End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9698795180722891
+      value: 0.9979716024340771
     - name: Recall
       type: recall
-      value: 0.9797160243407708
+      value: 0.9979716024340771
     - name: F1
       type: f1
-      value: 0.9747729566094854
+      value: 0.9979716024340771
     - name: Accuracy
       type: accuracy
-      value: 0.9964187908152518
+      value: 0.9997893406361913
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the generated dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0826
-- Precision: 0.9699
-- Recall: 0.9797
-- F1: 0.9748
-- Accuracy: 0.9964
+- Loss: 0.0040
+- Precision: 0.9980
+- Recall: 0.9980
+- F1: 0.9980
+- Accuracy: 0.9998
 
 ## Model description
 
@@ -73,14 +73,22 @@ 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: 200
+- training_steps: 1000
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 100  | 0.1528          | 0.9478    | 0.9574 | 0.9526 | 0.9941   |
-| No log        | 2.0   | 200  | 0.0826          | 0.9699    | 0.9797 | 0.9748 | 0.9964   |
+| No log        | 1.0   | 100  | 0.1249          | 0.796     | 0.8073 | 0.8016 | 0.9785   |
+| No log        | 2.0   | 200  | 0.0338          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
+| No log        | 3.0   | 300  | 0.0194          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
+| No log        | 4.0   | 400  | 0.0153          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
+| 0.1446        | 5.0   | 500  | 0.0126          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
+| 0.1446        | 6.0   | 600  | 0.0102          | 0.9739    | 0.9858 | 0.9798 | 0.9973   |
+| 0.1446        | 7.0   | 700  | 0.0065          | 0.9959    | 0.9939 | 0.9949 | 0.9994   |
+| 0.1446        | 8.0   | 800  | 0.0045          | 0.9959    | 0.9959 | 0.9959 | 0.9996   |
+| 0.1446        | 9.0   | 900  | 0.0052          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
+| 0.0103        | 10.0  | 1000 | 0.0040          | 0.9980    | 0.9980 | 0.9980 | 0.9998   |
 
 
 ### Framework versions