Hugging Face's logo

pabloma09
/
layoutlm-funsd

Token Classification
Transformers
TensorBoard
Safetensors
layoutlm
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
pabloma09 commited on
Commit
7c79486
·
1 Parent(s): 34dec1e

End of training

Browse files
Files changed (3) hide show
  1. README.md +25 -25
  2. logs/events.out.tfevents.1703159383.DESKTOP-HA84SVN.4393.0 +2 -2
  3. model.safetensors +1 -1
README.md CHANGED
@@ -17,14 +17,14 @@ should probably proofread and complete it, then remove this comment. -->
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: 0.7001
21
- - Answer: {'precision': 0.7016574585635359, 'recall': 0.7849196538936959, 'f1': 0.7409568261376897, 'number': 809}
22
- - Header: {'precision': 0.3115942028985507, 'recall': 0.36134453781512604, 'f1': 0.3346303501945525, 'number': 119}
23
- - Question: {'precision': 0.7809439002671416, 'recall': 0.8234741784037559, 'f1': 0.8016453382084096, 'number': 1065}
24
- - Overall Precision: 0.7179
25
- - Overall Recall: 0.7802
26
- - Overall F1: 0.7478
27
- - Overall Accuracy: 0.8048
28
 
29
  ## Model description
30
 
@@ -54,23 +54,23 @@ The following hyperparameters were used during training:
54
 
55
  ### Training results
56
 
57
- | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
58
- |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
59
- | 1.7703 | 1.0 | 10 | 1.5577 | {'precision': 0.02032913843175218, 'recall': 0.02595797280593325, 'f1': 0.02280130293159609, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1728395061728395, 'recall': 0.17089201877934274, 'f1': 0.17186024551463644, 'number': 1065} | 0.0973 | 0.1019 | 0.0995 | 0.3886 |
60
- | 1.3909 | 2.0 | 20 | 1.1804 | {'precision': 0.2391304347826087, 'recall': 0.20395550061804696, 'f1': 0.22014676450967313, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4996186117467582, 'recall': 0.6150234741784038, 'f1': 0.5513468013468013, 'number': 1065} | 0.4098 | 0.4114 | 0.4106 | 0.6136 |
61
- | 1.045 | 3.0 | 30 | 0.9199 | {'precision': 0.5039018952062431, 'recall': 0.5587144622991347, 'f1': 0.5298944900351701, 'number': 809} | {'precision': 0.025, 'recall': 0.008403361344537815, 'f1': 0.012578616352201259, 'number': 119} | {'precision': 0.5976095617529881, 'recall': 0.704225352112676, 'f1': 0.6465517241379312, 'number': 1065} | 0.5488 | 0.6036 | 0.5749 | 0.7203 |
62
- | 0.7906 | 4.0 | 40 | 0.7703 | {'precision': 0.6037735849056604, 'recall': 0.7119901112484549, 'f1': 0.6534316505955757, 'number': 809} | {'precision': 0.20833333333333334, 'recall': 0.12605042016806722, 'f1': 0.15706806282722513, 'number': 119} | {'precision': 0.6666666666666666, 'recall': 0.7812206572769953, 'f1': 0.719412019022914, 'number': 1065} | 0.6258 | 0.7140 | 0.6670 | 0.7687 |
63
- | 0.635 | 5.0 | 50 | 0.7374 | {'precision': 0.6146179401993356, 'recall': 0.6860321384425216, 'f1': 0.6483644859813085, 'number': 809} | {'precision': 0.2976190476190476, 'recall': 0.21008403361344538, 'f1': 0.24630541871921183, 'number': 119} | {'precision': 0.6959349593495935, 'recall': 0.8037558685446009, 'f1': 0.7459694989106754, 'number': 1065} | 0.6477 | 0.7205 | 0.6822 | 0.7712 |
64
- | 0.5475 | 6.0 | 60 | 0.6925 | {'precision': 0.6453305351521511, 'recall': 0.7601977750309024, 'f1': 0.6980703745743474, 'number': 809} | {'precision': 0.2542372881355932, 'recall': 0.25210084033613445, 'f1': 0.25316455696202533, 'number': 119} | {'precision': 0.7153589315525877, 'recall': 0.8046948356807512, 'f1': 0.7574016791869199, 'number': 1065} | 0.6620 | 0.7536 | 0.7048 | 0.7865 |
65
