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 [pabloma09/layoutlm-funsd](https://huggingface.co/pabloma09/layoutlm-funsd) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.9937
-- Eader: {'precision': 0.35, 'recall': 0.21875, 'f1': 0.2692307692307692, 'number': 32}
-- Nswer: {'precision': 0.5135135135135135, 'recall': 0.5428571428571428, 'f1': 0.5277777777777778, 'number': 70}
-- Uestion: {'precision': 0.4931506849315068, 'recall': 0.46153846153846156, 'f1': 0.4768211920529801, 'number': 78}
-- Overall Precision: 0.4850
-- Overall Recall: 0.45
-- Overall F1: 0.4669
-- Overall Accuracy: 0.8029
+- Loss: 0.5379
+- Eader: {'precision': 0.7209302325581395, 'recall': 0.543859649122807, 'f1': 0.6200000000000001, 'number': 57}
+- Nswer: {'precision': 0.7183098591549296, 'recall': 0.723404255319149, 'f1': 0.7208480565371025, 'number': 141}
+- Uestion: {'precision': 0.7290322580645161, 'recall': 0.7018633540372671, 'f1': 0.7151898734177216, 'number': 161}
+- Overall Precision: 0.7235
+- Overall Recall: 0.6852
+- Overall F1: 0.7039
+- Overall Accuracy: 0.9016
 
 ## Model description
 
@@ -53,23 +53,23 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                         | Nswer                                                                                                     | Uestion                                                                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| 0.1124        | 1.0   | 13   | 0.8542          | {'precision': 0.32, 'recall': 0.25, 'f1': 0.2807017543859649, 'number': 32}                   | {'precision': 0.4647887323943662, 'recall': 0.4714285714285714, 'f1': 0.46808510638297873, 'number': 70}  | {'precision': 0.4861111111111111, 'recall': 0.44871794871794873, 'f1': 0.4666666666666667, 'number': 78}   | 0.4524            | 0.4222         | 0.4368     | 0.7848           |
-| 0.1002        | 2.0   | 26   | 0.8579          | {'precision': 0.3181818181818182, 'recall': 0.21875, 'f1': 0.25925925925925924, 'number': 32} | {'precision': 0.4146341463414634, 'recall': 0.4857142857142857, 'f1': 0.4473684210526316, 'number': 70}   | {'precision': 0.4125, 'recall': 0.4230769230769231, 'f1': 0.4177215189873418, 'number': 78}                | 0.4022            | 0.4111         | 0.4066     | 0.7559           |
-| 0.0905        | 3.0   | 39   | 0.7874          | {'precision': 0.34782608695652173, 'recall': 0.25, 'f1': 0.2909090909090909, 'number': 32}    | {'precision': 0.45, 'recall': 0.5142857142857142, 'f1': 0.48, 'number': 70}                               | {'precision': 0.5, 'recall': 0.48717948717948717, 'f1': 0.49350649350649345, 'number': 78}                 | 0.4581            | 0.4556         | 0.4568     | 0.7987           |
-| 0.0743        | 4.0   | 52   | 0.9167          | {'precision': 0.4, 'recall': 0.1875, 'f1': 0.25531914893617025, 'number': 32}                 | {'precision': 0.48, 'recall': 0.5142857142857142, 'f1': 0.496551724137931, 'number': 70}                  | {'precision': 0.5333333333333333, 'recall': 0.5128205128205128, 'f1': 0.5228758169934641, 'number': 78}    | 0.4970            | 0.4556         | 0.4754     | 0.7926           |
-| 0.0534        | 5.0   | 65   | 0.9266          | {'precision': 0.45, 'recall': 0.28125, 'f1': 0.34615384615384615, 'number': 32}               | {'precision': 0.43373493975903615, 'recall': 0.5142857142857142, 'f1': 0.47058823529411764, 'number': 70} | {'precision': 0.4805194805194805, 'recall': 0.47435897435897434, 'f1': 0.47741935483870973, 'number': 78}  | 0.4556            | 0.4556         | 0.4556     | 0.7800           |
-| 0.0465        | 6.0   | 78   | 1.0600          | {'precision': 0.3, 'recall': 0.1875, 'f1': 0.23076923076923075, 'number': 32}                 | {'precision': 0.4864864864864865, 'recall': 0.5142857142857142, 'f1': 0.5, 'number': 70}                  | {'precision': 0.4666666666666667, 'recall': 0.44871794871794873, 'f1': 0.45751633986928103, 'number': 78}  | 0.4556            | 0.4278         | 0.4413     | 0.7197           |
