End of training

README.md

@@ -16,14 +16,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the funsd-layoutlmv3 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.9151
-- Answer: {'precision': 0.8149779735682819, 'recall': 0.9057527539779682, 'f1': 0.8579710144927536, 'number': 817}
-- Header: {'precision': 0.49523809523809526, 'recall': 0.4369747899159664, 'f1': 0.4642857142857143, 'number': 119}
-- Question: {'precision': 0.8627272727272727, 'recall': 0.8811513463324049, 'f1': 0.8718419843821773, 'number': 1077}
-- Overall Precision: 0.8239
-- Overall Recall: 0.8649
-- Overall F1: 0.8439
-- Overall Accuracy: 0.7891
+- Loss: 0.8811
+- Answer: {'precision': 0.8316268486916951, 'recall': 0.8947368421052632, 'f1': 0.8620283018867924, 'number': 817}
+- Header: {'precision': 0.4777777777777778, 'recall': 0.36134453781512604, 'f1': 0.41148325358851673, 'number': 119}
+- Question: {'precision': 0.8480349344978166, 'recall': 0.9015784586815228, 'f1': 0.873987398739874, 'number': 1077}
+- Overall Precision: 0.8254
+- Overall Recall: 0.8669
+- Overall F1: 0.8457
+- Overall Accuracy: 0.7806
 
 ## Model description
 
@@ -53,10 +53,10 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.7154        | 5.26  | 100  | 0.7542          | {'precision': 0.8251173708920188, 'recall': 0.8604651162790697, 'f1': 0.8424206111443978, 'number': 817} | {'precision': 0.45054945054945056, 'recall': 0.3445378151260504, 'f1': 0.3904761904761904, 'number': 119} | {'precision': 0.8157248157248157, 'recall': 0.924791086350975, 'f1': 0.866840731070496, 'number': 1077}   | 0.8041            | 0.8644         | 0.8331     | 0.7915           |
-| 0.1665        | 10.53 | 200  | 0.9151          | {'precision': 0.8149779735682819, 'recall': 0.9057527539779682, 'f1': 0.8579710144927536, 'number': 817} | {'precision': 0.49523809523809526, 'recall': 0.4369747899159664, 'f1': 0.4642857142857143, 'number': 119} | {'precision': 0.8627272727272727, 'recall': 0.8811513463324049, 'f1': 0.8718419843821773, 'number': 1077} | 0.8239            | 0.8649         | 0.8439     | 0.7891           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                     | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.7083        | 5.26  | 100  | 0.7032          | {'precision': 0.8124293785310734, 'recall': 0.8800489596083231, 'f1': 0.8448883666274971, 'number': 817} | {'precision': 0.4852941176470588, 'recall': 0.2773109243697479, 'f1': 0.35294117647058826, 'number': 119}  | {'precision': 0.8360375747224594, 'recall': 0.9090064995357474, 'f1': 0.8709964412811388, 'number': 1077} | 0.8150            | 0.8599         | 0.8368     | 0.8089           |
+| 0.1639        | 10.53 | 200  | 0.8811          | {'precision': 0.8316268486916951, 'recall': 0.8947368421052632, 'f1': 0.8620283018867924, 'number': 817} | {'precision': 0.4777777777777778, 'recall': 0.36134453781512604, 'f1': 0.41148325358851673, 'number': 119} | {'precision': 0.8480349344978166, 'recall': 0.9015784586815228, 'f1': 0.873987398739874, 'number': 1077}  | 0.8254            | 0.8669         | 0.8457     | 0.7806           |
 
 
 ### Framework versions

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