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: 0.8293
-- Answer: {'precision': 0.8251986379114642, 'recall': 0.8898408812729498, 'f1': 0.8563015312131919, 'number': 817}
-- Header: {'precision': 0.46987951807228917, 'recall': 0.3277310924369748, 'f1': 0.38613861386138615, 'number': 119}
-- Question: {'precision': 0.852014010507881, 'recall': 0.903435468895079, 'f1': 0.8769716088328076, 'number': 1077}
-- Overall Precision: 0.8257
-- Overall Recall: 0.8639
-- Overall F1: 0.8444
-- Overall Accuracy: 0.8141
+- 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
 
 ## 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.7356        | 5.26  | 100  | 0.6810          | {'precision': 0.8133484162895928, 'recall': 0.8800489596083231, 'f1': 0.8453850676072897, 'number': 817} | {'precision': 0.5333333333333333, 'recall': 0.2689075630252101, 'f1': 0.35754189944134074, 'number': 119}  | {'precision': 0.8194331983805668, 'recall': 0.9396471680594243, 'f1': 0.8754325259515571, 'number': 1077} | 0.8091            | 0.8758         | 0.8411     | 0.8040           |
-| 0.1783        | 10.53 | 200  | 0.8293          | {'precision': 0.8251986379114642, 'recall': 0.8898408812729498, 'f1': 0.8563015312131919, 'number': 817} | {'precision': 0.46987951807228917, 'recall': 0.3277310924369748, 'f1': 0.38613861386138615, 'number': 119} | {'precision': 0.852014010507881, 'recall': 0.903435468895079, 'f1': 0.8769716088328076, 'number': 1077}   | 0.8257            | 0.8639         | 0.8444     | 0.8141           |
+| 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           |
 
 
 ### Framework versions

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