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Benedict-L
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layoutlm-funsd1

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layoutlm
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  1. README.md +22 -22
  2. logs/events.out.tfevents.1719799708.HCIDC-SV-DMZ-ORC-NODE02.1891990.0 +2 -2
README.md CHANGED
@@ -4,7 +4,7 @@ base_model: microsoft/layoutlm-base-uncased
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  tags:
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  - generated_from_trainer
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  datasets:
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- - my_funsd
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  model-index:
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  - name: layoutlm-funsd1
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  results: []
@@ -15,16 +15,16 @@ should probably proofread and complete it, then remove this comment. -->
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  # layoutlm-funsd1
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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 my_funsd dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0974
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- - Answer: {'precision': 0.5384615384615384, 'recall': 0.4666666666666667, 'f1': 0.5, 'number': 15}
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- - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
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- - Question: {'precision': 0.3333333333333333, 'recall': 0.13333333333333333, 'f1': 0.19047619047619044, 'number': 15}
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- - Overall Precision: 0.4737
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- - Overall Recall: 0.2903
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- - Overall F1: 0.36
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- - Overall Accuracy: 0.6937
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  ## Model description
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@@ -54,18 +54,18 @@ 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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- | 1.9267 | 1.0 | 1 | 1.9079 | {'precision': 0.3125, 'recall': 0.3333333333333333, 'f1': 0.3225806451612903, 'number': 15} | {'precision': 0.07692307692307693, 'recall': 1.0, 'f1': 0.14285714285714288, 'number': 1} | {'precision': 0.21052631578947367, 'recall': 0.26666666666666666, 'f1': 0.23529411764705882, 'number': 15} | 0.2083 | 0.3226 | 0.2532 | 0.2523 |
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- | 1.9277 | 2.0 | 2 | 1.7357 | {'precision': 0.42857142857142855, 'recall': 0.4, 'f1': 0.4137931034482759, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.2222222222222222, 'recall': 0.13333333333333333, 'f1': 0.16666666666666669, 'number': 15} | 0.3333 | 0.2581 | 0.2909 | 0.4144 |
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- | 1.7536 | 3.0 | 3 | 1.5947 | {'precision': 0.3333333333333333, 'recall': 0.26666666666666666, 'f1': 0.2962962962962963, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.2353 | 0.1290 | 0.1667 | 0.4955 |
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- | 1.6284 | 4.0 | 4 | 1.4767 | {'precision': 0.3333333333333333, 'recall': 0.26666666666666666, 'f1': 0.2962962962962963, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.2667 | 0.1290 | 0.1739 | 0.5586 |
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- | 1.5334 | 5.0 | 5 | 1.3768 | {'precision': 0.4166666666666667, 'recall': 0.3333333333333333, 'f1': 0.3703703703703704, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.3125 | 0.1613 | 0.2128 | 0.6036 |
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- | 1.4284 | 6.0 | 6 | 1.2919 | {'precision': 0.5454545454545454, 'recall': 0.4, 'f1': 0.4615384615384615, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.375 | 0.1935 | 0.2553 | 0.6216 |
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- | 1.3646 | 7.0 | 7 | 1.2220 | {'precision': 0.5454545454545454, 'recall': 0.4, 'f1': 0.4615384615384615, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.375 | 0.1935 | 0.2553 | 0.6577 |
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- | 1.3005 | 8.0 | 8 | 1.1665 | {'precision': 0.5384615384615384, 'recall': 0.4666666666666667, 'f1': 0.5, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 15} | 0.3889 | 0.2258 | 0.2857 | 0.6667 |
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- | 1.2501 | 9.0 | 9 | 1.1250 | {'precision': 0.5384615384615384, 'recall': 0.4666666666666667, 'f1': 0.5, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.16666666666666666, 'recall': 0.06666666666666667, 'f1': 0.09523809523809522, 'number': 15} | 0.4211 | 0.2581 | 0.3200 | 0.6847 |
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- | 1.1952 | 10.0 | 10 | 1.0974 | {'precision': 0.5384615384615384, 'recall': 0.4666666666666667, 'f1': 0.5, 'number': 15} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1} | {'precision': 0.3333333333333333, 'recall': 0.13333333333333333, 'f1': 0.19047619047619044, 'number': 15} | 0.4737 | 0.2903 | 0.36 | 0.6937 |
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  ### Framework versions
 
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  tags:
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  - generated_from_trainer
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  datasets:
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+ - funsd
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  model-index:
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  - name: layoutlm-funsd1
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  results: []
 
