update model card README.md

README.md

@@ -1,6 +1,5 @@
 ---
 license: apache-2.0
-base_model: distilbert-base-uncased
 tags:
 - generated_from_trainer
 datasets:
@@ -25,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.999514136319467
+      value: 0.999537251272559
     - name: Recall
       type: recall
-      value: 0.999560388708931
+      value: 0.999537251272559
     - name: F1
       type: f1
-      value: 0.9995372619791305
+      value: 0.999537251272559
     - name: Accuracy
       type: accuracy
-      value: 0.9997259356253235
+      value: 0.9997335485246202
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -42,11 +41,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 # my_awesome_wnut_model
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ner dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0004
+- Loss: 0.0003
 - Precision: 0.9995
-- Recall: 0.9996
+- Recall: 0.9995
 - F1: 0.9995
 - Accuracy: 0.9997
 
@@ -79,14 +78,14 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0416        | 1.0   | 688  | 0.0029          | 0.9957    | 0.9972 | 0.9965 | 0.9980   |
-| 0.008         | 2.0   | 1376 | 0.0010          | 0.9985    | 0.9990 | 0.9987 | 0.9993   |
-| 0.0023        | 3.0   | 2064 | 0.0004          | 0.9995    | 0.9996 | 0.9995 | 0.9997   |
+| 0.0364        | 1.0   | 688  | 0.0026          | 0.9964    | 0.9965 | 0.9964 | 0.9979   |
+| 0.0088        | 2.0   | 1376 | 0.0008          | 0.9991    | 0.9988 | 0.9990 | 0.9994   |
+| 0.0017        | 3.0   | 2064 | 0.0003          | 0.9995    | 0.9995 | 0.9995 | 0.9997   |
 
 
 ### Framework versions
 
-- Transformers 4.33.2
+- Transformers 4.30.2
 - Pytorch 2.0.1+cu118
-- Datasets 2.14.5
+- Datasets 2.13.1
 - Tokenizers 0.13.3