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layoutlm-funsd2

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  1. README.md +23 -26
  2. logs/events.out.tfevents.1718874374.HCIDC-SV-DMZ-ORC-NODE02.3991783.0 +2 -2
  3. model.safetensors +1 -1
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: 0.6965
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- - Answer: {'precision': 0.7010869565217391, 'recall': 0.7972805933250927, 'f1': 0.746096009253904, 'number': 809}
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- - Header: {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119}
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- - Question: {'precision': 0.7692307692307693, 'recall': 0.8262910798122066, 'f1': 0.7967406066093254, 'number': 1065}
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- - Overall Precision: 0.7162
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- - Overall Recall: 0.7852
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- - Overall F1: 0.7492
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- - Overall Accuracy: 0.8006
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  ## Model description
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@@ -49,28 +49,25 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 15
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  - mixed_precision_training: Native AMP
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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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- | 1.8343 | 1.0 | 10 | 1.5921 | {'precision': 0.006666666666666667, 'recall': 0.006180469715698393, 'f1': 0.006414368184733804, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.22067901234567902, 'recall': 0.13427230046948357, 'f1': 0.166958552247519, 'number': 1065} | 0.1059 | 0.0743 | 0.0873 | 0.3510 |
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- | 1.4828 | 2.0 | 20 | 1.2849 | {'precision': 0.2738799661876585, 'recall': 0.4004944375772559, 'f1': 0.32530120481927705, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.38058114812189936, 'recall': 0.504225352112676, 'f1': 0.43376413570274636, 'number': 1065} | 0.3314 | 0.4320 | 0.3751 | 0.5951 |
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- | 1.1444 | 3.0 | 30 | 0.9563 | {'precision': 0.4725897920604915, 'recall': 0.6180469715698393, 'f1': 0.5356186395286556, 'number': 809} | {'precision': 0.041666666666666664, 'recall': 0.01680672268907563, 'f1': 0.02395209580838323, 'number': 119} | {'precision': 0.5378020265003897, 'recall': 0.647887323943662, 'f1': 0.5877342419080069, 'number': 1065} | 0.4990 | 0.5981 | 0.5440 | 0.6955 |
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- | 0.8658 | 4.0 | 40 | 0.7885 | {'precision': 0.5757009345794393, 'recall': 0.761433868974042, 'f1': 0.6556679084619478, 'number': 809} | {'precision': 0.1388888888888889, 'recall': 0.08403361344537816, 'f1': 0.10471204188481677, 'number': 119} | {'precision': 0.6567299006323396, 'recall': 0.6826291079812207, 'f1': 0.6694290976058932, 'number': 1065} | 0.6016 | 0.6789 | 0.6379 | 0.7601 |
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- | 0.6833 | 5.0 | 50 | 0.7124 | {'precision': 0.64375, 'recall': 0.7639060568603214, 'f1': 0.6986998304126625, 'number': 809} | {'precision': 0.35, 'recall': 0.23529411764705882, 'f1': 0.28140703517587945, 'number': 119} | {'precision': 0.6729354047424366, 'recall': 0.7727699530516432, 'f1': 0.7194055944055944, 'number': 1065} | 0.6491 | 0.7371 | 0.6903 | 0.7810 |
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- | 0.5898 | 6.0 | 60 | 0.6874 | {'precision': 0.6227141482194418, 'recall': 0.799752781211372, 'f1': 0.7002164502164502, 'number': 809} | {'precision': 0.3411764705882353, 'recall': 0.24369747899159663, 'f1': 0.28431372549019607, 'number': 119} | {'precision': 0.7236492471213464, 'recall': 0.7671361502347418, 'f1': 0.7447584320875114, 'number': 1065} | 0.6627 | 0.7491 | 0.7033 | 0.7851 |
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- | 0.5126 | 7.0 | 70 | 0.6599 | {'precision': 0.6705632306057385, 'recall': 0.7799752781211372, 'f1': 0.7211428571428572, 'number': 809} | {'precision': 0.32673267326732675, 'recall': 0.2773109243697479, 'f1': 0.30000000000000004, 'number': 119} | {'precision': 0.7427821522309711, 'recall': 0.7971830985915493, 'f1': 0.7690217391304347, 'number': 1065} | 0.6924 | 0.7592 | 0.7243 | 0.7963 |
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- | 0.4534 | 8.0 | 80 | 0.6562 | {'precision': 0.670490093847758, 'recall': 0.7948084054388134, 'f1': 0.7273755656108597, 'number': 809} | {'precision': 0.2966101694915254, 'recall': 0.29411764705882354, 'f1': 0.2953586497890296, 'number': 119} | {'precision': 0.7476475620188195, 'recall': 0.8206572769953052, 'f1': 0.7824529991047449, 'number': 1065} | 0.6910 | 0.7787 | 0.7322 | 0.7954 |