- | 0.4876 | 7.0 | 70 | 0.6876 | {'precision': 0.649740932642487, 'recall': 0.7750309023485785, 'f1': 0.7068771138669674, 'number': 809} | {'precision': 0.26732673267326734, 'recall': 0.226890756302521, 'f1': 0.24545454545454548, 'number': 119} | {'precision': 0.7449249779346867, 'recall': 0.7924882629107981, 'f1': 0.7679708826205641, 'number': 1065} | 0.6812 | 0.7516 | 0.7147 | 0.7952 |
66
- | 0.4438 | 8.0 | 80 | 0.6672 | {'precision': 0.6842684268426843, 'recall': 0.7688504326328801, 'f1': 0.7240977881257276, 'number': 809} | {'precision': 0.26717557251908397, 'recall': 0.29411764705882354, 'f1': 0.28, 'number': 119} | {'precision': 0.7534602076124568, 'recall': 0.8178403755868544, 'f1': 0.7843313822602431, 'number': 1065} | 0.6958 | 0.7667 | 0.7295 | 0.8040 |
67
- | 0.3708 | 9.0 | 90 | 0.6684 | {'precision': 0.6832779623477298, 'recall': 0.7626699629171817, 'f1': 0.7207943925233644, 'number': 809} | {'precision': 0.2549019607843137, 'recall': 0.3277310924369748, 'f1': 0.286764705882353, 'number': 119} | {'precision': 0.7547660311958405, 'recall': 0.8178403755868544, 'f1': 0.7850383055430372, 'number': 1065} | 0.6910 | 0.7662 | 0.7266 | 0.8003 |
68
- | 0.3433 | 10.0 | 100 | 0.6779 | {'precision': 0.6833514689880305, 'recall': 0.7762669962917181, 'f1': 0.7268518518518517, 'number': 809} | {'precision': 0.29411764705882354, 'recall': 0.29411764705882354, 'f1': 0.29411764705882354, 'number': 119} | {'precision': 0.7772887323943662, 'recall': 0.8291079812206573, 'f1': 0.8023625624716039, 'number': 1065} | 0.7111 | 0.7757 | 0.7420 | 0.8084 |
69
- | 0.3173 | 11.0 | 110 | 0.6856 | {'precision': 0.6939890710382514, 'recall': 0.7849196538936959, 'f1': 0.7366589327146172, 'number': 809} | {'precision': 0.3089430894308943, 'recall': 0.31932773109243695, 'f1': 0.3140495867768595, 'number': 119} | {'precision': 0.7876895628902766, 'recall': 0.8291079812206573, 'f1': 0.807868252516011, 'number': 1065} | 0.7207 | 0.7807 | 0.7495 | 0.8103 |
70
- | 0.2951 | 12.0 | 120 | 0.6854 | {'precision': 0.6961883408071748, 'recall': 0.7676143386897404, 'f1': 0.7301587301587301, 'number': 809} | {'precision': 0.2986111111111111, 'recall': 0.36134453781512604, 'f1': 0.32699619771863114, 'number': 119} | {'precision': 0.7908438061041293, 'recall': 0.8272300469483568, 'f1': 0.8086278109224414, 'number': 1065} | 0.7186 | 0.7752 | 0.7458 | 0.8061 |
71
- | 0.2819 | 13.0 | 130 | 0.6966 | {'precision': 0.6995661605206074, 'recall': 0.7972805933250927, 'f1': 0.7452339688041594, 'number': 809} | {'precision': 0.3, 'recall': 0.35294117647058826, 'f1': 0.3243243243243243, 'number': 119} | {'precision': 0.7793594306049823, 'recall': 0.8225352112676056, 'f1': 0.8003654636820466, 'number': 1065} | 0.7150 | 0.7842 | 0.7480 | 0.8056 |
72
- | 0.2653 | 14.0 | 140 | 0.7000 | {'precision': 0.6970033296337403, 'recall': 0.7762669962917181, 'f1': 0.7345029239766083, 'number': 809} | {'precision': 0.30714285714285716, 'recall': 0.36134453781512604, 'f1': 0.33204633204633205, 'number': 119} | {'precision': 0.7908025247971145, 'recall': 0.8234741784037559, 'f1': 0.8068077276908925, 'number': 1065} | 0.72 | 0.7767 | 0.7473 | 0.8057 |
73
- | 0.2662 | 15.0 | 150 | 0.7001 | {'precision': 0.7016574585635359, 'recall': 0.7849196538936959, 'f1': 0.7409568261376897, 'number': 809} | {'precision': 0.3115942028985507, 'recall': 0.36134453781512604, 'f1': 0.3346303501945525, 'number': 119} | {'precision': 0.7809439002671416, 'recall': 0.8234741784037559, 'f1': 0.8016453382084096, 'number': 1065} | 0.7179 | 0.7802 | 0.7478 | 0.8048 |
74
 