-| 0.0388        | 7.0   | 91   | 0.9172          | {'precision': 0.4444444444444444, 'recall': 0.25, 'f1': 0.32, 'number': 32}                   | {'precision': 0.5, 'recall': 0.5571428571428572, 'f1': 0.5270270270270271, 'number': 70}                  | {'precision': 0.4625, 'recall': 0.47435897435897434, 'f1': 0.46835443037974683, 'number': 78}              | 0.4773            | 0.4667         | 0.4719     | 0.7740           |
-| 0.0333        | 8.0   | 104  | 0.9758          | {'precision': 0.3684210526315789, 'recall': 0.21875, 'f1': 0.2745098039215686, 'number': 32}  | {'precision': 0.4157303370786517, 'recall': 0.5285714285714286, 'f1': 0.46540880503144655, 'number': 70}  | {'precision': 0.4186046511627907, 'recall': 0.46153846153846156, 'f1': 0.4390243902439025, 'number': 78}   | 0.4124            | 0.4444         | 0.4278     | 0.7770           |
-| 0.0269        | 9.0   | 117  | 0.9879          | {'precision': 0.2916666666666667, 'recall': 0.21875, 'f1': 0.25, 'number': 32}                | {'precision': 0.4, 'recall': 0.5142857142857142, 'f1': 0.45, 'number': 70}                                | {'precision': 0.3888888888888889, 'recall': 0.44871794871794873, 'f1': 0.41666666666666663, 'number': 78}  | 0.3824            | 0.4333         | 0.4062     | 0.7794           |
-| 0.0255        | 10.0  | 130  | 0.9909          | {'precision': 0.3, 'recall': 0.1875, 'f1': 0.23076923076923075, 'number': 32}                 | {'precision': 0.43902439024390244, 'recall': 0.5142857142857142, 'f1': 0.4736842105263158, 'number': 70}  | {'precision': 0.4358974358974359, 'recall': 0.4358974358974359, 'f1': 0.4358974358974359, 'number': 78}    | 0.4222            | 0.4222         | 0.4222     | 0.7914           |
-| 0.0217        | 11.0  | 143  | 0.9914          | {'precision': 0.35, 'recall': 0.21875, 'f1': 0.2692307692307692, 'number': 32}                | {'precision': 0.5066666666666667, 'recall': 0.5428571428571428, 'f1': 0.5241379310344827, 'number': 70}   | {'precision': 0.45121951219512196, 'recall': 0.47435897435897434, 'f1': 0.46249999999999997, 'number': 78} | 0.4633            | 0.4556         | 0.4594     | 0.7951           |
-| 0.024         | 12.0  | 156  | 0.9999          | {'precision': 0.3684210526315789, 'recall': 0.21875, 'f1': 0.2745098039215686, 'number': 32}  | {'precision': 0.5064935064935064, 'recall': 0.5571428571428572, 'f1': 0.5306122448979592, 'number': 70}   | {'precision': 0.475, 'recall': 0.48717948717948717, 'f1': 0.4810126582278481, 'number': 78}                | 0.4773            | 0.4667         | 0.4719     | 0.7896           |
-| 0.0197        | 13.0  | 169  | 0.9820          | {'precision': 0.3333333333333333, 'recall': 0.21875, 'f1': 0.2641509433962264, 'number': 32}  | {'precision': 0.49333333333333335, 'recall': 0.5285714285714286, 'f1': 0.5103448275862069, 'number': 70}  | {'precision': 0.5205479452054794, 'recall': 0.48717948717948717, 'f1': 0.5033112582781456, 'number': 78}   | 0.4852            | 0.4556         | 0.4699     | 0.8053           |
-| 0.022         | 14.0  | 182  | 0.9912          | {'precision': 0.35, 'recall': 0.21875, 'f1': 0.2692307692307692, 'number': 32}                | {'precision': 0.5, 'recall': 0.5428571428571428, 'f1': 0.5205479452054795, 'number': 70}                  | {'precision': 0.4864864864864865, 'recall': 0.46153846153846156, 'f1': 0.47368421052631576, 'number': 78}  | 0.4765            | 0.45           | 0.4629     | 0.8023           |
