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layoutlm-funsd

Token Classification
Transformers
TensorBoard
Safetensors
layoutlm
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Files changed (4) hide show
  1. README.md +25 -25
  2. logs/events.out.tfevents.1703747876.dlmachine2.187596.0 +2 -2
  3. model.safetensors +1 -1
  4. tokenizer.json +16 -2
README.md CHANGED
@@ -15,14 +15,14 @@ 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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6968
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- - Answer: {'precision': 0.7076923076923077, 'recall': 0.796044499381953, 'f1': 0.7492728330424666, 'number': 809}
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- - Header: {'precision': 0.3805309734513274, 'recall': 0.36134453781512604, 'f1': 0.3706896551724138, 'number': 119}
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- - Question: {'precision': 0.7793721973094171, 'recall': 0.815962441314554, 'f1': 0.7972477064220184, 'number': 1065}
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- - Overall Precision: 0.7278
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- - Overall Recall: 0.7807
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- - Overall F1: 0.7533
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- - Overall Accuracy: 0.8046
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  ## Model description
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@@ -52,23 +52,23 @@ 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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- | 1.834 | 1.0 | 10 | 1.6241 | {'precision': 0.008517887563884156, 'recall': 0.006180469715698393, 'f1': 0.007163323782234957, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2681912681912682, 'recall': 0.12112676056338029, 'f1': 0.16688227684346701, 'number': 1065} | 0.1255 | 0.0672 | 0.0876 | 0.3353 |
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- | 1.4921 | 2.0 | 20 | 1.2870 | {'precision': 0.18115942028985507, 'recall': 0.21631644004944375, 'f1': 0.19718309859154928, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.41122213681783243, 'recall': 0.5023474178403756, 'f1': 0.452240067624683, 'number': 1065} | 0.3132 | 0.3562 | 0.3333 | 0.5844 |
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- | 1.1743 | 3.0 | 30 | 0.9788 | {'precision': 0.44285714285714284, 'recall': 0.5747836835599506, 'f1': 0.5002689618074233, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5620496397117695, 'recall': 0.6591549295774648, 'f1': 0.606741573033708, 'number': 1065} | 0.5043 | 0.5855 | 0.5419 | 0.6859 |
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- | 0.8858 | 4.0 | 40 | 0.8011 | {'precision': 0.5779467680608364, 'recall': 0.7515451174289246, 'f1': 0.6534121440085975, 'number': 809} | {'precision': 0.08695652173913043, 'recall': 0.03361344537815126, 'f1': 0.048484848484848485, 'number': 119} | {'precision': 0.6457094307561597, 'recall': 0.7136150234741784, 'f1': 0.6779661016949151, 'number': 1065} | 0.6031 | 0.6884 | 0.6429 | 0.7449 |
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- | 0.7086 | 5.0 | 50 | 0.7224 | {'precision': 0.6253902185223725, 'recall': 0.7428924598269468, 'f1': 0.6790960451977401, 'number': 809} | {'precision': 0.21052631578947367, 'recall': 0.10084033613445378, 'f1': 0.13636363636363635, 'number': 119} | {'precision': 0.6908783783783784, 'recall': 0.7680751173708921, 'f1': 0.7274344152956871, 'number': 1065} | 0.6499 | 0.7180 | 0.6822 | 0.7736 |
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- | 0.5921 | 6.0 | 60 | 0.6817 | {'precision': 0.646878198567042, 'recall': 0.7812113720642769, 'f1': 0.7077267637178051, 'number': 809} | {'precision': 0.29411764705882354, 'recall': 0.16806722689075632, 'f1': 0.21390374331550802, 'number': 119} | {'precision': 0.7291666666666666, 'recall': 0.7887323943661971, 'f1': 0.7577807848443843, 'number': 1065} | 0.6791 | 0.7486 | 0.7122 | 0.7935 |
