update model card README.md

README.md

@@ -20,16 +20,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9059871350816427
+      value: 0.875725338491296
     - name: Recall
       type: recall
-      value: 0.9155
+      value: 0.9055
     - name: F1
       type: f1
-      value: 0.9107187266849044
+      value: 0.8903638151425762
     - name: Accuracy
       type: accuracy
-      value: 0.8407211759301791
+      value: 0.843706936150666
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -39,11 +39,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/funsd dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.8860
-- Precision: 0.9060
-- Recall: 0.9155
-- F1: 0.9107
-- Accuracy: 0.8407
+- Loss: 0.6187
+- Precision: 0.8757
+- Recall: 0.9055
+- F1: 0.8904
+- Accuracy: 0.8437
 
 ## Model description
 
@@ -68,22 +68,22 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- training_steps: 1000
+- training_steps: 500
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 2.63  | 100  | 0.6111          | 0.7963    | 0.864  | 0.8288 | 0.7987   |
-| No log        | 5.26  | 200  | 0.5861          | 0.8507    | 0.883  | 0.8665 | 0.8266   |
-| No log        | 7.89  | 300  | 0.5856          | 0.8654    | 0.9005 | 0.8826 | 0.8426   |
-| No log        | 10.53 | 400  | 0.6502          | 0.8801    | 0.8995 | 0.8897 | 0.8427   |
-| 0.4088        | 13.16 | 500  | 0.7679          | 0.8880    | 0.904  | 0.8959 | 0.8373   |
-| 0.4088        | 15.79 | 600  | 0.8371          | 0.8820    | 0.904  | 0.8928 | 0.8333   |
-| 0.4088        | 18.42 | 700  | 0.8320          | 0.8931    | 0.9145 | 0.9037 | 0.8336   |
-| 0.4088        | 21.05 | 800  | 0.8494          | 0.8969    | 0.9135 | 0.9051 | 0.8341   |
-| 0.4088        | 23.68 | 900  | 0.8700          | 0.9005    | 0.914  | 0.9072 | 0.8385   |
-| 0.061         | 26.32 | 1000 | 0.8860          | 0.9060    | 0.9155 | 0.9107 | 0.8407   |
+| No log        | 1.32  | 50   | 0.9063          | 0.7006    | 0.757  | 0.7277 | 0.7607   |
+| No log        | 2.63  | 100  | 0.6387          | 0.7930    | 0.858  | 0.8242 | 0.7967   |
+| No log        | 3.95  | 150  | 0.5691          | 0.8171    | 0.8825 | 0.8486 | 0.8254   |
+| No log        | 5.26  | 200  | 0.5723          | 0.8315    | 0.881  | 0.8555 | 0.8223   |
+| No log        | 6.58  | 250  | 0.5897          | 0.8475    | 0.9    | 0.8729 | 0.8292   |
+| No log        | 7.89  | 300  | 0.6122          | 0.8482    | 0.9025 | 0.8745 | 0.8283   |
+| No log        | 9.21  | 350  | 0.6045          | 0.8505    | 0.899  | 0.8741 | 0.8392   |
+| No log        | 10.53 | 400  | 0.5662          | 0.8708    | 0.9    | 0.8852 | 0.8446   |
+| No log        | 11.84 | 450  | 0.5973          | 0.8739    | 0.9045 | 0.8889 | 0.8437   |
+| 0.4305        | 13.16 | 500  | 0.6187          | 0.8757    | 0.9055 | 0.8904 | 0.8437   |
 
 
 ### Framework versions