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  # layoutlm-funsd1
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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: 0.6511
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+ - Answer: {'precision': 0.6761487964989059, 'recall': 0.7639060568603214, 'f1': 0.7173534532791643, 'number': 809}
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+ - Header: {'precision': 0.24545454545454545, 'recall': 0.226890756302521, 'f1': 0.23580786026200873, 'number': 119}
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+ - Question: {'precision': 0.7472245943637916, 'recall': 0.8215962441314554, 'f1': 0.7826475849731663, 'number': 1065}
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+ - Overall Precision: 0.6925
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+ - Overall Recall: 0.7627
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+ - Overall F1: 0.7259
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+ - Overall Accuracy: 0.7992
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  ## Model description
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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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+ | 1.7571 | 1.0 | 10 | 1.5405 | {'precision': 0.0392156862745098, 'recall': 0.0519159456118665, 'f1': 0.04468085106382978, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.23129251700680273, 'recall': 0.3511737089201878, 'f1': 0.27889634601044, 'number': 1065} | 0.1548 | 0.2087 | 0.1777 | 0.4539 |
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+ | 1.4002 | 2.0 | 20 | 1.2087 | {'precision': 0.21976592977893367, 'recall': 0.2088998763906057, 'f1': 0.21419518377693283, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4806934594168637, 'recall': 0.5727699530516432, 'f1': 0.5227077977720652, 'number': 1065} | 0.3822 | 0.3909 | 0.3865 | 0.5991 |
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+ | 1.0781 | 3.0 | 30 | 0.9612 | {'precision': 0.437219730941704, 'recall': 0.4820766378244747, 'f1': 0.4585537918871252, 'number': 809} | {'precision': 0.030303030303030304, 'recall': 0.008403361344537815, 'f1': 0.013157894736842105, 'number': 119} | {'precision': 0.6361233480176212, 'recall': 0.6779342723004694, 'f1': 0.6563636363636363, 'number': 1065} | 0.5403 | 0.5585 | 0.5492 | 0.6934 |
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+ | 0.8462 | 4.0 | 40 | 0.7985 | {'precision': 0.5972515856236786, 'recall': 0.6983930778739185, 'f1': 0.6438746438746439, 'number': 809} | {'precision': 0.11363636363636363, 'recall': 0.04201680672268908, 'f1': 0.06134969325153375, 'number': 119} | {'precision': 0.6884955752212389, 'recall': 0.7305164319248826, 'f1': 0.7088838268792711, 'number': 1065} | 0.6358 | 0.6764 | 0.6555 | 0.7564 |
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+ | 0.6873 | 5.0 | 50 | 0.7161 | {'precision': 0.6699779249448123, 'recall': 0.7503090234857849, 'f1': 0.707871720116618, 'number': 809} | {'precision': 0.23529411764705882, 'recall': 0.16806722689075632, 'f1': 0.19607843137254902, 'number': 119} | {'precision': 0.6994022203245089, 'recall': 0.7690140845070422, 'f1': 0.7325581395348838, 'number': 1065} | 0.6688 | 0.7255 | 0.6960 | 0.7858 |
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+ | 0.5786 | 6.0 | 60 | 0.6912 | {'precision': 0.6480505795574288, 'recall': 0.7601977750309024, 'f1': 0.6996587030716724, 'number': 809} | {'precision': 0.2638888888888889, 'recall': 0.15966386554621848, 'f1': 0.19895287958115182, 'number': 119} | {'precision': 0.7293700088731144, 'recall': 0.7718309859154929, 'f1': 0.7499999999999999, 'number': 1065} | 0.6778 | 0.7306 | 0.7032 | 0.7848 |
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+ | 0.5389 | 7.0 | 70 | 0.6760 | {'precision': 0.6835722160970231, 'recall': 0.7663782447466008, 'f1': 0.7226107226107226, 'number': 809} | {'precision': 0.21978021978021978, 'recall': 0.16806722689075632, 'f1': 0.1904761904761905, 'number': 119} | {'precision': 0.7195723684210527, 'recall': 0.8215962441314554, 'f1': 0.7672073651907059, 'number': 1065} | 0.6843 | 0.7602 | 0.7202 | 0.7929 |
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+ | 0.491 | 8.0 | 80 | 0.6643 | {'precision': 0.6782608695652174, 'recall': 0.7713226205191595, 'f1': 0.7218045112781956, 'number': 809} | {'precision': 0.2708333333333333, 'recall': 0.2184873949579832, 'f1': 0.24186046511627907, 'number': 119} | {'precision': 0.757847533632287, 'recall': 0.7934272300469484, 'f1': 0.7752293577981653, 'number': 1065} | 0.7015 | 0.7501 | 0.7250 | 0.7969 |
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+ | 0.4543 | 9.0 | 90 | 0.6519 | {'precision': 0.6808743169398908, 'recall': 0.7700865265760197, 'f1': 0.722737819025522, 'number': 809} | {'precision': 0.24509803921568626, 'recall': 0.21008403361344538, 'f1': 0.22624434389140272, 'number': 119} | {'precision': 0.7564102564102564, 'recall': 0.8309859154929577, 'f1': 0.7919463087248323, 'number': 1065} | 0.7010 | 0.7692 | 0.7335 | 0.8003 |
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+ | 0.4461 | 10.0 | 100 | 0.6511 | {'precision': 0.6761487964989059, 'recall': 0.7639060568603214, 'f1': 0.7173534532791643, 'number': 809} | {'precision': 0.24545454545454545, 'recall': 0.226890756302521, 'f1': 0.23580786026200873, 'number': 119} | {'precision': 0.7472245943637916, 'recall': 0.8215962441314554, 'f1': 0.7826475849731663, 'number': 1065} | 0.6925 | 0.7627 | 0.7259 | 0.7992 |
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  ### Framework versions
logs/events.out.tfevents.1719799708.HCIDC-SV-DMZ-ORC-NODE02.1891990.0 CHANGED
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