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- | 0.5194 | 7.0 | 70 | 0.6736 | {'precision': 0.6726057906458798, 'recall': 0.7466007416563659, 'f1': 0.7076742823667252, 'number': 809} | {'precision': 0.26126126126126126, 'recall': 0.24369747899159663, 'f1': 0.25217391304347825, 'number': 119} | {'precision': 0.7426597582037997, 'recall': 0.8075117370892019, 'f1': 0.7737291947818264, 'number': 1065} | 0.6890 | 0.7491 | 0.7178 | 0.7950 |
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- | 0.4598 | 8.0 | 80 | 0.6587 | {'precision': 0.6781115879828327, 'recall': 0.7812113720642769, 'f1': 0.7260195290063183, 'number': 809} | {'precision': 0.288135593220339, 'recall': 0.2857142857142857, 'f1': 0.2869198312236287, 'number': 119} | {'precision': 0.7576821773485514, 'recall': 0.8103286384976526, 'f1': 0.7831215970961887, 'number': 1065} | 0.6985 | 0.7672 | 0.7312 | 0.8050 |
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- | 0.3976 | 9.0 | 90 | 0.6732 | {'precision': 0.6772486772486772, 'recall': 0.7911001236093943, 'f1': 0.7297605473204103, 'number': 809} | {'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119} | {'precision': 0.768609865470852, 'recall': 0.8046948356807512, 'f1': 0.7862385321100916, 'number': 1065} | 0.7052 | 0.7682 | 0.7354 | 0.7987 |
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- | 0.3672 | 10.0 | 100 | 0.6696 | {'precision': 0.683982683982684, 'recall': 0.7812113720642769, 'f1': 0.7293710328909406, 'number': 809} | {'precision': 0.3114754098360656, 'recall': 0.31932773109243695, 'f1': 0.3153526970954357, 'number': 119} | {'precision': 0.773936170212766, 'recall': 0.819718309859155, 'f1': 0.796169630642955, 'number': 1065} | 0.7098 | 0.7742 | 0.7406 | 0.8047 |
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- | 0.3431 | 11.0 | 110 | 0.6742 | {'precision': 0.698237885462555, 'recall': 0.7836835599505563, 'f1': 0.7384973791496796, 'number': 809} | {'precision': 0.34545454545454546, 'recall': 0.31932773109243695, 'f1': 0.3318777292576419, 'number': 119} | {'precision': 0.7726075504828798, 'recall': 0.8262910798122066, 'f1': 0.7985480943738658, 'number': 1065} | 0.7195 | 0.7787 | 0.7480 | 0.8059 |
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- | 0.3204 | 12.0 | 120 | 0.6759 | {'precision': 0.6983783783783784, 'recall': 0.7985166872682324, 'f1': 0.7450980392156863, 'number': 809} | {'precision': 0.34146341463414637, 'recall': 0.35294117647058826, 'f1': 0.34710743801652894, 'number': 119} | {'precision': 0.779385171790235, 'recall': 0.8093896713615023, 'f1': 0.7941040994933211, 'number': 1065} | 0.7196 | 0.7777 | 0.7475 | 0.8052 |
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- | 0.308 | 13.0 | 130 | 0.6854 | {'precision': 0.6980728051391863, 'recall': 0.8059332509270705, 'f1': 0.7481353987378083, 'number': 809} | {'precision': 0.36752136752136755, 'recall': 0.36134453781512604, 'f1': 0.3644067796610169, 'number': 119} | {'precision': 0.7732142857142857, 'recall': 0.8131455399061033, 'f1': 0.7926773455377575, 'number': 1065} | 0.7190 | 0.7832 | 0.7498 | 0.8029 |
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- | 0.287 | 14.0 | 140 | 0.6927 | {'precision': 0.7041484716157205, 'recall': 0.7972805933250927, 'f1': 0.7478260869565218, 'number': 809} | {'precision': 0.3761467889908257, 'recall': 0.3445378151260504, 'f1': 0.3596491228070175, 'number': 119} | {'precision': 0.7774798927613941, 'recall': 0.8169014084507042, 'f1': 0.7967032967032968, 'number': 1065} | 0.7257 | 0.7807 | 0.7522 | 0.8047 |
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- | 0.2918 | 15.0 | 150 | 0.6968 | {'precision': 0.7076923076923077, 'recall': 0.796044499381953, 'f1': 0.7492728330424666, 'number': 809} | {'precision': 0.3805309734513274, 'recall': 0.36134453781512604, 'f1': 0.3706896551724138, 'number': 119} | {'precision': 0.7793721973094171, 'recall': 0.815962441314554, 'f1': 0.7972477064220184, 'number': 1065} | 0.7278 | 0.7807 | 0.7533 | 0.8046 |
