update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.999537251272559
+      value: 1.0
     - name: Recall
       type: recall
-      value: 0.999537251272559
+      value: 1.0
     - name: F1
       type: f1
-      value: 0.999537251272559
+      value: 1.0
     - name: Accuracy
       type: accuracy
-      value: 0.9997335485246202
+      value: 1.0
 ---
 
 <!-- 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 [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the ner dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0003
-- Precision: 0.9995
-- Recall: 0.9995
-- F1: 0.9995
-- Accuracy: 0.9997
+- Loss: 0.0000
+- Precision: 1.0
+- Recall: 1.0
+- F1: 1.0
+- Accuracy: 1.0
 
 ## Model description
 
@@ -67,20 +67,22 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 16
-- eval_batch_size: 16
+- train_batch_size: 32
+- eval_batch_size: 32
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 3
+- num_epochs: 5
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 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   |
+| No log        | 1.0   | 344  | 0.0008          | 0.9995    | 0.9994 | 0.9994 | 0.9997   |
+| 0.0027        | 2.0   | 688  | 0.0008          | 0.9994    | 0.9993 | 0.9994 | 0.9996   |
+| 0.0016        | 3.0   | 1032 | 0.0001          | 1.0000    | 0.9999 | 0.9999 | 1.0000   |
+| 0.0016        | 4.0   | 1376 | 0.0000          | 1.0000    | 1.0000 | 1.0000 | 1.0000   |
+| 0.0003        | 5.0   | 1720 | 0.0000          | 1.0       | 1.0    | 1.0    | 1.0      |
 
 
 ### Framework versions