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: 0.8811
-- 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
+- Loss: 0.9065
+- Answer: {'precision': 0.834096109839817, 'recall': 0.8922888616891065, 'f1': 0.8622117090479007, 'number': 817}
+- Header: {'precision': 0.5319148936170213, 'recall': 0.42016806722689076, 'f1': 0.4694835680751173, 'number': 119}
+- Question: {'precision': 0.8570175438596491, 'recall': 0.9071494893221913, 'f1': 0.8813712223725756, 'number': 1077}
+- Overall Precision: 0.8330
+- Overall Recall: 0.8723
+- Overall F1: 0.8522
+- Overall Accuracy: 0.7918
 
 ## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.7083        | 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           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                   | Header                                                                                                    | Question                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.7017        | 5.26  | 100  | 0.7391          | {'precision': 0.8216340621403913, 'recall': 0.8739290085679314, 'f1': 0.8469750889679716, 'number': 817} | {'precision': 0.4533333333333333, 'recall': 0.2857142857142857, 'f1': 0.3505154639175258, 'number': 119}  | {'precision': 0.8234323432343235, 'recall': 0.9266480965645311, 'f1': 0.8719965050240279, 'number': 1077} | 0.8098            | 0.8674         | 0.8376     | 0.8073           |
+| 0.1656        | 10.53 | 200  | 0.9065          | {'precision': 0.834096109839817, 'recall': 0.8922888616891065, 'f1': 0.8622117090479007, 'number': 817}  | {'precision': 0.5319148936170213, 'recall': 0.42016806722689076, 'f1': 0.4694835680751173, 'number': 119} | {'precision': 0.8570175438596491, 'recall': 0.9071494893221913, 'f1': 0.8813712223725756, 'number': 1077} | 0.8330            | 0.8723         | 0.8522     | 0.7918           |
 
 
 ### Framework versions

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