75
 
76
  ### 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: 0.7065
21
+ - Answer: {'precision': 0.7120708748615725, 'recall': 0.7948084054388134, 'f1': 0.7511682242990654, 'number': 809}
22
+ - Header: {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119}
23
+ - Question: {'precision': 0.7776809067131648, 'recall': 0.8375586854460094, 'f1': 0.8065099457504521, 'number': 1065}
24
+ - Overall Precision: 0.7238
25
+ - Overall Recall: 0.7903
26
+ - Overall F1: 0.7556
27
+ - Overall Accuracy: 0.7999
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 |
58
+ |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
59
+ | 1.7682 | 1.0 | 10 | 1.5613 | {'precision': 0.029469548133595286, 'recall': 0.037082818294190356, 'f1': 0.03284072249589491, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.14761904761904762, 'recall': 0.14553990610328638, 'f1': 0.14657210401891252, 'number': 1065} | 0.0895 | 0.0928 | 0.0911 | 0.3814 |
60
+ | 1.4106 | 2.0 | 20 | 1.2321 | {'precision': 0.1530120481927711, 'recall': 0.15698393077873918, 'f1': 0.1549725442342892, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4595959595959596, 'recall': 0.5981220657276995, 'f1': 0.5197878416972664, 'number': 1065} | 0.3448 | 0.3833 | 0.3630 | 0.5884 |
61
+ | 1.0722 | 3.0 | 30 | 0.9239 | {'precision': 0.45864661654135336, 'recall': 0.5278121137206427, 'f1': 0.4908045977011494, 'number': 809} | {'precision': 0.034482758620689655, 'recall': 0.008403361344537815, 'f1': 0.013513513513513513, 'number': 119} | {'precision': 0.6172006745362564, 'recall': 0.6873239436619718, 'f1': 0.6503776099511329, 'number': 1065} | 0.5405 | 0.5820 | 0.5605 | 0.7059 |
62
+ | 0.813 | 4.0 | 40 | 0.7588 | {'precision': 0.6421404682274248, 'recall': 0.7119901112484549, 'f1': 0.675263774912075, 'number': 809} | {'precision': 0.17543859649122806, 'recall': 0.08403361344537816, 'f1': 0.11363636363636363, 'number': 119} | {'precision': 0.6691236691236692, 'recall': 0.7671361502347418, 'f1': 0.7147856517935257, 'number': 1065} | 0.6451 | 0.7040 | 0.6732 | 0.7672 |
63
+ | 0.6435 | 5.0 | 50 | 0.6978 | {'precision': 0.6810631229235881, 'recall': 0.7601977750309024, 'f1': 0.7184579439252335, 'number': 809} | {'precision': 0.33766233766233766, 'recall': 0.2184873949579832, 'f1': 0.2653061224489796, 'number': 119} | {'precision': 0.6998394863563403, 'recall': 0.8187793427230047, 'f1': 0.7546516659454782, 'number': 1065} | 0.6797 | 0.7592 | 0.7172 | 0.7834 |
64
+ | 0.5577 | 6.0 | 60 | 0.6803 | {'precision': 0.6797385620915033, 'recall': 0.7713226205191595, 'f1': 0.7226404169079328, 'number': 809} | {'precision': 0.31645569620253167, 'recall': 0.21008403361344538, 'f1': 0.25252525252525254, 'number': 119} | {'precision': 0.7280334728033473, 'recall': 0.8169014084507042, 'f1': 0.7699115044247787, 'number': 1065} | 0.6930 | 0.7622 | 0.7259 | 0.7920 |
65
+ | 0.4844 | 7.0 | 70 | 0.6678 | {'precision': 0.6868798235942668, 'recall': 0.7700865265760197, 'f1': 0.726107226107226, 'number': 809} | {'precision': 0.3157894736842105, 'recall': 0.25210084033613445, 'f1': 0.2803738317757009, 'number': 119} | {'precision': 0.7511032656663724, 'recall': 0.7990610328638498, 'f1': 0.7743403093721565, 'number': 1065} | 0.7044 | 0.7546 | 0.7287 | 0.7968 |
66
+ | 0.4396 | 8.0 | 80 | 0.6579 | {'precision': 0.7017543859649122, 'recall': 0.7911001236093943, 'f1': 0.7437536316095292, 'number': 809} | {'precision': 0.30927835051546393, 'recall': 0.25210084033613445, 'f1': 0.2777777777777778, 'number': 119} | {'precision': 0.7595486111111112, 'recall': 0.8215962441314554, 'f1': 0.7893549842129003, 'number': 1065} | 0.7149 | 0.7752 | 0.7439 | 0.8041 |
