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End of training

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README.md CHANGED
@@ -14,20 +14,16 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.3110
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- - Footer: {'precision': 0.9177158273381295, 'recall': 0.8951754385964912, 'f1': 0.9063055062166964, 'number': 2280}
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- - Header: {'precision': 0.5789971617786187, 'recall': 0.6435331230283912, 'f1': 0.6095617529880478, 'number': 951}
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- - Able: {'precision': 0.15821771611526148, 'recall': 0.4848732624693377, 'f1': 0.23858378595855967, 'number': 1223}
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- - Aption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 825}
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- - Ext: {'precision': 0.25493653032440056, 'recall': 0.40928389470704785, 'f1': 0.3141770776751765, 'number': 3533}
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- - Icture: {'precision': 0.013513513513513514, 'recall': 0.018092105263157895, 'f1': 0.01547116736990155, 'number': 608}
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- - Itle: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
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- - Ootnote: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 145}
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- - Ormula: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 360}
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- - Overall Precision: 0.3480
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- - Overall Recall: 0.4682
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- - Overall F1: 0.3992
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- - Overall Accuracy: 0.7076
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  ## Model description
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@@ -56,10 +52,10 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Footer | Header | Able | Aption | Ext | Icture | Itle | Ootnote | Ormula | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 0.451 | 1.0 | 500 | 1.4545 | {'precision': 0.7658186562296151, 'recall': 0.5149122807017544, 'f1': 0.6157880933648046, 'number': 2280} | {'precision': 1.0, 'recall': 0.0010515247108307045, 'f1': 0.0021008403361344537, 'number': 951} | {'precision': 0.11016949152542373, 'recall': 0.3507767784137367, 'f1': 0.16767637287473128, 'number': 1223} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 825} | {'precision': 0.20891744548286603, 'recall': 0.30370789697141237, 'f1': 0.24754873687853268, 'number': 3533} | {'precision': 0.018442622950819672, 'recall': 0.029605263157894735, 'f1': 0.022727272727272728, 'number': 608} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 145} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 360} | 0.2335 | 0.2683 | 0.2497 | 0.6695 |
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- | 0.2521 | 2.0 | 1000 | 1.3110 | {'precision': 0.9177158273381295, 'recall': 0.8951754385964912, 'f1': 0.9063055062166964, 'number': 2280} | {'precision': 0.5789971617786187, 'recall': 0.6435331230283912, 'f1': 0.6095617529880478, 'number': 951} | {'precision': 0.15821771611526148, 'recall': 0.4848732624693377, 'f1': 0.23858378595855967, 'number': 1223} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 825} | {'precision': 0.25493653032440056, 'recall': 0.40928389470704785, 'f1': 0.3141770776751765, 'number': 3533} | {'precision': 0.013513513513513514, 'recall': 0.018092105263157895, 'f1': 0.01547116736990155, 'number': 608} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 145} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 360} | 0.3480 | 0.4682 | 0.3992 | 0.7076 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1562
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+ - Footer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
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+ - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
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+ - Able: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
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+ - Aption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
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+ - Ext: {'precision': 0.06153846153846154, 'recall': 0.4, 'f1': 0.10666666666666667, 'number': 10}
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+ - Overall Precision: 0.0310
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+ - Overall Recall: 0.1739
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+ - Overall F1: 0.0526
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+ - Overall Accuracy: 0.8882
 
 
 
 
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Footer | Header | Able | Aption | Ext | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 2.0796 | 1.0 | 5 | 1.4462 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.05063291139240506, 'recall': 0.4, 'f1': 0.0898876404494382, 'number': 10} | 0.0255 | 0.1739 | 0.0444 | 0.8518 |
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+ | 1.2478 | 2.0 | 10 | 1.1562 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.06153846153846154, 'recall': 0.4, 'f1': 0.10666666666666667, 'number': 10} | 0.0310 | 0.1739 | 0.0526 | 0.8882 |
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  ### Framework versions
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