update model card README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: apache-2.0
+license: mit
 tags:
 - generated_from_trainer
 datasets:
@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9290076335877863
+      value: 0.9502661343978709
     - name: Recall
       type: recall
-      value: 0.9391467313416382
+      value: 0.9614607876135981
     - name: F1
       type: f1
-      value: 0.9340496683295871
+      value: 0.9558306842897775
     - name: Accuracy
       type: accuracy
-      value: 0.9849767124843803
+      value: 0.9925431252677076
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,13 +41,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-base-uncased-finetuned-ner
 
-This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset.
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0664
-- Precision: 0.9290
-- Recall: 0.9391
-- F1: 0.9340
-- Accuracy: 0.9850
+- Loss: 0.0328
+- Precision: 0.9503
+- Recall: 0.9615
+- F1: 0.9558
+- Accuracy: 0.9925
 
 ## Model description
 
@@ -78,9 +78,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1694        | 1.0   | 878  | 0.0677          | 0.9162    | 0.9141 | 0.9152 | 0.9812   |
-| 0.0571        | 2.0   | 1756 | 0.0657          | 0.9193    | 0.9286 | 0.9239 | 0.9832   |
-| 0.0283        | 3.0   | 2634 | 0.0664          | 0.9290    | 0.9391 | 0.9340 | 0.9850   |
+| 0.1407        | 1.0   | 878  | 0.0407          | 0.9355    | 0.9468 | 0.9411 | 0.9902   |
+| 0.0329        | 2.0   | 1756 | 0.0338          | 0.9471    | 0.9584 | 0.9527 | 0.9920   |
+| 0.0167        | 3.0   | 2634 | 0.0328          | 0.9503    | 0.9615 | 0.9558 | 0.9925   |
 
 
 ### Framework versions