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  ### Framework versions
 
15
 
16
  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 0.7235
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+ - Answer: {'precision': 0.6962719298245614, 'recall': 0.7849196538936959, 'f1': 0.73794305636258, 'number': 809}
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+ - Header: {'precision': 0.27692307692307694, 'recall': 0.3025210084033613, 'f1': 0.2891566265060241, 'number': 119}
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+ - Question: {'precision': 0.7558039552880481, 'recall': 0.8253521126760563, 'f1': 0.789048473967684, 'number': 1065}
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+ - Overall Precision: 0.7029
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+ - Overall Recall: 0.7777
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+ - Overall F1: 0.7384
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+ - Overall Accuracy: 0.7998
26
 
27
  ## Model description
28
 
 
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  ### Training results
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55
+ | 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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+ | 1.7228 | 1.0 | 10 | 1.5183 | {'precision': 0.060676779463243874, 'recall': 0.06427688504326329, 'f1': 0.062424969987995196, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3057324840764331, 'recall': 0.4056338028169014, 'f1': 0.3486682808716707, 'number': 1065} | 0.2132 | 0.2428 | 0.2271 | 0.4422 |
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+ | 1.3399 | 2.0 | 20 | 1.1666 | {'precision': 0.27170868347338933, 'recall': 0.23980222496909764, 'f1': 0.25476034143138543, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.45522949586155004, 'recall': 0.568075117370892, 'f1': 0.5054302422723475, 'number': 1065} | 0.3911 | 0.4009 | 0.3959 | 0.6011 |
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+ | 1.04 | 3.0 | 30 | 0.9328 | {'precision': 0.47839195979899496, 'recall': 0.588380716934487, 'f1': 0.5277161862527716, 'number': 809} | {'precision': 0.06818181818181818, 'recall': 0.025210084033613446, 'f1': 0.03680981595092025, 'number': 119} | {'precision': 0.6219201359388276, 'recall': 0.6873239436619718, 'f1': 0.6529884032114184, 'number': 1065} | 0.5465 | 0.6076 | 0.5754 | 0.7133 |
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+ | 0.8105 | 4.0 | 40 | 0.7992 | {'precision': 0.5817060637204522, 'recall': 0.6996291718170581, 'f1': 0.6352413019079686, 'number': 809} | {'precision': 0.0963855421686747, 'recall': 0.06722689075630252, 'f1': 0.07920792079207921, 'number': 119} | {'precision': 0.6542904290429042, 'recall': 0.7446009389671362, 'f1': 0.696530522617479, 'number': 1065} | 0.6027 | 0.6859 | 0.6416 | 0.7516 |
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+ | 0.6523 | 5.0 | 50 | 0.7333 | {'precision': 0.6176470588235294, 'recall': 0.7527812113720643, 'f1': 0.6785515320334262, 'number': 809} | {'precision': 0.20430107526881722, 'recall': 0.15966386554621848, 'f1': 0.1792452830188679, 'number': 119} | {'precision': 0.6836393989983306, 'recall': 0.7690140845070422, 'f1': 0.7238179407865665, 'number': 1065} | 0.6355 | 0.7260 | 0.6778 | 0.7724 |
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+ | 0.5591 | 6.0 | 60 | 0.7152 | {'precision': 0.6452304394426581, 'recall': 0.7441285537700866, 'f1': 0.6911595866819749, 'number': 809} | {'precision': 0.2222222222222222, 'recall': 0.18487394957983194, 'f1': 0.2018348623853211, 'number': 119} | {'precision': 0.6821086261980831, 'recall': 0.8018779342723005, 'f1': 0.7371601208459214, 'number': 1065} | 0.6471 | 0.7416 | 0.6911 | 0.7845 |
