update model card README.md

README.md

@@ -23,16 +23,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.8241758241758241
+      value: 0.8308823529411765
     - name: Recall
       type: recall
-      value: 0.8576874205844981
+      value: 0.8614993646759848
     - name: F1
       type: f1
-      value: 0.8405977584059776
+      value: 0.8459139114160948
     - name: Accuracy
       type: accuracy
-      value: 0.985111585724456
+      value: 0.9845853839725137
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -42,11 +42,11 @@ 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 ncbi_disease dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0621
-- Precision: 0.8242
-- Recall: 0.8577
-- F1: 0.8406
-- Accuracy: 0.9851
+- Loss: 0.0816
+- Precision: 0.8309
+- Recall: 0.8615
+- F1: 0.8459
+- Accuracy: 0.9846
 
 ## Model description
 
@@ -70,16 +70,18 @@ The following hyperparameters were used during training:
 - eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 3
+- lr_scheduler_type: cosine_with_restarts
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0982        | 1.0   | 680  | 0.0582          | 0.7792    | 0.7891 | 0.7841 | 0.9818   |
-| 0.0426        | 2.0   | 1360 | 0.0564          | 0.8102    | 0.8463 | 0.8278 | 0.9848   |
-| 0.0123        | 3.0   | 2040 | 0.0621          | 0.8242    | 0.8577 | 0.8406 | 0.9851   |
+| 0.0986        | 1.0   | 680  | 0.0657          | 0.7476    | 0.8018 | 0.7738 | 0.9806   |
+| 0.0427        | 2.0   | 1360 | 0.0585          | 0.7726    | 0.8590 | 0.8135 | 0.9830   |
+| 0.0127        | 3.0   | 2040 | 0.0616          | 0.8420    | 0.8602 | 0.8510 | 0.9849   |
+| 0.0051        | 4.0   | 2720 | 0.0800          | 0.8317    | 0.8602 | 0.8457 | 0.9848   |
+| 0.0041        | 5.0   | 3400 | 0.0816          | 0.8309    | 0.8615 | 0.8459 | 0.9846   |
 
 
 ### Framework versions