End of training

README.md

@@ -21,16 +21,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.8267254038179148
+      value: 0.9474485910129474
     - name: Recall
       type: recall
-      value: 0.8742236024844721
+      value: 0.9658385093167702
     - name: F1
       type: f1
-      value: 0.849811320754717
+      value: 0.9565551710880431
     - name: Accuracy
       type: accuracy
-      value: 0.8629032258064516
+      value: 0.9613752122241087
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -40,11 +40,11 @@ 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/cord dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6607
-- Precision: 0.8267
-- Recall: 0.8742
-- F1: 0.8498
-- Accuracy: 0.8629
+- Loss: 0.2047
+- Precision: 0.9474
+- Recall: 0.9658
+- F1: 0.9566
+- Accuracy: 0.9614
 
 ## Model description
 
@@ -69,22 +69,18 @@ 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: 500
+- training_steps: 1500
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 0.625 | 50   | 2.1674          | 0.4806    | 0.5947 | 0.5316 | 0.5221   |
-| 2.5708        | 1.25  | 100  | 1.5070          | 0.5181    | 0.6328 | 0.5697 | 0.6541   |
-| 2.5708        | 1.875 | 150  | 1.2109          | 0.6437    | 0.7461 | 0.6911 | 0.7432   |
-| 1.4992        | 2.5   | 200  | 1.0374          | 0.7118    | 0.7919 | 0.7497 | 0.7861   |
-| 1.1332        | 3.125 | 250  | 0.9108          | 0.7656    | 0.8292 | 0.7961 | 0.8226   |
-| 1.1332        | 3.75  | 300  | 0.7992          | 0.7847    | 0.8463 | 0.8143 | 0.8374   |
-| 0.9023        | 4.375 | 350  | 0.7404          | 0.8124    | 0.8641 | 0.8375 | 0.8519   |
-| 0.9023        | 5.0   | 400  | 0.6935          | 0.8210    | 0.8727 | 0.8461 | 0.8612   |
-| 0.7884        | 5.625 | 450  | 0.6681          | 0.8254    | 0.8734 | 0.8487 | 0.8625   |
-| 0.7236        | 6.25  | 500  | 0.6607          | 0.8267    | 0.8742 | 0.8498 | 0.8629   |
+| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 3.125  | 250  | 0.7037          | 0.8188    | 0.8665 | 0.8419 | 0.8587   |
+| 1.0839        | 6.25   | 500  | 0.3828          | 0.8926    | 0.9293 | 0.9106 | 0.9223   |
+| 1.0839        | 9.375  | 750  | 0.2811          | 0.9371    | 0.9596 | 0.9482 | 0.9469   |
+| 0.2469        | 12.5   | 1000 | 0.2295          | 0.9401    | 0.9620 | 0.9509 | 0.9529   |
+| 0.2469        | 15.625 | 1250 | 0.2106          | 0.9460    | 0.9658 | 0.9558 | 0.9601   |
+| 0.1263        | 18.75  | 1500 | 0.2047          | 0.9474    | 0.9658 | 0.9566 | 0.9614   |
 
 
 ### Framework versions