recall
-      value: 0.9725378787878788
+      value: 0.9740530303030303
     - name: F1
       type: f1
-      value: 0.9706994328922495
+      value: 0.973131504257332
 ---
 
 <!-- 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: 0.0016
+- Loss: 0.0018
 - Accuracy Score: 0.9996
-- Precision: 0.9689
-- Recall: 0.9725
-- F1: 0.9707
+- Precision: 0.9722
+- Recall: 0.9741
+- F1: 0.9731
 
 ## Model description
 
@@ -79,7 +79,7 @@ No additional optimizer arguments
 
 | Training Loss | Epoch  | Step | Validation Loss | Accuracy Score | Precision | Recall | F1     |
 |:-------------:|:------:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
-| 0.001         | 0.9994 | 863  | 0.0016          | 0.9996         | 0.9689    | 0.9725 | 0.9707 |
+| 0.0008        | 0.9994 | 863  | 0.0018          | 0.9996         | 0.9722    | 0.9741 | 0.9731 |
 
 
 ### Framework versions