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layoutlm-funsd

Token Classification
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Safetensors
layoutlm
Generated from Trainer
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xet
 Metrics Community
riasharma commited on
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69cc054
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1 Parent(s): 2e52e1d

End of training

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  1. README.md +25 -25
  2. logs/events.out.tfevents.1703685981.dlmachine2.184251.0 +2 -2
  3. model.safetensors +1 -1
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: 0.6794
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- - Answer: {'precision': 0.7050592034445641, 'recall': 0.8096415327564895, 'f1': 0.7537399309551209, 'number': 809}
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- - Header: {'precision': 0.2971014492753623, 'recall': 0.3445378151260504, 'f1': 0.31906614785992216, 'number': 119}
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- - Question: {'precision': 0.7856502242152467, 'recall': 0.8225352112676056, 'f1': 0.8036697247706422, 'number': 1065}
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- - Overall Precision: 0.7204
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- - Overall Recall: 0.7888
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- - Overall F1: 0.7531
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- - Overall Accuracy: 0.8043
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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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- | 1.7235 | 1.0 | 10 | 1.5460 | {'precision': 0.024602026049204053, 'recall': 0.021013597033374538, 'f1': 0.02266666666666667, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4123076923076923, 'recall': 0.2516431924882629, 'f1': 0.31253644314868806, 'number': 1065} | 0.2125 | 0.1430 | 0.1710 | 0.3788 |
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- | 1.407 | 2.0 | 20 | 1.2205 | {'precision': 0.15948777648428406, 'recall': 0.16934487021013597, 'f1': 0.1642685851318945, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4168994413407821, 'recall': 0.5605633802816902, 'f1': 0.4781738085702843, 'number': 1065} | 0.3197 | 0.3683 | 0.3423 | 0.5851 |
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- | 1.0795 | 3.0 | 30 | 0.9267 | {'precision': 0.510934393638171, 'recall': 0.6353522867737948, 'f1': 0.5663911845730027, 'number': 809} | {'precision': 0.1111111111111111, 'recall': 0.04201680672268908, 'f1': 0.06097560975609755, 'number': 119} | {'precision': 0.5874476987447699, 'recall': 0.6591549295774648, 'f1': 0.6212389380530974, 'number': 1065} | 0.5436 | 0.6126 | 0.5761 | 0.7163 |
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- | 0.8244 | 4.0 | 40 | 0.7676 | {'precision': 0.6219758064516129, 'recall': 0.7626699629171817, 'f1': 0.6851749028317601, 'number': 809} | {'precision': 0.2191780821917808, 'recall': 0.13445378151260504, 'f1': 0.16666666666666669, 'number': 119} | {'precision': 0.6691666666666667, 'recall': 0.7539906103286385, 'f1': 0.7090507726269316, 'number': 1065} | 0.6340 | 0.7205 | 0.6745 | 0.7652 |
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- | 0.6702 | 5.0 | 50 | 0.7059 | {'precision': 0.6361848574237955, 'recall': 0.799752781211372, 'f1': 0.7086527929901425, 'number': 809} | {'precision': 0.25773195876288657, 'recall': 0.21008403361344538, 'f1': 0.23148148148148145, 'number': 119} | {'precision': 0.7138018628281118, 'recall': 0.7915492957746478, 'f1': 0.7506678539626003, 'number': 1065} | 0.6601 | 0.7602 | 0.7066 | 0.7714 |
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- | 0.5695 | 6.0 | 60 | 0.6803 | {'precision': 0.6496424923391215, 'recall': 0.7861557478368356, 'f1': 0.7114093959731544, 'number': 809} | {'precision': 0.25, 'recall': 0.2184873949579832, 'f1': 0.23318385650224216, 'number': 119} | {'precision': 0.7203098106712564, 'recall': 0.7859154929577464, 'f1': 0.7516838796587336, 'number': 1065} | 0.6677 | 0.7521 | 0.7074 | 0.7823 |