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- | 0.3984 | 9.0 | 90 | 0.6561 | {'precision': 0.6838709677419355, 'recall': 0.7861557478368356, 'f1': 0.7314548591144335, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3025210084033613, 'f1': 0.3171806167400881, 'number': 119} | {'precision': 0.7555555555555555, 'recall': 0.8300469483568075, 'f1': 0.7910514541387024, 'number': 1065} | 0.7047 | 0.7807 | 0.7408 | 0.7986 |
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- | 0.3865 | 10.0 | 100 | 0.6673 | {'precision': 0.6877005347593583, 'recall': 0.7948084054388134, 'f1': 0.7373853211009175, 'number': 809} | {'precision': 0.31666666666666665, 'recall': 0.31932773109243695, 'f1': 0.3179916317991632, 'number': 119} | {'precision': 0.7613240418118467, 'recall': 0.8206572769953052, 'f1': 0.7898779936737461, 'number': 1065} | 0.7059 | 0.7802 | 0.7412 | 0.8019 |
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- | 0.3343 | 11.0 | 110 | 0.6761 | {'precision': 0.6853220696937699, 'recall': 0.8022249690976514, 'f1': 0.7391799544419134, 'number': 809} | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119} | {'precision': 0.7692307692307693, 'recall': 0.8262910798122066, 'f1': 0.7967406066093254, 'number': 1065} | 0.7110 | 0.7863 | 0.7467 | 0.7998 |
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- | 0.314 | 12.0 | 120 | 0.6772 | {'precision': 0.6989130434782609, 'recall': 0.7948084054388134, 'f1': 0.7437825332562175, 'number': 809} | {'precision': 0.34545454545454546, 'recall': 0.31932773109243695, 'f1': 0.3318777292576419, 'number': 119} | {'precision': 0.7698343504795118, 'recall': 0.8291079812206573, 'f1': 0.7983725135623869, 'number': 1065} | 0.7184 | 0.7847 | 0.7501 | 0.8053 |
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- | 0.3008 | 13.0 | 130 | 0.6878 | {'precision': 0.7048648648648649, 'recall': 0.8059332509270705, 'f1': 0.7520184544405998, 'number': 809} | {'precision': 0.33620689655172414, 'recall': 0.3277310924369748, 'f1': 0.33191489361702126, 'number': 119} | {'precision': 0.7689594356261023, 'recall': 0.8187793427230047, 'f1': 0.793087767166894, 'number': 1065} | 0.7186 | 0.7842 | 0.75 | 0.8033 |
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- | 0.2797 | 14.0 | 140 | 0.6948 | {'precision': 0.7027322404371584, 'recall': 0.7948084054388134, 'f1': 0.7459396751740139, 'number': 809} | {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119} | {'precision': 0.7661996497373029, 'recall': 0.8215962441314554, 'f1': 0.7929315813321249, 'number': 1065} | 0.7137 | 0.7817 | 0.7462 | 0.8017 |
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- | 0.2722 | 15.0 | 150 | 0.6965 | {'precision': 0.7010869565217391, 'recall': 0.7972805933250927, 'f1': 0.746096009253904, 'number': 809} | {'precision': 0.3305785123966942, 'recall': 0.33613445378151263, 'f1': 0.33333333333333337, 'number': 119} | {'precision': 0.7692307692307693, 'recall': 0.8262910798122066, 'f1': 0.7967406066093254, 'number': 1065} | 0.7162 | 0.7852 | 0.7492 | 0.8006 |
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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:
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+ - Loss: 0.6614
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+ - Answer: {'precision': 0.6683778234086243, 'recall': 0.8046971569839307, 'f1': 0.7302299495232752, 'number': 809}
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+ - Header: {'precision': 0.3130434782608696, 'recall': 0.3025210084033613, 'f1': 0.3076923076923077, 'number': 119}
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+ - Question: {'precision': 0.7667814113597247, 'recall': 0.8366197183098592, 'f1': 0.8001796138302649, 'number': 1065}
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+ - Overall Precision: 0.7010
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+ - Overall Recall: 0.7918
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+ - Overall F1: 0.7436
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+ - Overall Accuracy: 0.8029
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 12
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  - mixed_precision_training: Native AMP
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55
  ### 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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+ | 1.8071 | 1.0 | 10 | 1.5850 | {'precision': 0.011918951132300357, 'recall': 0.012360939431396786, 'f1': 0.012135922330097087, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.17111459968602827, 'recall': 0.10234741784037558, 'f1': 0.1280846063454759, 'number': 1065} | 0.0806 | 0.0597 | 0.0686 | 0.3795 |
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+ | 1.4934 | 2.0 | 20 | 1.2707 | {'precision': 0.09924812030075188, 'recall': 0.0815822002472188, 'f1': 0.08955223880597016, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4546952224052718, 'recall': 0.5183098591549296, 'f1': 0.484422992540588, 'number': 1065} | 0.3289 | 0.3101 | 0.3192 | 0.5753 |
