Training in progress, step 200

README.md

@@ -16,33 +16,33 @@ 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 cord dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1915
-- Menu.cnt: {'precision': 0.9775784753363229, 'recall': 0.9688888888888889, 'f1': 0.9732142857142856, 'number': 225}
-- Menu.discountprice: {'precision': 0.3888888888888889, 'recall': 0.7, 'f1': 0.5, 'number': 10}
-- Menu.nm: {'precision': 0.9108527131782945, 'recall': 0.9325396825396826, 'f1': 0.9215686274509803, 'number': 252}
-- Menu.num: {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11}
-- Menu.price: {'precision': 0.9723320158102767, 'recall': 0.9919354838709677, 'f1': 0.9820359281437127, 'number': 248}
-- Menu.sub Cnt: {'precision': 0.8421052631578947, 'recall': 0.9411764705882353, 'f1': 0.8888888888888888, 'number': 17}
-- Menu.sub Nm: {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 32}
-- Menu.sub Price: {'precision': 0.8636363636363636, 'recall': 0.95, 'f1': 0.9047619047619048, 'number': 20}
-- Menu.unitprice: {'precision': 0.9848484848484849, 'recall': 0.9558823529411765, 'f1': 0.9701492537313432, 'number': 68}
-- Sub Total.discount Price: {'precision': 0.7777777777777778, 'recall': 1.0, 'f1': 0.8750000000000001, 'number': 7}
-- Sub Total.etc: {'precision': 0.75, 'recall': 0.75, 'f1': 0.75, 'number': 8}
+- Loss: 0.2317
+- Menu.cnt: {'precision': 0.9521739130434783, 'recall': 0.9733333333333334, 'f1': 0.9626373626373628, 'number': 225}
+- Menu.discountprice: {'precision': 0.6, 'recall': 0.6, 'f1': 0.6, 'number': 10}
+- Menu.nm: {'precision': 0.9011406844106464, 'recall': 0.9404761904761905, 'f1': 0.920388349514563, 'number': 252}
+- Menu.num: {'precision': 1.0, 'recall': 0.8181818181818182, 'f1': 0.9, 'number': 11}
+- Menu.price: {'precision': 0.9565217391304348, 'recall': 0.9758064516129032, 'f1': 0.9660678642714571, 'number': 248}
+- Menu.sub Cnt: {'precision': 0.875, 'recall': 0.8235294117647058, 'f1': 0.8484848484848485, 'number': 17}
+- Menu.sub Nm: {'precision': 0.6666666666666666, 'recall': 0.8125, 'f1': 0.7323943661971831, 'number': 32}
+- Menu.sub Price: {'precision': 1.0, 'recall': 0.7, 'f1': 0.8235294117647058, 'number': 20}
+- Menu.unitprice: {'precision': 0.9253731343283582, 'recall': 0.9117647058823529, 'f1': 0.9185185185185185, 'number': 68}
+- Sub Total.discount Price: {'precision': 0.8571428571428571, 'recall': 0.8571428571428571, 'f1': 0.8571428571428571, 'number': 7}
+- Sub Total.etc: {'precision': 1.0, 'recall': 0.75, 'f1': 0.8571428571428571, 'number': 8}
 - Sub Total.service Price: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12}
-- Sub Total.subtotal Price: {'precision': 0.8947368421052632, 'recall': 0.9855072463768116, 'f1': 0.9379310344827586, 'number': 69}
-- Sub Total.tax Price: {'precision': 0.9545454545454546, 'recall': 0.9333333333333333, 'f1': 0.9438202247191012, 'number': 45}
+- Sub Total.subtotal Price: {'precision': 0.8205128205128205, 'recall': 0.927536231884058, 'f1': 0.870748299319728, 'number': 69}
+- Sub Total.tax Price: {'precision': 1.0, 'recall': 0.9555555555555556, 'f1': 0.9772727272727273, 'number': 45}
 - Total.cashprice: {'precision': 0.9393939393939394, 'recall': 0.8732394366197183, 'f1': 0.9051094890510948, 'number': 71}
-- Total.changeprice: {'precision': 0.9672131147540983, 'recall': 0.9833333333333333, 'f1': 0.9752066115702478, 'number': 60}
-- Total.creditcardprice: {'precision': 0.9333333333333333, 'recall': 0.875, 'f1': 0.9032258064516129, 'number': 16}
-- Total.emoneyprice: {'precision': 0.25, 'recall': 0.5, 'f1': 0.3333333333333333, 'number': 2}
-- Total.menuqty Cnt: {'precision': 0.9032258064516129, 'recall': 0.9333333333333333, 'f1': 0.9180327868852459, 'number': 30}
-- Total.menutype Cnt: {'precision': 0.75, 'recall': 0.75, 'f1': 0.75, 'number': 8}
+- Total.changeprice: {'precision': 0.9661016949152542, 'recall': 0.95, 'f1': 0.957983193277311, 'number': 60}
+- Total.creditcardprice: {'precision': 0.8235294117647058, 'recall': 0.875, 'f1': 0.8484848484848485, 'number': 16}
+- Total.emoneyprice: {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2}
+- Total.menuqty Cnt: {'precision': 0.71875, 'recall': 0.7666666666666667, 'f1': 0.7419354838709677, 'number': 30}
+- Total.menutype Cnt: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}
 - Total.total Etc: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
-- Total.total Price: {'precision': 0.9191919191919192, 'recall': 0.9191919191919192, 'f1': 0.9191919191919192, 'number': 99}
-- Overall Precision: 0.9212
-- Overall Recall: 0.9429
-- Overall F1: 0.9319
-- Overall Accuracy: 0.9542
+- Total.total Price: {'precision': 0.9019607843137255, 'recall': 0.9292929292929293, 'f1': 0.9154228855721392, 'number': 99}
+- Overall Precision: 0.9125
+- Overall Recall: 0.9201
+- Overall F1: 0.9163
+- Overall Accuracy: 0.9355
 
