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@@ -24,14 +24,31 @@ This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/
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  It achieves the following results on the evaluation set:
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  - Loss: 0.0949
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- - Loc: {'precision': 0.9289891395154553, 'recall': 0.9336691855583543, 'f1': 0.931323283082077, 'number': 5955}
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- - Misc: {'precision': 0.8191960332920134, 'recall': 0.9140486069946651, 'f1': 0.8640268957788569, 'number': 5061}
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- - Org: {'precision': 0.9199886104783599, 'recall': 0.9367932734125833, 'f1': 0.9283148972848728, 'number': 3449}
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- - Per: {'precision': 0.9687377113645301, 'recall': 0.9456813819577735, 'f1': 0.9570707070707071, 'number': 5210}
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- - Overall Precision: 0.9068
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- - Overall Recall: 0.9324
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- - Overall F1: 0.9194
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- - Overall Accuracy: 0.9904
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Model description
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@@ -60,11 +77,11 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Loc | Misc | Org | Per | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 0.1119 | 1.0 | 5795 | 0.1067 | {'precision': 0.9053637984119267, 'recall': 0.9382031905961377, 'f1': 0.9214910110506349, 'number': 5955} | {'precision': 0.7967393230551125, 'recall': 0.8883619837976684, 'f1': 0.8400597907324365, 'number': 5061} | {'precision': 0.911225658648339, 'recall': 0.9225862568860539, 'f1': 0.9168707679008787, 'number': 3449} | {'precision': 0.958470156461271, 'recall': 0.9523992322456813, 'f1': 0.9554250505439492, 'number': 5210} | 0.8899 | 0.9264 | 0.9078 | 0.9887 |
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- | 0.0724 | 2.0 | 11590 | 0.0949 | {'precision': 0.9289891395154553, 'recall': 0.9336691855583543, 'f1': 0.931323283082077, 'number': 5955} | {'precision': 0.8191960332920134, 'recall': 0.9140486069946651, 'f1': 0.8640268957788569, 'number': 5061} | {'precision': 0.9199886104783599, 'recall': 0.9367932734125833, 'f1': 0.9283148972848728, 'number': 3449} | {'precision': 0.9687377113645301, 'recall': 0.9456813819577735, 'f1': 0.9570707070707071, 'number': 5210} | 0.9068 | 0.9324 | 0.9194 | 0.9904 |
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  ### Framework versions
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  It achieves the following results on the evaluation set:
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  - Loss: 0.0949
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+ - Loc
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+ - Precision: 0.9289891395154553
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+ - Recall: 0.9336691855583543
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+ - F1: 0.931323283082077
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+ - Number: 5955
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+ - Misc
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+ - Precision: 0.8191960332920134
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+ - Recall: 0.9140486069946651
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+ - F1: 0.8640268957788569
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+ - Number: 5061
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+ - Org
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+ - Precision: 0.9199886104783599
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+ - Recall: 0.9367932734125833
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+ - F1: 0.9283148972848728
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+ - Number: 3449
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+ - Per
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+ - Precision: 0.9687377113645301
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+ - Recall: 0.9456813819577735
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+ - F1: 0.9570707070707071
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+ - Number: 5210
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+ - Overall
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+ - Precision: 0.9068
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+ - Recall: 0.9324
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+ - F1: 0.9194
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+ - Accuracy: 0.9904
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Loc Precision | Loc Recall | Loc F1 | Loc Number | Misc Precision | Misc Recall | Misc F1 | Misc Number | Org Precision | Org Recall | Org F1 | Org Number | Per Precision | Per Recall | Per F1 | Per Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:-----:|:--------------:|:-----------------:|:-------------:|:------------:|:--------------:|:-----------------:|:--------------:|:----------:|:--------:|:--------:|:----------:|:-----------:|:----------:|:----------:|:----------:|:---------:|:----------:|:---------:|:-------:|:----------:|:---------:|
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+ | 0.1119 | 1.0 | 5795 | 0.1067 | 0.9054 | 0.9382 | 0.9215 | 5955 | 0.7967 | 0.8884 | 0.8401 | 5061 | 0.9112 | 0.9226 | 0.9169 | 3449 | 0.9585 | 0.9524 | 0.9554 | 5210 | 0.8899 | 0.9264 | 0.9078 | 0.9887 |
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+ | 0.0724 | 2.0 | 11590 | 0.0949 | 0.9290 | 0.9337 | 0.9313 | 5955 | 0.8192 | 0.9140 | 0.8640 | 5061 | 0.9200 | 0.9368 | 0.9283 | 3449 | 0.9687 | 0.9457 | 0.9571 | 5210 | 0.9068 | 0.9324 | 0.9194 | 0.9904 |
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+ * All values in the above chart are rounded to the nearest ten-thousandths.
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  ### Framework versions
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