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- | 0.5039 | 7.0 | 70 | 0.6660 | {'precision': 0.6928034371643395, 'recall': 0.7972805933250927, 'f1': 0.7413793103448275, 'number': 809} | {'precision': 0.2222222222222222, 'recall': 0.2689075630252101, 'f1': 0.24334600760456274, 'number': 119} | {'precision': 0.7471466198419666, 'recall': 0.7990610328638498, 'f1': 0.7722323049001815, 'number': 1065} | 0.6902 | 0.7667 | 0.7264 | 0.7893 |
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- | 0.4396 | 8.0 | 80 | 0.6420 | {'precision': 0.6763485477178424, 'recall': 0.8059332509270705, 'f1': 0.7354765933446138, 'number': 809} | {'precision': 0.23484848484848486, 'recall': 0.2605042016806723, 'f1': 0.24701195219123506, 'number': 119} | {'precision': 0.7559523809523809, 'recall': 0.8347417840375587, 'f1': 0.7933958054439983, 'number': 1065} | 0.6919 | 0.7888 | 0.7372 | 0.7965 |
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- | 0.3747 | 9.0 | 90 | 0.6437 | {'precision': 0.6881606765327696, 'recall': 0.8046971569839307, 'f1': 0.7418803418803418, 'number': 809} | {'precision': 0.22535211267605634, 'recall': 0.2689075630252101, 'f1': 0.24521072796934865, 'number': 119} | {'precision': 0.7732506643046945, 'recall': 0.819718309859155, 'f1': 0.7958067456700091, 'number': 1065} | 0.7018 | 0.7807 | 0.7392 | 0.7979 |
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- | 0.3415 | 10.0 | 100 | 0.6580 | {'precision': 0.69989281886388, 'recall': 0.8071693448702101, 'f1': 0.7497129735935705, 'number': 809} | {'precision': 0.23076923076923078, 'recall': 0.25210084033613445, 'f1': 0.24096385542168675, 'number': 119} | {'precision': 0.7621527777777778, 'recall': 0.8244131455399061, 'f1': 0.7920613441587732, 'number': 1065} | 0.7047 | 0.7832 | 0.7419 | 0.8022 |
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- | 0.3206 | 11.0 | 110 | 0.6671 | {'precision': 0.7009544008483564, 'recall': 0.8170580964153276, 'f1': 0.754566210045662, 'number': 809} | {'precision': 0.2624113475177305, 'recall': 0.31092436974789917, 'f1': 0.2846153846153846, 'number': 119} | {'precision': 0.7675628794449263, 'recall': 0.8309859154929577, 'f1': 0.7980162308385933, 'number': 1065} | 0.7076 | 0.7943 | 0.7485 | 0.7997 |
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- | 0.2974 | 12.0 | 120 | 0.6651 | {'precision': 0.7023809523809523, 'recall': 0.8022249690976514, 'f1': 0.7489901904212348, 'number': 809} | {'precision': 0.3, 'recall': 0.35294117647058826, 'f1': 0.3243243243243243, 'number': 119} | {'precision': 0.7784697508896797, 'recall': 0.8215962441314554, 'f1': 0.7994518044769301, 'number': 1065} | 0.7157 | 0.7858 | 0.7491 | 0.8040 |
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- | 0.2809 | 13.0 | 130 | 0.6796 | {'precision': 0.7013669821240799, 'recall': 0.8244746600741656, 'f1': 0.7579545454545454, 'number': 809} | {'precision': 0.273972602739726, 'recall': 0.33613445378151263, 'f1': 0.3018867924528302, 'number': 119} | {'precision': 0.770999115826702, 'recall': 0.8187793427230047, 'f1': 0.7941712204007287, 'number': 1065} | 0.7087 | 0.7923 | 0.7482 | 0.8014 |
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- | 0.2664 | 14.0 | 140 | 0.6777 | {'precision': 0.6993534482758621, 'recall': 0.8022249690976514, 'f1': 0.7472654001151412, 'number': 809} | {'precision': 0.2826086956521739, 'recall': 0.3277310924369748, 'f1': 0.3035019455252918, 'number': 119} | {'precision': 0.785204991087344, 'recall': 0.8272300469483568, 'f1': 0.8056698673982624, 'number': 1065} | 0.7171 | 0.7873 | 0.7505 | 0.8025 |
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- | 0.2685 | 15.0 | 150 | 0.6794 | {'precision': 0.7050592034445641, 'recall': 0.8096415327564895, 'f1': 0.7537399309551209, 'number': 809} | {'precision': 0.2971014492753623, 'recall': 0.3445378151260504, 'f1': 0.31906614785992216, 'number': 119} | {'precision': 0.7856502242152467, 'recall': 0.8225352112676056, 'f1': 0.8036697247706422, 'number': 1065} | 0.7204 | 0.7888 | 0.7531 | 0.8043 |
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  ### Framework versions
 
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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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  ### 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
logs/events.out.tfevents.1703685981.dlmachine2.184251.0 CHANGED
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