update model card README.md

README.md

@@ -22,16 +22,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.8911526429774604
+      value: 0.8944236722550557
     - name: Recall
       type: recall
-      value: 0.8766669296930482
+      value: 0.8777321865383098
     - name: F1
       type: f1
-      value: 0.8838504375497215
+      value: 0.8859993229654115
     - name: Accuracy
       type: accuracy
-      value: 0.9906009271141061
+      value: 0.9908228496683563
 ---
 
 <!-- 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 [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the bc4chemd_ner dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0648
-- Precision: 0.8912
-- Recall: 0.8767
-- F1: 0.8839
-- Accuracy: 0.9906
+- Loss: 0.0641
+- Precision: 0.8944
+- Recall: 0.8777
+- F1: 0.8860
+- Accuracy: 0.9908
 
 ## Model description
 
@@ -76,16 +76,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0088        | 1.0   | 1918  | 0.0322          | 0.8763    | 0.8506 | 0.8633 | 0.9894   |
-| 0.0079        | 2.0   | 3836  | 0.0375          | 0.8820    | 0.8571 | 0.8694 | 0.9896   |
-| 0.0085        | 3.0   | 5754  | 0.0382          | 0.8671    | 0.8820 | 0.8744 | 0.9900   |
-| 0.0011        | 4.0   | 7672  | 0.0513          | 0.8895    | 0.8520 | 0.8704 | 0.9896   |
-| 0.0016        | 5.0   | 9590  | 0.0489          | 0.8871    | 0.8721 | 0.8795 | 0.9903   |
-| 0.0002        | 6.0   | 11508 | 0.0541          | 0.8906    | 0.8751 | 0.8828 | 0.9904   |
-| 0.0001        | 7.0   | 13426 | 0.0541          | 0.8794    | 0.8828 | 0.8811 | 0.9904   |
-| 0.0001        | 8.0   | 15344 | 0.0590          | 0.8771    | 0.8881 | 0.8825 | 0.9905   |
-| 0.0           | 9.0   | 17262 | 0.0622          | 0.8901    | 0.8797 | 0.8849 | 0.9906   |
-| 0.0058        | 10.0  | 19180 | 0.0648          | 0.8912    | 0.8767 | 0.8839 | 0.9906   |
+| 0.006         | 1.0   | 1918  | 0.0310          | 0.8697    | 0.8510 | 0.8602 | 0.9894   |
+| 0.0097        | 2.0   | 3836  | 0.0345          | 0.8855    | 0.8637 | 0.8745 | 0.9898   |
+| 0.0058        | 3.0   | 5754  | 0.0359          | 0.8733    | 0.8836 | 0.8784 | 0.9902   |
+| 0.0014        | 4.0   | 7672  | 0.0440          | 0.8723    | 0.8842 | 0.8782 | 0.9903   |
+| 0.0005        | 5.0   | 9590  | 0.0539          | 0.8862    | 0.8673 | 0.8766 | 0.9903   |
+| 0.0001        | 6.0   | 11508 | 0.0558          | 0.8939    | 0.8628 | 0.8781 | 0.9904   |
+| 0.0001        | 7.0   | 13426 | 0.0558          | 0.8846    | 0.8729 | 0.8787 | 0.9903   |
+| 0.0012        | 8.0   | 15344 | 0.0635          | 0.8935    | 0.8696 | 0.8814 | 0.9905   |
+| 0.0           | 9.0   | 17262 | 0.0624          | 0.8897    | 0.8831 | 0.8864 | 0.9908   |
+| 0.0002        | 10.0  | 19180 | 0.0641          | 0.8944    | 0.8777 | 0.8860 | 0.9908   |
 
 
 ### Framework versions