update model card README.md

README.md

@@ -22,16 +22,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9233990962195525
+      value: 0.9299878143347735
     - name: Recall
       type: recall
-      value: 0.9372413021590782
+      value: 0.9391430808815304
     - name: F1
       type: f1
-      value: 0.9302687097490562
+      value: 0.93454302571524
     - name: Accuracy
       type: accuracy
-      value: 0.9833193003637981
+      value: 0.9841453921553053
 ---
 
 <!-- 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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0619
-- Precision: 0.9234
-- Recall: 0.9372
-- F1: 0.9303
-- Accuracy: 0.9833
+- Loss: 0.0635
+- Precision: 0.9300
+- Recall: 0.9391
+- F1: 0.9345
+- Accuracy: 0.9841
 
 ## Model description
 
@@ -65,8 +65,8 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
+- train_batch_size: 8
+- eval_batch_size: 8
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
@@ -76,9 +76,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.2421        | 1.0   | 878  | 0.0750          | 0.9086    | 0.9178 | 0.9132 | 0.9797   |
-| 0.056         | 2.0   | 1756 | 0.0601          | 0.9213    | 0.9363 | 0.9288 | 0.9828   |
-| 0.0319        | 3.0   | 2634 | 0.0619          | 0.9234    | 0.9372 | 0.9303 | 0.9833   |
+| 0.0886        | 1.0   | 1756 | 0.0676          | 0.9198    | 0.9233 | 0.9215 | 0.9809   |
+| 0.0382        | 2.0   | 3512 | 0.0605          | 0.9271    | 0.9360 | 0.9315 | 0.9836   |
+| 0.0247        | 3.0   | 5268 | 0.0635          | 0.9300    | 0.9391 | 0.9345 | 0.9841   |
 
 
 ### Framework versions