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Benedict-L
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layoutlm-funsd

Token Classification
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layoutlm
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End of training

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  1. README.md +25 -25
  2. logs/events.out.tfevents.1717946378.Designori.16644.0 +2 -2
  3. model.safetensors +1 -1
  4. tokenizer.json +16 -2
README.md CHANGED
@@ -17,14 +17,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6930
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- - Answer: {'precision': 0.705114254624592, 'recall': 0.8009888751545118, 'f1': 0.7499999999999999, 'number': 809}
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- - Header: {'precision': 0.2642857142857143, 'recall': 0.31092436974789917, 'f1': 0.28571428571428575, 'number': 119}
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- - Question: {'precision': 0.7760141093474426, 'recall': 0.8262910798122066, 'f1': 0.8003638017280582, 'number': 1065}
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- - Overall Precision: 0.7136
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- - Overall Recall: 0.7852
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- - Overall F1: 0.7477
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- - Overall Accuracy: 0.8082
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  ## Model description
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@@ -54,23 +54,23 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 1.812 | 1.0 | 10 | 1.5657 | {'precision': 0.026246719160104987, 'recall': 0.024721878862793572, 'f1': 0.02546148949713558, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1956521739130435, 'recall': 0.1267605633802817, 'f1': 0.15384615384615385, 'number': 1065} | 0.1067 | 0.0778 | 0.0900 | 0.3859 |
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- | 1.4244 | 2.0 | 20 | 1.2288 | {'precision': 0.14189189189189189, 'recall': 0.103831891223733, 'f1': 0.11991434689507495, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.42844202898550726, 'recall': 0.444131455399061, 'f1': 0.43614568925772246, 'number': 1065} | 0.3284 | 0.2795 | 0.3020 | 0.5784 |
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- | 1.1038 | 3.0 | 30 | 0.9813 | {'precision': 0.4468629961587708, 'recall': 0.43139678615574784, 'f1': 0.4389937106918239, 'number': 809} | {'precision': 0.03225806451612903, 'recall': 0.008403361344537815, 'f1': 0.013333333333333332, 'number': 119} | {'precision': 0.6163522012578616, 'recall': 0.644131455399061, 'f1': 0.6299357208448118, 'number': 1065} | 0.5382 | 0.5198 | 0.5288 | 0.7101 |
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- | 0.8512 | 4.0 | 40 | 0.8085 | {'precision': 0.5877192982456141, 'recall': 0.6625463535228677, 'f1': 0.6228936664729808, 'number': 809} | {'precision': 0.109375, 'recall': 0.058823529411764705, 'f1': 0.07650273224043715, 'number': 119} | {'precision': 0.6793760831889082, 'recall': 0.7361502347417841, 'f1': 0.7066246056782335, 'number': 1065} | 0.6230 | 0.6658 | 0.6437 | 0.7566 |
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- | 0.6646 | 5.0 | 50 | 0.7071 | {'precision': 0.6478723404255319, 'recall': 0.7527812113720643, 'f1': 0.6963979416809606, 'number': 809} | {'precision': 0.21052631578947367, 'recall': 0.16806722689075632, 'f1': 0.1869158878504673, 'number': 119} | {'precision': 0.6853658536585366, 'recall': 0.7915492957746478, 'f1': 0.7346405228758169, 'number': 1065} | 0.6499 | 0.7386 | 0.6914 | 0.7871 |
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- | 0.5615 | 6.0 | 60 | 0.6934 | {'precision': 0.6427840327533265, 'recall': 0.7762669962917181, 'f1': 0.7032474804031356, 'number': 809} | {'precision': 0.2191780821917808, 'recall': 0.13445378151260504, 'f1': 0.16666666666666669, 'number': 119} | {'precision': 0.7584973166368515, 'recall': 0.7962441314553991, 'f1': 0.7769125057260651, 'number': 1065} | 0.6882 | 0.7486 | 0.7171 | 0.8008 |