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+ | 0.494 | 7.0 | 70 | 0.6953 | {'precision': 0.652542372881356, 'recall': 0.761433868974042, 'f1': 0.7027952082144895, 'number': 809} | {'precision': 0.2184873949579832, 'recall': 0.2184873949579832, 'f1': 0.2184873949579832, 'number': 119} | {'precision': 0.7113316790736146, 'recall': 0.8075117370892019, 'f1': 0.7563764291996481, 'number': 1065} | 0.6611 | 0.7536 | 0.7043 | 0.7893 |
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+ | 0.4345 | 8.0 | 80 | 0.6955 | {'precision': 0.6485042735042735, 'recall': 0.7503090234857849, 'f1': 0.695702005730659, 'number': 809} | {'precision': 0.23809523809523808, 'recall': 0.25210084033613445, 'f1': 0.24489795918367344, 'number': 119} | {'precision': 0.7281632653061224, 'recall': 0.8375586854460094, 'f1': 0.7790393013100436, 'number': 1065} | 0.6686 | 0.7672 | 0.7145 | 0.7936 |
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+ | 0.3786 | 9.0 | 90 | 0.7151 | {'precision': 0.6762513312034079, 'recall': 0.7849196538936959, 'f1': 0.7265446224256292, 'number': 809} | {'precision': 0.24817518248175183, 'recall': 0.2857142857142857, 'f1': 0.265625, 'number': 119} | {'precision': 0.7582515611061552, 'recall': 0.7981220657276995, 'f1': 0.7776761207685269, 'number': 1065} | 0.6914 | 0.7622 | 0.7251 | 0.7907 |
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+ | 0.3465 | 10.0 | 100 | 0.7036 | {'precision': 0.6802197802197802, 'recall': 0.765142150803461, 'f1': 0.7201861547411287, 'number': 809} | {'precision': 0.2777777777777778, 'recall': 0.29411764705882354, 'f1': 0.28571428571428575, 'number': 119} | {'precision': 0.7470588235294118, 'recall': 0.8347417840375587, 'f1': 0.7884700665188471, 'number': 1065} | 0.6932 | 0.7742 | 0.7315 | 0.8004 |
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+ | 0.3289 | 11.0 | 110 | 0.7109 | {'precision': 0.6814734561213435, 'recall': 0.7775030902348579, 'f1': 0.7263279445727483, 'number': 809} | {'precision': 0.2692307692307692, 'recall': 0.29411764705882354, 'f1': 0.28112449799196787, 'number': 119} | {'precision': 0.7449832775919732, 'recall': 0.8366197183098592, 'f1': 0.7881468376824414, 'number': 1065} | 0.6914 | 0.7802 | 0.7331 | 0.7950 |
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+ | 0.3066 | 12.0 | 120 | 0.7106 | {'precision': 0.6941694169416942, 'recall': 0.7799752781211372, 'f1': 0.7345750873108267, 'number': 809} | {'precision': 0.2868217054263566, 'recall': 0.31092436974789917, 'f1': 0.2983870967741935, 'number': 119} | {'precision': 0.7540425531914894, 'recall': 0.831924882629108, 'f1': 0.7910714285714286, 'number': 1065} | 0.7022 | 0.7797 | 0.7389 | 0.7980 |
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+ | 0.2914 | 13.0 | 130 | 0.7253 | {'precision': 0.6913849509269356, 'recall': 0.7836835599505563, 'f1': 0.7346465816917729, 'number': 809} | {'precision': 0.2642857142857143, 'recall': 0.31092436974789917, 'f1': 0.28571428571428575, 'number': 119} | {'precision': 0.7402707275803723, 'recall': 0.8215962441314554, 'f1': 0.778816199376947, 'number': 1065} | 0.6905 | 0.7757 | 0.7306 | 0.7956 |
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+ | 0.2751 | 14.0 | 140 | 0.7191 | {'precision': 0.6818181818181818, 'recall': 0.7787391841779975, 'f1': 0.7270628967109058, 'number': 809} | {'precision': 0.2748091603053435, 'recall': 0.3025210084033613, 'f1': 0.288, 'number': 119} | {'precision': 0.7474489795918368, 'recall': 0.8253521126760563, 'f1': 0.784471218206158, 'number': 1065} | 0.6925 | 0.7752 | 0.7315 | 0.7991 |
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+ | 0.2769 | 15.0 | 150 | 0.7235 | {'precision': 0.6962719298245614, 'recall': 0.7849196538936959, 'f1': 0.73794305636258, 'number': 809} | {'precision': 0.27692307692307694, 'recall': 0.3025210084033613, 'f1': 0.2891566265060241, 'number': 119} | {'precision': 0.7558039552880481, 'recall': 0.8253521126760563, 'f1': 0.789048473967684, 'number': 1065} | 0.7029 | 0.7777 | 0.7384 | 0.7998 |
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  ### Framework versions
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