update model card README.md

README.md

@@ -20,16 +20,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.8746976294146106
+      value: 0.9059871350816427
     - name: Recall
       type: recall
-      value: 0.904
+      value: 0.9155
     - name: F1
       type: f1
-      value: 0.8891074502089993
+      value: 0.9107187266849044
     - name: Accuracy
       type: accuracy
-      value: 0.8368167202572347
+      value: 0.8407211759301791
 ---
 
 <!-- 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: 0.6541
-- Precision: 0.8747
-- Recall: 0.904
-- F1: 0.8891
-- Accuracy: 0.8368
+- Loss: 0.8860
+- Precision: 0.9060
+- Recall: 0.9155
+- F1: 0.9107
+- Accuracy: 0.8407
 
 ## Model description
 
@@ -63,33 +63,27 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 1e-05
-- train_batch_size: 6
+- train_batch_size: 4
 - eval_batch_size: 16
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- training_steps: 400
+- training_steps: 1000
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 25   | 1.2831          | 0.4033    | 0.4795 | 0.4381 | 0.6092   |
-| No log        | 2.0   | 50   | 0.8178          | 0.7266    | 0.7935 | 0.7586 | 0.7748   |
-| No log        | 3.0   | 75   | 0.6843          | 0.7951    | 0.8345 | 0.8143 | 0.7990   |
-| No log        | 4.0   | 100  | 0.6317          | 0.8024    | 0.861  | 0.8307 | 0.8161   |
-| No log        | 5.0   | 125  | 0.5964          | 0.8260    | 0.897  | 0.8600 | 0.8234   |
-| No log        | 6.0   | 150  | 0.6050          | 0.8204    | 0.87   | 0.8445 | 0.8207   |
-| No log        | 7.0   | 175  | 0.6281          | 0.8203    | 0.8765 | 0.8475 | 0.8168   |
-| No log        | 8.0   | 200  | 0.6228          | 0.8449    | 0.8985 | 0.8709 | 0.8235   |
-| No log        | 9.0   | 225  | 0.6213          | 0.8345    | 0.88   | 0.8567 | 0.8266   |
-| No log        | 10.0  | 250  | 0.6173          | 0.8450    | 0.897  | 0.8702 | 0.8357   |
-| No log        | 11.0  | 275  | 0.6476          | 0.8388    | 0.8895 | 0.8634 | 0.8299   |
-| No log        | 12.0  | 300  | 0.6359          | 0.8584    | 0.8945 | 0.8761 | 0.8382   |
-| No log        | 13.0  | 325  | 0.6469          | 0.8759    | 0.907  | 0.8912 | 0.8395   |
-| No log        | 14.0  | 350  | 0.6510          | 0.8729    | 0.9035 | 0.8880 | 0.8373   |
-| No log        | 15.0  | 375  | 0.6555          | 0.8656    | 0.902  | 0.8834 | 0.8354   |
-| No log        | 16.0  | 400  | 0.6541          | 0.8747    | 0.904  | 0.8891 | 0.8368   |
+| 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   |
 
 
 ### Framework versions