update model card README.md

README.md

@@ -22,16 +22,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9265618077093487
+      value: 0.9300908486594284
     - name: Recall
       type: recall
-      value: 0.9357870007830854
+      value: 0.9391430808815304
     - name: F1
       type: f1
-      value: 0.9311515556297656
+      value: 0.9345950459226274
     - name: Accuracy
       type: accuracy
-      value: 0.9838117781625813
+      value: 0.9842407104389407
 ---
 
 <!-- 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: 0.0614
-- Precision: 0.9266
-- Recall: 0.9358
-- F1: 0.9312
-- Accuracy: 0.9838
+- Loss: 0.0589
+- Precision: 0.9301
+- Recall: 0.9391
+- F1: 0.9346
+- Accuracy: 0.9842
 
 ## Model description
 
@@ -76,14 +76,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.2393        | 1.0   | 878  | 0.0719          | 0.9105    | 0.9207 | 0.9156 | 0.9806   |
-| 0.051         | 2.0   | 1756 | 0.0619          | 0.9203    | 0.9335 | 0.9269 | 0.9828   |
-| 0.0308        | 3.0   | 2634 | 0.0614          | 0.9266    | 0.9358 | 0.9312 | 0.9838   |
+| 0.2402        | 1.0   | 878  | 0.0692          | 0.9177    | 0.9248 | 0.9213 | 0.9815   |
+| 0.0506        | 2.0   | 1756 | 0.0600          | 0.9249    | 0.9361 | 0.9305 | 0.9836   |
+| 0.0304        | 3.0   | 2634 | 0.0589          | 0.9301    | 0.9391 | 0.9346 | 0.9842   |
 
 
 ### Framework versions
 
-- Transformers 4.12.3
-- Pytorch 1.9.0+cu111
-- Datasets 1.15.1
+- Transformers 4.12.5
+- Pytorch 1.10.0+cu111
+- Datasets 1.16.1
 - Tokenizers 0.10.3