End of training

README.md

@@ -22,16 +22,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9609494640122511
+      value: 0.963984674329502
     - name: Recall
       type: recall
-      value: 0.9743788819875776
+      value: 0.9767080745341615
     - name: F1
       type: f1
-      value: 0.9676175790285273
+      value: 0.9703046664095644
     - name: Accuracy
       type: accuracy
-      value: 0.9690152801358234
+      value: 0.9702886247877759
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,11 +41,11 @@ 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: 0.1800
-- Precision: 0.9609
-- Recall: 0.9744
-- F1: 0.9676
-- Accuracy: 0.9690
+- Loss: 0.2044
+- Precision: 0.9640
+- Recall: 0.9767
+- F1: 0.9703
+- Accuracy: 0.9703
 
 ## Model description
 
@@ -65,8 +65,8 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 10
-- eval_batch_size: 10
+- train_batch_size: 14
+- eval_batch_size: 14
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
@@ -74,18 +74,18 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.25  | 100  | 0.5802          | 0.8140    | 0.8696 | 0.8408 | 0.8574   |
-| No log        | 2.5   | 200  | 0.2946          | 0.9013    | 0.9433 | 0.9219 | 0.9329   |
-| No log        | 3.75  | 300  | 0.2259          | 0.9409    | 0.9635 | 0.9521 | 0.9571   |
-| No log        | 5.0   | 400  | 0.2496          | 0.9376    | 0.9565 | 0.9470 | 0.9482   |
-| 0.4497        | 6.25  | 500  | 0.2174          | 0.9399    | 0.9596 | 0.9497 | 0.9546   |
-| 0.4497        | 7.5   | 600  | 0.1812          | 0.9535    | 0.9713 | 0.9623 | 0.9648   |
-| 0.4497        | 8.75  | 700  | 0.1699          | 0.9587    | 0.9720 | 0.9653 | 0.9699   |
-| 0.4497        | 10.0  | 800  | 0.1810          | 0.9625    | 0.9752 | 0.9688 | 0.9690   |
-| 0.4497        | 11.25 | 900  | 0.1789          | 0.9647    | 0.9767 | 0.9707 | 0.9694   |
-| 0.0416        | 12.5  | 1000 | 0.1800          | 0.9609    | 0.9744 | 0.9676 | 0.9690   |
+| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.7241  | 100  | 0.4595          | 0.8310    | 0.8859 | 0.8576 | 0.8786   |
+| No log        | 3.4483  | 200  | 0.2637          | 0.9284    | 0.9565 | 0.9423 | 0.9469   |
+| No log        | 5.1724  | 300  | 0.2096          | 0.9513    | 0.9697 | 0.9604 | 0.9626   |
+| No log        | 6.8966  | 400  | 0.2016          | 0.9512    | 0.9689 | 0.96   | 0.9622   |
+| 0.3892        | 8.6207  | 500  | 0.2418          | 0.9453    | 0.9658 | 0.9555 | 0.9593   |
+| 0.3892        | 10.3448 | 600  | 0.2149          | 0.9579    | 0.9713 | 0.9645 | 0.9660   |
+| 0.3892        | 12.0690 | 700  | 0.2090          | 0.9608    | 0.9713 | 0.9660 | 0.9652   |
+| 0.3892        | 13.7931 | 800  | 0.2202          | 0.9580    | 0.9728 | 0.9653 | 0.9673   |
+| 0.3892        | 15.5172 | 900  | 0.2217          | 0.9595    | 0.9744 | 0.9669 | 0.9682   |
+| 0.0278        | 17.2414 | 1000 | 0.2044          | 0.9640    | 0.9767 | 0.9703 | 0.9703   |
 
 
 ### Framework versions

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