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- | 0.4852 | 7.0 | 70 | 0.6675 | {'precision': 0.6806451612903226, 'recall': 0.7824474660074165, 'f1': 0.7280046003450259, 'number': 809} | {'precision': 0.2421875, 'recall': 0.2605042016806723, 'f1': 0.2510121457489879, 'number': 119} | {'precision': 0.7596759675967597, 'recall': 0.7924882629107981, 'f1': 0.775735294117647, 'number': 1065} | 0.6953 | 0.7566 | 0.7247 | 0.8098 |
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- | 0.4261 | 8.0 | 80 | 0.6601 | {'precision': 0.6707818930041153, 'recall': 0.8059332509270705, 'f1': 0.7321729365524987, 'number': 809} | {'precision': 0.23770491803278687, 'recall': 0.24369747899159663, 'f1': 0.24066390041493776, 'number': 119} | {'precision': 0.7515257192676548, 'recall': 0.8093896713615023, 'f1': 0.779385171790235, 'number': 1065} | 0.6885 | 0.7742 | 0.7289 | 0.8027 |
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- | 0.3798 | 9.0 | 90 | 0.6595 | {'precision': 0.6950431034482759, 'recall': 0.7972805933250927, 'f1': 0.7426597582037997, 'number': 809} | {'precision': 0.2727272727272727, 'recall': 0.2773109243697479, 'f1': 0.27499999999999997, 'number': 119} | {'precision': 0.7698343504795118, 'recall': 0.8291079812206573, 'f1': 0.7983725135623869, 'number': 1065} | 0.7108 | 0.7832 | 0.7453 | 0.8120 |
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- | 0.366 | 10.0 | 100 | 0.6659 | {'precision': 0.6912393162393162, 'recall': 0.799752781211372, 'f1': 0.7415472779369628, 'number': 809} | {'precision': 0.29310344827586204, 'recall': 0.2857142857142857, 'f1': 0.2893617021276596, 'number': 119} | {'precision': 0.7822222222222223, 'recall': 0.8262910798122066, 'f1': 0.8036529680365297, 'number': 1065} | 0.7170 | 0.7832 | 0.7487 | 0.8196 |
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- | 0.3112 | 11.0 | 110 | 0.6790 | {'precision': 0.674562306900103, 'recall': 0.8096415327564895, 'f1': 0.7359550561797752, 'number': 809} | {'precision': 0.2890625, 'recall': 0.31092436974789917, 'f1': 0.29959514170040485, 'number': 119} | {'precision': 0.7867383512544803, 'recall': 0.8244131455399061, 'f1': 0.8051352590554791, 'number': 1065} | 0.7088 | 0.7878 | 0.7462 | 0.8022 |
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- | 0.3003 | 12.0 | 120 | 0.6876 | {'precision': 0.7192393736017897, 'recall': 0.7948084054388134, 'f1': 0.7551379917792131, 'number': 809} | {'precision': 0.2824427480916031, 'recall': 0.31092436974789917, 'f1': 0.29600000000000004, 'number': 119} | {'precision': 0.7788546255506608, 'recall': 0.8300469483568075, 'f1': 0.8036363636363637, 'number': 1065} | 0.7241 | 0.7847 | 0.7532 | 0.8069 |
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- | 0.28 | 13.0 | 130 | 0.6905 | {'precision': 0.7013963480128894, 'recall': 0.8071693448702101, 'f1': 0.7505747126436783, 'number': 809} | {'precision': 0.2923076923076923, 'recall': 0.31932773109243695, 'f1': 0.3052208835341365, 'number': 119} | {'precision': 0.7860340196956133, 'recall': 0.8244131455399061, 'f1': 0.8047662694775436, 'number': 1065} | 0.7204 | 0.7873 | 0.7523 | 0.8104 |
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- | 0.2654 | 14.0 | 140 | 0.6952 | {'precision': 0.7069154774972558, 'recall': 0.796044499381953, 'f1': 0.7488372093023256, 'number': 809} | {'precision': 0.2569444444444444, 'recall': 0.31092436974789917, 'f1': 0.28136882129277563, 'number': 119} | {'precision': 0.7758164165931156, 'recall': 0.8253521126760563, 'f1': 0.7998180163785259, 'number': 1065} | 0.7130 | 0.7827 | 0.7462 | 0.8068 |
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- | 0.2629 | 15.0 | 150 | 0.6930 | {'precision': 0.705114254624592, 'recall': 0.8009888751545118, 'f1': 0.7499999999999999, 'number': 809} | {'precision': 0.2642857142857143, 'recall': 0.31092436974789917, 'f1': 0.28571428571428575, 'number': 119} | {'precision': 0.7760141093474426, 'recall': 0.8262910798122066, 'f1': 0.8003638017280582, 'number': 1065} | 0.7136 | 0.7852 | 0.7477 | 0.8082 |
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  ### Framework versions
 