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+ | 1.1823 | 3.0 | 30 | 0.9970 | {'precision': 0.4033214709371293, 'recall': 0.42027194066749074, 'f1': 0.4116222760290557, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5919540229885057, 'recall': 0.6769953051643193, 'f1': 0.6316250547525186, 'number': 1065} | 0.5106 | 0.5324 | 0.5212 | 0.6915 |
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+ | 0.9185 | 4.0 | 40 | 0.8213 | {'precision': 0.6075156576200418, 'recall': 0.7194066749072929, 'f1': 0.6587436332767402, 'number': 809} | {'precision': 0.05128205128205128, 'recall': 0.01680672268907563, 'f1': 0.025316455696202535, 'number': 119} | {'precision': 0.6559048428207307, 'recall': 0.7248826291079812, 'f1': 0.6886708296164139, 'number': 1065} | 0.6237 | 0.6804 | 0.6508 | 0.7467 |
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+ | 0.7233 | 5.0 | 50 | 0.7353 | {'precision': 0.638974358974359, 'recall': 0.7700865265760197, 'f1': 0.6984304932735426, 'number': 809} | {'precision': 0.22093023255813954, 'recall': 0.15966386554621848, 'f1': 0.18536585365853656, 'number': 119} | {'precision': 0.6809716599190283, 'recall': 0.7896713615023474, 'f1': 0.731304347826087, 'number': 1065} | 0.6459 | 0.7441 | 0.6915 | 0.7794 |
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+ | 0.6262 | 6.0 | 60 | 0.7036 | {'precision': 0.632512315270936, 'recall': 0.7935723114956736, 'f1': 0.7039473684210525, 'number': 809} | {'precision': 0.24324324324324326, 'recall': 0.15126050420168066, 'f1': 0.18652849740932642, 'number': 119} | {'precision': 0.7235345581802275, 'recall': 0.7765258215962442, 'f1': 0.7490942028985508, 'number': 1065} | 0.6662 | 0.7461 | 0.7039 | 0.7818 |
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+ | 0.5552 | 7.0 | 70 | 0.6694 | {'precision': 0.6639089968976215, 'recall': 0.7935723114956736, 'f1': 0.722972972972973, 'number': 809} | {'precision': 0.24770642201834864, 'recall': 0.226890756302521, 'f1': 0.23684210526315788, 'number': 119} | {'precision': 0.730999146029035, 'recall': 0.8037558685446009, 'f1': 0.7656529516994633, 'number': 1065} | 0.6787 | 0.7652 | 0.7193 | 0.7913 |
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+ | 0.5016 | 8.0 | 80 | 0.6598 | {'precision': 0.6592517694641051, 'recall': 0.8059332509270705, 'f1': 0.7252502780867631, 'number': 809} | {'precision': 0.24324324324324326, 'recall': 0.226890756302521, 'f1': 0.23478260869565218, 'number': 119} | {'precision': 0.7482817869415808, 'recall': 0.8178403755868544, 'f1': 0.781516375056079, 'number': 1065} | 0.6846 | 0.7777 | 0.7282 | 0.7931 |
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+ | 0.4496 | 9.0 | 90 | 0.6561 | {'precision': 0.6663265306122449, 'recall': 0.8071693448702101, 'f1': 0.7300167691447736, 'number': 809} | {'precision': 0.2743362831858407, 'recall': 0.2605042016806723, 'f1': 0.26724137931034486, 'number': 119} | {'precision': 0.7584708948740226, 'recall': 0.819718309859155, 'f1': 0.7879061371841156, 'number': 1065} | 0.6939 | 0.7812 | 0.7350 | 0.7982 |
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+ | 0.4481 | 10.0 | 100 | 0.6633 | {'precision': 0.6711340206185566, 'recall': 0.8046971569839307, 'f1': 0.7318718381112984, 'number': 809} | {'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119} | {'precision': 0.7640350877192983, 'recall': 0.8178403755868544, 'f1': 0.7900226757369614, 'number': 1065} | 0.7003 | 0.7797 | 0.7379 | 0.7987 |
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+ | 0.4012 | 11.0 | 110 | 0.6624 | {'precision': 0.6625766871165644, 'recall': 0.8009888751545118, 'f1': 0.7252378287632905, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3025210084033613, 'f1': 0.3171806167400881, 'number': 119} | {'precision': 0.7696969696969697, 'recall': 0.8347417840375587, 'f1': 0.8009009009009008, 'number': 1065} | 0.7019 | 0.7893 | 0.7430 | 0.8074 |
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+ | 0.4065 | 12.0 | 120 | 0.6614 | {'precision': 0.6683778234086243, 'recall': 0.8046971569839307, 'f1': 0.7302299495232752, 'number': 809} | {'precision': 0.3130434782608696, 'recall': 0.3025210084033613, 'f1': 0.3076923076923077, 'number': 119} | {'precision': 0.7667814113597247, 'recall': 0.8366197183098592, 'f1': 0.8001796138302649, 'number': 1065} | 0.7010 | 0.7918 | 0.7436 | 0.8029 |
 
 
 
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  ### Framework versions
logs/events.out.tfevents.1718874374.HCIDC-SV-DMZ-ORC-NODE02.3991783.0 CHANGED
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