End of training

README.md

@@ -25,13 +25,13 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9622641509433962
+      value: 0.9707271010387157
     - name: Recall
       type: recall
-      value: 0.9659090909090909
+      value: 0.9734848484848485
     - name: F1
       type: f1
-      value: 0.9640831758034027
+      value: 0.9721040189125295
 ---
 
 <!-- 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-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract) on the source_data dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0017
-- Accuracy Score: 0.9995
-- Precision: 0.9623
-- Recall: 0.9659
-- F1: 0.9641
+- Loss: 0.0016
+- Accuracy Score: 0.9996
+- Precision: 0.9707
+- Recall: 0.9735
+- F1: 0.9721
 
 ## Model description
 
@@ -77,7 +77,7 @@ No additional optimizer arguments
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1     |
 |:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
-| 0.0009        | 1.0   | 864  | 0.0017          | 0.9995         | 0.9623    | 0.9659 | 0.9641 |
+| 0.0012        | 1.0   | 864  | 0.0016          | 0.9996         | 0.9707    | 0.9735 | 0.9721 |
 
 
 ### Framework versions