 ## Model description
 
@@ -67,15 +67,14 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- training_steps: 500
+- training_steps: 300
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Menu.cnt                                                                                                 | Menu.discountprice                                                          | Menu.nm                                                                                                  | Menu.num                                                                                   | Menu.price                                                                                               | Menu.sub Cnt                                                                                            | Menu.sub Nm                                                                                 | Menu.sub Price                                                                            | Menu.unitprice                                                                                          | Sub Total.discount Price                                                                | Sub Total.etc                                                   | Sub Total.service Price                                    | Sub Total.subtotal Price                                                                                | Sub Total.tax Price                                                                                     | Total.cashprice                                                                                         | Total.changeprice                                                                                       | Total.creditcardprice                                                                      | Total.emoneyprice                                                         | Total.menuqty Cnt                                                                                       | Total.menutype Cnt                                           | Total.total Etc                                           | Total.total Price                                                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------:|:---------------------------------------------------------------:|:----------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.6695        | 2.0   | 200  | 0.2561          | {'precision': 0.9313304721030042, 'recall': 0.9644444444444444, 'f1': 0.947598253275109, 'number': 225}  | {'precision': 0.625, 'recall': 0.5, 'f1': 0.5555555555555556, 'number': 10} | {'precision': 0.8830188679245283, 'recall': 0.9285714285714286, 'f1': 0.9052224371373309, 'number': 252} | {'precision': 0.875, 'recall': 0.6363636363636364, 'f1': 0.7368421052631579, 'number': 11} | {'precision': 0.9644268774703557, 'recall': 0.9838709677419355, 'f1': 0.9740518962075848, 'number': 248} | {'precision': 0.9285714285714286, 'recall': 0.7647058823529411, 'f1': 0.8387096774193549, 'number': 17} | {'precision': 0.7333333333333333, 'recall': 0.6875, 'f1': 0.7096774193548386, 'number': 32} | {'precision': 1.0, 'recall': 0.85, 'f1': 0.9189189189189189, 'number': 20}                | {'precision': 0.8955223880597015, 'recall': 0.8823529411764706, 'f1': 0.888888888888889, 'number': 68}  | {'precision': 0.875, 'recall': 1.0, 'f1': 0.9333333333333333, 'number': 7}              | {'precision': 0.625, 'recall': 0.625, 'f1': 0.625, 'number': 8} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.8552631578947368, 'recall': 0.9420289855072463, 'f1': 0.8965517241379309, 'number': 69} | {'precision': 1.0, 'recall': 0.9555555555555556, 'f1': 0.9772727272727273, 'number': 45}                | {'precision': 0.9375, 'recall': 0.8450704225352113, 'f1': 0.8888888888888888, 'number': 71}             | {'precision': 0.9152542372881356, 'recall': 0.9, 'f1': 0.9075630252100839, 'number': 60}                | {'precision': 0.875, 'recall': 0.875, 'f1': 0.875, 'number': 16}                           | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 2}                 | {'precision': 0.7419354838709677, 'recall': 0.7666666666666667, 'f1': 0.7540983606557377, 'number': 30} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}    | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.9081632653061225, 'recall': 0.898989898989899, 'f1': 0.9035532994923858, 'number': 99}  | 0.9086            | 0.9079         | 0.9083     | 0.9343           |
-| 0.129         | 4.0   | 400  | 0.1915          | {'precision': 0.9775784753363229, 'recall': 0.9688888888888889, 'f1': 0.9732142857142856, 'number': 225} | {'precision': 0.3888888888888889, 'recall': 0.7, 'f1': 0.5, 'number': 10}   | {'precision': 0.9108527131782945, 'recall': 0.9325396825396826, 'f1': 0.9215686274509803, 'number': 252} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11}   | {'precision': 0.9723320158102767, 'recall': 0.9919354838709677, 'f1': 0.9820359281437127, 'number': 248} | {'precision': 0.8421052631578947, 'recall': 0.9411764705882353, 'f1': 0.8888888888888888, 'number': 17} | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 32}  | {'precision': 0.8636363636363636, 'recall': 0.95, 'f1': 0.9047619047619048, 'number': 20} | {'precision': 0.9848484848484849, 'recall': 0.9558823529411765, 'f1': 0.9701492537313432, 'number': 68} | {'precision': 