-| 0.0215        | 15.0  | 195  | 0.9937          | {'precision': 0.35, 'recall': 0.21875, 'f1': 0.2692307692307692, 'number': 32}                | {'precision': 0.5135135135135135, 'recall': 0.5428571428571428, 'f1': 0.5277777777777778, 'number': 70}   | {'precision': 0.4931506849315068, 'recall': 0.46153846153846156, 'f1': 0.4768211920529801, 'number': 78}   | 0.4850            | 0.45           | 0.4669     | 0.8029           |
+| Training Loss | Epoch | Step | Validation Loss | Eader                                                                                                   | Nswer                                                                                                    | Uestion                                                                                                  | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0751        | 1.0   | 12   | 0.4989          | {'precision': 0.5740740740740741, 'recall': 0.543859649122807, 'f1': 0.5585585585585585, 'number': 57}  | {'precision': 0.673202614379085, 'recall': 0.7304964539007093, 'f1': 0.7006802721088436, 'number': 141}  | {'precision': 0.6666666666666666, 'recall': 0.6708074534161491, 'f1': 0.6687306501547988, 'number': 161} | 0.6558            | 0.6741         | 0.6648     | 0.8675           |
+| 0.0681        | 2.0   | 24   | 0.4233          | {'precision': 0.6739130434782609, 'recall': 0.543859649122807, 'f1': 0.6019417475728156, 'number': 57}  | {'precision': 0.7394366197183099, 'recall': 0.7446808510638298, 'f1': 0.7420494699646644, 'number': 141} | {'precision': 0.7044025157232704, 'recall': 0.6956521739130435, 'f1': 0.7, 'number': 161}                | 0.7147            | 0.6908         | 0.7025     | 0.9004           |
+| 0.0499        | 3.0   | 36   | 0.4571          | {'precision': 0.775, 'recall': 0.543859649122807, 'f1': 0.6391752577319588, 'number': 57}               | {'precision': 0.7083333333333334, 'recall': 0.723404255319149, 'f1': 0.7157894736842105, 'number': 141}  | {'precision': 0.73125, 'recall': 0.7267080745341615, 'f1': 0.7289719626168223, 'number': 161}            | 0.7267            | 0.6964         | 0.7112     | 0.8998           |
+| 0.037         | 4.0   | 48   | 0.4636          | {'precision': 0.7045454545454546, 'recall': 0.543859649122807, 'f1': 0.613861386138614, 'number': 57}   | {'precision': 0.7142857142857143, 'recall': 0.7446808510638298, 'f1': 0.7291666666666666, 'number': 141} | {'precision': 0.7222222222222222, 'recall': 0.7267080745341615, 'f1': 0.7244582043343654, 'number': 161} | 0.7167            | 0.7047         | 0.7107     | 0.9016           |
+| 0.0329        | 5.0   | 60   | 0.5128          | {'precision': 0.6530612244897959, 'recall': 0.5614035087719298, 'f1': 0.6037735849056605, 'number': 57} | {'precision': 0.697986577181208, 'recall': 0.7375886524822695, 'f1': 0.7172413793103447, 'number': 141}  | {'precision': 0.6706586826347305, 'recall': 0.6956521739130435, 'f1': 0.6829268292682926, 'number': 161} | 0.6795            | 0.6908         | 0.6851     | 0.8880           |
+| 0.0263        | 6.0   | 72   | 0.5192          | {'precision': 0.6904761904761905, 'recall': 0.5087719298245614, 'f1': 0.5858585858585859, 'number': 57} | {'precision': 0.7183098591549296, 'recall': 0.723404255319149, 'f1': 0.7208480565371025, 'number': 141}  | {'precision': 0.7484276729559748, 'recall': 0.7391304347826086, 'f1': 0.7437500000000001, 'number': 161} | 0.7289            | 0.6964         | 0.7123     | 0.8995           |
+| 0.023         | 7.0   | 84   | 0.5452          | {'precision': 0.6976744186046512, 'recall': 0.5263157894736842, 'f1': 0.6, 'number': 57}                | {'precision': 0.7202797202797203, 'recall': 0.7304964539007093, 'f1': 0.7253521126760565, 'number': 141} | {'precision': 0.7, 'recall': 0.6956521739130435, 'f1': 0.6978193146417445, 'number': 161}                | 0.7081            | 0.6825         | 0.6950     | 0.8956           |