67
+ | 0.3704 | 9.0 | 90 | 0.6669 | {'precision': 0.7118834080717489, 'recall': 0.7849196538936959, 'f1': 0.7466196355085244, 'number': 809} | {'precision': 0.3064516129032258, 'recall': 0.31932773109243695, 'f1': 0.31275720164609055, 'number': 119} | {'precision': 0.7565217391304347, 'recall': 0.8169014084507042, 'f1': 0.7855530474040633, 'number': 1065} | 0.7124 | 0.7742 | 0.7420 | 0.7986 |
68
+ | 0.3414 | 10.0 | 100 | 0.6865 | {'precision': 0.7039911308203991, 'recall': 0.7849196538936959, 'f1': 0.7422559906487435, 'number': 809} | {'precision': 0.308411214953271, 'recall': 0.2773109243697479, 'f1': 0.29203539823008845, 'number': 119} | {'precision': 0.7589743589743589, 'recall': 0.8338028169014085, 'f1': 0.7946308724832215, 'number': 1065} | 0.7141 | 0.7807 | 0.7459 | 0.7986 |
69
+ | 0.3189 | 11.0 | 110 | 0.6830 | {'precision': 0.7093922651933702, 'recall': 0.7935723114956736, 'f1': 0.7491248541423571, 'number': 809} | {'precision': 0.2972972972972973, 'recall': 0.2773109243697479, 'f1': 0.28695652173913044, 'number': 119} | {'precision': 0.7759078830823738, 'recall': 0.8225352112676056, 'f1': 0.7985414767547857, 'number': 1065} | 0.7231 | 0.7782 | 0.7496 | 0.8025 |
70
+ | 0.2921 | 12.0 | 120 | 0.6976 | {'precision': 0.7194163860830527, 'recall': 0.792336217552534, 'f1': 0.7541176470588233, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.36134453781512604, 'f1': 0.34677419354838707, 'number': 119} | {'precision': 0.7757417102966842, 'recall': 0.8347417840375587, 'f1': 0.8041610131162371, 'number': 1065} | 0.7262 | 0.7893 | 0.7564 | 0.7987 |
71
+ | 0.2775 | 13.0 | 130 | 0.6988 | {'precision': 0.7120708748615725, 'recall': 0.7948084054388134, 'f1': 0.7511682242990654, 'number': 809} | {'precision': 0.34234234234234234, 'recall': 0.31932773109243695, 'f1': 0.33043478260869563, 'number': 119} | {'precision': 0.7779735682819383, 'recall': 0.8291079812206573, 'f1': 0.8027272727272727, 'number': 1065} | 0.7278 | 0.7847 | 0.7552 | 0.8008 |
72
+ | 0.2632 | 14.0 | 140 | 0.7080 | {'precision': 0.7189249720044792, 'recall': 0.7935723114956736, 'f1': 0.7544065804935369, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3445378151260504, 'f1': 0.33884297520661155, 'number': 119} | {'precision': 0.7769028871391076, 'recall': 0.8338028169014085, 'f1': 0.8043478260869565, 'number': 1065} | 0.7277 | 0.7883 | 0.7567 | 0.8005 |
73
+ | 0.2666 | 15.0 | 150 | 0.7065 | {'precision': 0.7120708748615725, 'recall': 0.7948084054388134, 'f1': 0.7511682242990654, 'number': 809} | {'precision': 0.31746031746031744, 'recall': 0.33613445378151263, 'f1': 0.32653061224489793, 'number': 119} | {'precision': 0.7776809067131648, 'recall': 0.8375586854460094, 'f1': 0.8065099457504521, 'number': 1065} | 0.7238 | 0.7903 | 0.7556 | 0.7999 |
74
 
75
 
76
  ### Framework versions
logs/events.out.tfevents.1703159383.DESKTOP-HA84SVN.4393.0 CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:3109cfd0563bc22863812f195d9e675b6787c6ee581fb2fab999b88657010cef
3
- size 14375
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:98ae78f4bc623d30a8d7e3a7b590a8b95bb449a22d16fe1a49050019be702461
3
+ size 14729
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:d38418a9cd0c3e0e350db4f4286788419dda04069f6cd8a0314408eb0a86ba95
3
  size 450558212
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a14a523164886dc3c5852d14cef949b23efda0f50afde0030d06e6845255c0c2
3
  size 450558212