0.7777777777777778, 'recall': 1.0, 'f1': 0.8750000000000001, 'number': 7} | {'precision': 0.75, 'recall': 0.75, 'f1': 0.75, 'number': 8}    | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.8947368421052632, 'recall': 0.9855072463768116, 'f1': 0.9379310344827586, 'number': 69} | {'precision': 0.9545454545454546, 'recall': 0.9333333333333333, 'f1': 0.9438202247191012, 'number': 45} | {'precision': 0.9393939393939394, 'recall': 0.8732394366197183, 'f1': 0.9051094890510948, 'number': 71} | {'precision': 0.9672131147540983, 'recall': 0.9833333333333333, 'f1': 0.9752066115702478, 'number': 60} | {'precision': 0.9333333333333333, 'recall': 0.875, 'f1': 0.9032258064516129, 'number': 16} | {'precision': 0.25, 'recall': 0.5, 'f1': 0.3333333333333333, 'number': 2} | {'precision': 0.9032258064516129, 'recall': 0.9333333333333333, 'f1': 0.9180327868852459, 'number': 30} | {'precision': 0.75, 'recall': 0.75, 'f1': 0.75, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.9191919191919192, 'recall': 0.9191919191919192, 'f1': 0.9191919191919192, 'number': 99} | 0.9212            | 0.9429         | 0.9319     | 0.9542           |
+| Training Loss | Epoch | Step | Validation Loss | Menu.cnt                                                                                                 | Menu.discountprice                                         | Menu.nm                                                                                                 | Menu.num                                                                  | Menu.price                                                                                               | Menu.sub Cnt                                                                               | Menu.sub Nm                                                                                 | Menu.sub Price                                                            | Menu.unitprice                                                                                          | Sub Total.discount Price                                                                               | Sub Total.etc                                                             | Sub Total.service Price                                    | Sub Total.subtotal Price                                                                              | Sub Total.tax Price                                                                      | Total.cashprice                                                                                         | Total.changeprice                                                                        | Total.creditcardprice                                                                      | Total.emoneyprice                                         | Total.menuqty Cnt                                                                            | Total.menutype Cnt                                        | Total.total Etc                                           | Total.total Price                                                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:----------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.6711        | 2.0   | 200  | 0.2317          | {'precision': 0.9521739130434783, 'recall': 0.9733333333333334, 'f1': 0.9626373626373628, 'number': 225} | {'precision': 0.6, 'recall': 0.6, 'f1': 0.6, 'number': 10} | {'precision': 0.9011406844106464, 'recall': 0.9404761904761905, 'f1': 0.920388349514563, 'number': 252} | {'precision': 1.0, 'recall': 0.8181818181818182, 'f1': 0.9, 'number': 11} | {'precision': 0.9565217391304348, 'recall': 0.9758064516129032, 'f1': 0.9660678642714571, 'number': 248} | {'precision': 0.875, 'recall': 0.8235294117647058, 'f1': 0.8484848484848485, 'number': 17} | {'precision': 0.6666666666666666, 'recall': 0.8125, 'f1': 0.7323943661971831, 'number': 32} | {'precision': 1.0, 'recall': 0.7, 'f1': 0.8235294117647058, 'number': 20} | {'precision': 0.9253731343283582, 'recall': 0.9117647058823529, 'f1': 0.9185185185185185, 'number': 68} | {'precision': 0.8571428571428571, 'recall': 0.8571428571428571, 'f1': 0.8571428571428571, 'number': 7} | {'precision': 1.0, 'recall': 0.75, 'f1': 0.8571428571428571, 'number': 8} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 12} | {'precision': 0.8205128205128205, 'recall': 0.927536231884058, 'f1': 0.870748299319728, 'number': 69} | {'precision': 1.0, 'recall': 0.9555555555555556, 'f1': 0.9772727272727273, 'number': 45} | {'precision': 0.9393939393939394, 'recall': 0.8732394366197183, 'f1': 0.9051094890510948, 'number': 71} | {'precision': 0.9661016949152542, 'recall': 0.95, 'f1': 0.957983193277311, 'number': 60} | {'precision': 0.8235294117647058, 'recall': 0.875, 'f1': 0.8484848484848485, 'number': 16} | {'precision': 0.5, 'recall': 0.5, 'f1': 0.5, 'number': 2} | {'precision': 0.71875, 'recall': 0.7666666666666667, 'f1': 0.7419354838709677, 'number': 30} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.9019607843137255, 'recall': 0.9292929292929293, 'f1': 0.9154228855721392, 'number': 99} | 0.9125            | 0.9201         | 0.9163     | 0.9355           |
 
 
 ### Framework versions

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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

training_args.bin

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