update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9254922831293241
+      value: 0.9306692773228907
     - name: Recall
       type: recall
-      value: 0.9361205813744842
+      value: 0.9381841019199713
     - name: F1
       type: f1
-      value: 0.9307760927743086
+      value: 0.9344115807345187
     - name: Accuracy
       type: accuracy
-      value: 0.9831488785541885
+      value: 0.9832666156472597
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0966
-- Precision: 0.9255
-- Recall: 0.9361
-- F1: 0.9308
-- Accuracy: 0.9831
+- Loss: 0.1183
+- Precision: 0.9307
+- Recall: 0.9382
+- F1: 0.9344
+- Accuracy: 0.9833
 
 ## Model description
 
@@ -66,29 +66,34 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
+- learning_rate: 2e-05
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 2147483647
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 5
+- num_epochs: 10
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1045        | 1.0   | 1756 | 0.0891          | 0.8908    | 0.9032 | 0.8970 | 0.9747   |
-| 0.044         | 2.0   | 3512 | 0.0809          | 0.9209    | 0.9175 | 0.9192 | 0.9793   |
-| 0.0253        | 3.0   | 5268 | 0.0806          | 0.9268    | 0.9280 | 0.9274 | 0.9821   |
-| 0.0129        | 4.0   | 7024 | 0.0909          | 0.9301    | 0.9341 | 0.9321 | 0.9829   |
-| 0.0042        | 5.0   | 8780 | 0.0966          | 0.9255    | 0.9361 | 0.9308 | 0.9831   |
+| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1081        | 1.0   | 1756  | 0.0963          | 0.8947    | 0.8982 | 0.8964 | 0.9742   |
+| 0.0518        | 2.0   | 3512  | 0.0780          | 0.9219    | 0.9182 | 0.9200 | 0.9803   |
+| 0.0348        | 3.0   | 5268  | 0.0833          | 0.9258    | 0.9271 | 0.9264 | 0.9819   |
+| 0.0268        | 4.0   | 7024  | 0.0900          | 0.9152    | 0.9241 | 0.9196 | 0.9805   |
+| 0.0167        | 5.0   | 8780  | 0.0929          | 0.9225    | 0.9320 | 0.9272 | 0.9822   |
+| 0.0071        | 6.0   | 10536 | 0.1119          | 0.9229    | 0.9270 | 0.9249 | 0.9816   |
+| 0.0056        | 7.0   | 12292 | 0.1073          | 0.9286    | 0.9366 | 0.9326 | 0.9832   |
+| 0.0021        | 8.0   | 14048 | 0.1194          | 0.9285    | 0.9350 | 0.9318 | 0.9829   |
+| 0.0019        | 9.0   | 15804 | 0.1156          | 0.9318    | 0.9376 | 0.9347 | 0.9833   |
+| 0.0011        | 10.0  | 17560 | 0.1183          | 0.9307    | 0.9382 | 0.9344 | 0.9833   |
 
 
 ### Framework versions
 
-- Transformers 4.28.0.dev0
+- Transformers 4.27.4
 - Pytorch 2.0.0+cu118
 - Datasets 2.11.0
 - Tokenizers 0.13.3