+| 0.0205        | 8.0   | 96   | 0.5398          | {'precision': 0.6666666666666666, 'recall': 0.5614035087719298, 'f1': 0.6095238095238096, 'number': 57} | {'precision': 0.7083333333333334, 'recall': 0.723404255319149, 'f1': 0.7157894736842105, 'number': 141}  | {'precision': 0.7151898734177216, 'recall': 0.7018633540372671, 'f1': 0.7084639498432601, 'number': 161} | 0.7057            | 0.6880         | 0.6968     | 0.8971           |
+| 0.0182        | 9.0   | 108  | 0.5025          | {'precision': 0.62, 'recall': 0.543859649122807, 'f1': 0.5794392523364487, 'number': 57}                | {'precision': 0.7482014388489209, 'recall': 0.7375886524822695, 'f1': 0.7428571428571428, 'number': 141} | {'precision': 0.7088607594936709, 'recall': 0.6956521739130435, 'f1': 0.7021943573667712, 'number': 161} | 0.7118            | 0.6880         | 0.6997     | 0.9046           |
+| 0.0175        | 10.0  | 120  | 0.5017          | {'precision': 0.6888888888888889, 'recall': 0.543859649122807, 'f1': 0.6078431372549019, 'number': 57}  | {'precision': 0.7183098591549296, 'recall': 0.723404255319149, 'f1': 0.7208480565371025, 'number': 141}  | {'precision': 0.7133757961783439, 'recall': 0.6956521739130435, 'f1': 0.7044025157232704, 'number': 161} | 0.7122            | 0.6825         | 0.6970     | 0.9031           |
+| 0.0157        | 11.0  | 132  | 0.5034          | {'precision': 0.7272727272727273, 'recall': 0.5614035087719298, 'f1': 0.6336633663366337, 'number': 57} | {'precision': 0.7357142857142858, 'recall': 0.7304964539007093, 'f1': 0.7330960854092528, 'number': 141} | {'precision': 0.7243589743589743, 'recall': 0.7018633540372671, 'f1': 0.7129337539432177, 'number': 161} | 0.7294            | 0.6908         | 0.7096     | 0.9037           |
+| 0.0151        | 12.0  | 144  | 0.5181          | {'precision': 0.7209302325581395, 'recall': 0.543859649122807, 'f1': 0.6200000000000001, 'number': 57}  | {'precision': 0.7183098591549296, 'recall': 0.723404255319149, 'f1': 0.7208480565371025, 'number': 141}  | {'precision': 0.7290322580645161, 'recall': 0.7018633540372671, 'f1': 0.7151898734177216, 'number': 161} | 0.7235            | 0.6852         | 0.7039     | 0.9040           |
+| 0.0122        | 13.0  | 156  | 0.5368          | {'precision': 0.7209302325581395, 'recall': 0.543859649122807, 'f1': 0.6200000000000001, 'number': 57}  | {'precision': 0.7394366197183099, 'recall': 0.7446808510638298, 'f1': 0.7420494699646644, 'number': 141} | {'precision': 0.7261146496815286, 'recall': 0.7080745341614907, 'f1': 0.7169811320754716, 'number': 161} | 0.7310            | 0.6964         | 0.7133     | 0.9019           |
+| 0.0114        | 14.0  | 168  | 0.5372          | {'precision': 0.7272727272727273, 'recall': 0.5614035087719298, 'f1': 0.6336633663366337, 'number': 57} | {'precision': 0.7272727272727273, 'recall': 0.7375886524822695, 'f1': 0.7323943661971831, 'number': 141} | {'precision': 0.7197452229299363, 'recall': 0.7018633540372671, 'f1': 0.7106918238993711, 'number': 161} | 0.7238            | 0.6936         | 0.7084     | 0.9022           |
+| 0.0126        | 15.0  | 180  | 0.5379          | {'precision': 0.7209302325581395, 'recall': 0.543859649122807, 'f1': 0.6200000000000001, 'number': 57}  | {'precision': 0.7183098591549296, 'recall': 0.723404255319149, 'f1': 0.7208480565371025, 'number': 141}  | {'precision': 0.7290322580645161, 'recall': 0.7018633540372671, 'f1': 0.7151898734177216, 'number': 161} | 0.7235            | 0.6852         | 0.7039     | 0.9016           |
 
 
 ### Framework versions

logs/events.out.tfevents.1741102575.DESKTOP-HA84SVN.3577307.2

@@ -1,3 +1,3 @@
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-size 15332
+oid sha256:ac2d4e3ecc8dc8d39463a9b229e601d3398a26ee30b4119eaec5a2edcd423c00
+size 16190

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:59a36a86148643326fc04e9ec26d92c5596012b296eaaa1dd76395a400055a40
 size 450548984