End of training

README.md

@@ -18,14 +18,14 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.8456
-- Answer: {'precision': 0.5559701492537313, 'recall': 0.5525339925834364, 'f1': 0.5542467451952883, 'number': 809}
-- Header: {'precision': 0.2391304347826087, 'recall': 0.09243697478991597, 'f1': 0.13333333333333333, 'number': 119}
-- Question: {'precision': 0.674055829228243, 'recall': 0.7708920187793428, 'f1': 0.7192290845378887, 'number': 1065}
-- Overall Precision: 0.6185
-- Overall Recall: 0.6417
-- Overall F1: 0.6299
-- Overall Accuracy: 0.7467
+- Loss: 1.5594
+- Answer: {'precision': 0.03052064631956912, 'recall': 0.042027194066749075, 'f1': 0.035361414456578255, 'number': 809}
+- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
+- Question: {'precision': 0.18327402135231316, 'recall': 0.19342723004694837, 'f1': 0.18821379625399726, 'number': 1065}
+- Overall Precision: 0.1072
+- Overall Recall: 0.1204
+- Overall F1: 0.1134
+- Overall Accuracy: 0.4038
 
 ## Model description
 
@@ -55,10 +55,10 @@ 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 |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 1.0112        | 1.0   | 10   | 0.9155          | {'precision': 0.505938242280285, 'recall': 0.5265760197775031, 'f1': 0.5160508782556026, 'number': 809}  | {'precision': 0.17647058823529413, 'recall': 0.05042016806722689, 'f1': 0.07843137254901959, 'number': 119} | {'precision': 0.6454248366013072, 'recall': 0.7417840375586855, 'f1': 0.690257754477938, 'number': 1065} | 0.5819            | 0.6131         | 0.5971     | 0.7295           |
-| 0.7911        | 2.0   | 20   | 0.8456          | {'precision': 0.5559701492537313, 'recall': 0.5525339925834364, 'f1': 0.5542467451952883, 'number': 809} | {'precision': 0.2391304347826087, 'recall': 0.09243697478991597, 'f1': 0.13333333333333333, 'number': 119}  | {'precision': 0.674055829228243, 'recall': 0.7708920187793428, 'f1': 0.7192290845378887, 'number': 1065} | 0.6185            | 0.6417         | 0.6299     | 0.7467           |
+| Training Loss | Epoch | Step | Validation Loss | Answer                                                                                                         | Header                                                      | Question                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 1.8318        | 1.0   | 10   | 1.6681          | {'precision': 0.009746588693957114, 'recall': 0.012360939431396786, 'f1': 0.010899182561307902, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0981169474727453, 'recall': 0.09295774647887324, 'f1': 0.09546769527483126, 'number': 1065}  | 0.0535            | 0.0547         | 0.0541     | 0.3444           |
+| 1.5836        | 2.0   | 20   | 1.5594          | {'precision': 0.03052064631956912, 'recall': 0.042027194066749075, 'f1': 0.035361414456578255, 'number': 809}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18327402135231316, 'recall': 0.19342723004694837, 'f1': 0.18821379625399726, 'number': 1065} | 0.1072            | 0.1204         | 0.1134     | 0.4038           |
 
 
 ### Framework versions

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