17
 
18
  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
19
  It achieves the following results on the evaluation set:
20
+ - Loss: 0.8707
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+ - Answer: {'precision': 0.731359649122807, 'recall': 0.8244746600741656, 'f1': 0.7751307379430563, 'number': 809}
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+ - Header: {'precision': 0.47101449275362317, 'recall': 0.5462184873949579, 'f1': 0.5058365758754864, 'number': 119}
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+ - Question: {'precision': 0.8043087971274686, 'recall': 0.8413145539906103, 'f1': 0.8223955943093162, 'number': 1065}
24
+ - Overall Precision: 0.7523
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+ - Overall Recall: 0.8169
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+ - Overall F1: 0.7833
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+ - Overall Accuracy: 0.8120
28
 
29
  ## Model description
30
 
 
54
 
55
  ### Training results
56
 
57
+ | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 1.6648 | 1.0 | 19 | 1.2802 | {'precision': 0.24287028518859247, 'recall': 0.3263288009888752, 'f1': 0.2784810126582279, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3659942363112392, 'recall': 0.596244131455399, 'f1': 0.45357142857142857, 'number': 1065} | 0.3186 | 0.4511 | 0.3734 | 0.5834 |
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+ | 1.0035 | 2.0 | 38 | 0.7824 | {'precision': 0.5849056603773585, 'recall': 0.7280593325092707, 'f1': 0.6486784140969163, 'number': 809} | {'precision': 0.031746031746031744, 'recall': 0.01680672268907563, 'f1': 0.02197802197802198, 'number': 119} | {'precision': 0.6030042918454935, 'recall': 0.7915492957746478, 'f1': 0.684531059683313, 'number': 1065} | 0.5810 | 0.7195 | 0.6429 | 0.7645 |
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+ | 0.6425 | 3.0 | 57 | 0.6680 | {'precision': 0.6607515657620042, 'recall': 0.7824474660074165, 'f1': 0.7164685908319186, 'number': 809} | {'precision': 0.12037037037037036, 'recall': 0.1092436974789916, 'f1': 0.1145374449339207, 'number': 119} | {'precision': 0.7028753993610224, 'recall': 0.8262910798122066, 'f1': 0.7596029348295209, 'number': 1065} | 0.6583 | 0.7657 | 0.7080 | 0.7896 |
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+ | 0.4629 | 4.0 | 76 | 0.6420 | {'precision': 0.6653102746693794, 'recall': 0.8084054388133498, 'f1': 0.7299107142857143, 'number': 809} | {'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119} | {'precision': 0.7648578811369509, 'recall': 0.8338028169014085, 'f1': 0.7978436657681941, 'number': 1065} | 0.6986 | 0.7898 | 0.7414 | 0.8070 |
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+ | 0.3456 | 5.0 | 95 | 0.6765 | {'precision': 0.6901709401709402, 'recall': 0.7985166872682324, 'f1': 0.7404011461318052, 'number': 809} | {'precision': 0.3007518796992481, 'recall': 0.33613445378151263, 'f1': 0.31746031746031744, 'number': 119} | {'precision': 0.7731685789938217, 'recall': 0.8225352112676056, 'f1': 0.7970882620564149, 'number': 1065} | 0.7094 | 0.7837 | 0.7447 | 0.8024 |
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+ | 0.2667 | 6.0 | 114 | 0.6940 | {'precision': 0.6908315565031983, 'recall': 0.8009888751545118, 'f1': 0.7418431597023468, 'number': 809} | {'precision': 0.35294117647058826, 'recall': 0.35294117647058826, 'f1': 0.35294117647058826, 'number': 119} | {'precision': 0.7787610619469026, 'recall': 0.8262910798122066, 'f1': 0.8018223234624146, 'number': 1065} | 0.7179 | 0.7878 | 0.7512 | 0.8054 |
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+ | 0.2131 | 7.0 | 133 | 0.7425 | {'precision': 0.696652719665272, 'recall': 0.823238566131026, 'f1': 0.7546742209631728, 'number': 809} | {'precision': 0.40310077519379844, 'recall': 0.4369747899159664, 'f1': 0.4193548387096774, 'number': 119} | {'precision': 0.8073394495412844, 'recall': 0.8262910798122066, 'f1': 0.8167053364269143, 'number': 1065} | 0.7347 | 0.8018 | 0.7668 | 0.8062 |
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+ | 0.1712 | 8.0 | 152 | 0.7707 | {'precision': 0.7065101387406617, 'recall': 0.8182941903584673, 'f1': 0.7583046964490264, 'number': 809} | {'precision': 0.39215686274509803, 'recall': 0.5042016806722689, 'f1': 0.4411764705882353, 'number': 119} | {'precision': 0.8030713640469738, 'recall': 0.8347417840375587, 'f1': 0.8186003683241252, 'number': 1065} | 0.7333 | 0.8083 | 0.7690 | 0.8080 |
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+ | 0.1426 | 9.0 | 171 | 0.7811 | {'precision': 0.717391304347826, 'recall': 0.8158220024721878, 'f1': 0.7634470792365529, 'number': 809} | {'precision': 0.4274193548387097, 'recall': 0.44537815126050423, 'f1': 0.43621399176954734, 'number': 119} | {'precision': 0.7968056787932565, 'recall': 0.8431924882629108, 'f1': 0.8193430656934306, 'number': 1065} | 0.7421 | 0.8083 | 0.7738 | 0.8127 |
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+ | 0.1229 | 10.0 | 190 | 0.8075 | {'precision': 0.7242524916943521, 'recall': 0.8084054388133498, 'f1': 0.764018691588785, 'number': 809} | {'precision': 0.4722222222222222, 'recall': 0.5714285714285714, 'f1': 0.5171102661596959, 'number': 119} | {'precision': 0.8030438675022381, 'recall': 0.8422535211267606, 'f1': 0.8221814848762603, 'number': 1065} | 0.7482 | 0.8123 | 0.7789 | 0.8139 |
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+ | 0.1023 | 11.0 | 209 | 0.8426 | {'precision': 0.7358490566037735, 'recall': 0.8195302843016069, 'f1': 0.775438596491228, 'number': 809} | {'precision': 0.45774647887323944, 'recall': 0.5462184873949579, 'f1': 0.49808429118773945, 'number': 119} | {'precision': 0.7978339350180506, 'recall': 0.8300469483568075, 'f1': 0.8136217211228716, 'number': 1065} | 0.7494 | 0.8088 | 0.7780 | 0.8106 |
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+ | 0.0935 | 12.0 | 228 | 0.8502 | {'precision': 0.7300546448087432, 'recall': 0.8257107540173053, 'f1': 0.7749419953596288, 'number': 809} | {'precision': 0.4520547945205479, 'recall': 0.5546218487394958, 'f1': 0.4981132075471698, 'number': 119} | {'precision': 0.7978339350180506, 'recall': 0.8300469483568075, 'f1': 0.8136217211228716, 'number': 1065} | 0.7460 | 0.8118 | 0.7775 | 0.8110 |
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+ | 0.0829 | 13.0 | 247 | 0.8513 | {'precision': 0.7335562987736901, 'recall': 0.8133498145859085, 'f1': 0.7713950762016414, 'number': 809} | {'precision': 0.4589041095890411, 'recall': 0.5630252100840336, 'f1': 0.5056603773584906, 'number': 119} | {'precision': 0.8018018018018018, 'recall': 0.8356807511737089, 'f1': 0.8183908045977012, 'number': 1065} | 0.7501 | 0.8103 | 0.7791 | 0.8124 |
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+ | 0.0801 | 14.0 | 266 | 0.8655 | {'precision': 0.729490022172949, 'recall': 0.8133498145859085, 'f1': 0.7691408533021624, 'number': 809} | {'precision': 0.4507042253521127, 'recall': 0.5378151260504201, 'f1': 0.4904214559386973, 'number': 119} | {'precision': 0.8057553956834532, 'recall': 0.8413145539906103, 'f1': 0.8231511254019293, 'number': 1065} | 0.7505 | 0.8118 | 0.7799 | 0.8110 |
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+ | 0.0751 | 15.0 | 285 | 0.8707 | {'precision': 0.731359649122807, 'recall': 0.8244746600741656, 'f1': 0.7751307379430563, 'number': 809} | {'precision': 0.47101449275362317, 'recall': 0.5462184873949579, 'f1': 0.5058365758754864, 'number': 119} | {'precision': 0.8043087971274686, 'recall': 0.8413145539906103, 'f1': 0.8223955943093162, 'number': 1065} | 0.7523 | 0.8169 | 0.7833 | 0.8120 |
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  ### Framework versions
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