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layoutlm-funsd

Token Classification
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layoutlm
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  1. README.md +25 -25
  2. logs/events.out.tfevents.1717259827.d213d09c0354.3673.0 +2 -2
  3. model.safetensors +1 -1
  4. tokenizer.json +16 -2
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: 0.7055
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- - Answer: {'precision': 0.7035830618892508, 'recall': 0.8009888751545118, 'f1': 0.7491329479768787, 'number': 809}
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- - Header: {'precision': 0.34146341463414637, 'recall': 0.35294117647058826, 'f1': 0.34710743801652894, 'number': 119}
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- - Question: {'precision': 0.7775816416593115, 'recall': 0.8272300469483568, 'f1': 0.8016378525932666, 'number': 1065}
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- - Overall Precision: 0.7216
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- - Overall Recall: 0.7883
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- - Overall F1: 0.7535
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- - Overall Accuracy: 0.8028
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  ## Model description
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@@ -54,23 +54,23 @@ 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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- | 1.8301 | 1.0 | 10 | 1.5849 | {'precision': 0.008086253369272238, 'recall': 0.007416563658838072, 'f1': 0.007736943907156674, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.22358346094946402, 'recall': 0.13708920187793427, 'f1': 0.16996507566938301, 'number': 1065} | 0.1090 | 0.0763 | 0.0897 | 0.3514 |
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- | 1.4704 | 2.0 | 20 | 1.2710 | {'precision': 0.2843881856540084, 'recall': 0.41656365883807167, 'f1': 0.3380140421263791, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3906474820143885, 'recall': 0.5098591549295775, 'f1': 0.44236252545824845, 'number': 1065} | 0.3408 | 0.4415 | 0.3847 | 0.6020 |
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- | 1.1259 | 3.0 | 30 | 0.9451 | {'precision': 0.47373447946513847, 'recall': 0.6131025957972805, 'f1': 0.5344827586206896, 'number': 809} | {'precision': 0.0625, 'recall': 0.025210084033613446, 'f1': 0.035928143712574856, 'number': 119} | {'precision': 0.5223654283548143, 'recall': 0.6469483568075117, 'f1': 0.5780201342281879, 'number': 1065} | 0.4921 | 0.5961 | 0.5391 | 0.7000 |
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- | 0.8549 | 4.0 | 40 | 0.7891 | {'precision': 0.5652985074626866, 'recall': 0.7490729295426453, 'f1': 0.6443381180223287, 'number': 809} | {'precision': 0.20833333333333334, 'recall': 0.12605042016806722, 'f1': 0.15706806282722513, 'number': 119} | {'precision': 0.6485013623978202, 'recall': 0.6704225352112676, 'f1': 0.6592797783933518, 'number': 1065} | 0.5947 | 0.6698 | 0.6300 | 0.7562 |
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- | 0.6872 | 5.0 | 50 | 0.7203 | {'precision': 0.6393617021276595, 'recall': 0.7428924598269468, 'f1': 0.6872498570611778, 'number': 809} | {'precision': 0.358974358974359, 'recall': 0.23529411764705882, 'f1': 0.28426395939086296, 'number': 119} | {'precision': 0.6650563607085346, 'recall': 0.7755868544600939, 'f1': 0.716081491114001, 'number': 1065} | 0.6438 | 0.7301 | 0.6842 | 0.7798 |
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- | 0.5872 | 6.0 | 60 | 0.6889 | {'precision': 0.6236559139784946, 'recall': 0.788627935723115, 'f1': 0.6965065502183407, 'number': 809} | {'precision': 0.35802469135802467, 'recall': 0.24369747899159663, 'f1': 0.29000000000000004, 'number': 119} | {'precision': 0.7190517998244074, 'recall': 0.7690140845070422, 'f1': 0.7431941923774955, 'number': 1065} | 0.6625 | 0.7456 | 0.7016 | 0.7797 |
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- | 0.5065 | 7.0 | 70 | 0.6618 | {'precision': 0.681283422459893, 'recall': 0.7873918417799752, 'f1': 0.7305045871559632, 'number': 809} | {'precision': 0.336734693877551, 'recall': 0.2773109243697479, 'f1': 0.30414746543778803, 'number': 119} | {'precision': 0.748471615720524, 'recall': 0.8046948356807512, 'f1': 0.7755656108597285, 'number': 1065} | 0.7011 | 0.7662 | 0.7322 | 0.7934 |
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- | 0.4527 | 8.0 | 80 | 0.6639 | {'precision': 0.671161825726141, 'recall': 0.799752781211372, 'f1': 0.7298364354201917, 'number': 809} | {'precision': 0.3170731707317073, 'recall': 0.3277310924369748, 'f1': 0.32231404958677684, 'number': 119} | {'precision': 0.7473867595818815, 'recall': 0.8056338028169014, 'f1': 0.7754179846362403, 'number': 1065} | 0.6908 | 0.7747 | 0.7304 | 0.7955 |
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- | 0.3952 | 9.0 | 90 | 0.6666 | {'precision': 0.686358754027927, 'recall': 0.7898640296662547, 'f1': 0.7344827586206897, 'number': 809} | {'precision': 0.3523809523809524, 'recall': 0.31092436974789917, 'f1': 0.33035714285714285, 'number': 119} | {'precision': 0.7519247219846023, 'recall': 0.8253521126760563, 'f1': 0.7869292748433303, 'number': 1065} | 0.7052 | 0.7802 | 0.7408 | 0.7969 |
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- | 0.3863 | 10.0 | 100 | 0.6806 | {'precision': 0.6849894291754757, 'recall': 0.8009888751545118, 'f1': 0.7384615384615385, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.31932773109243695, 'f1': 0.3261802575107296, 'number': 119} | {'precision': 0.7670157068062827, 'recall': 0.8253521126760563, 'f1': 0.7951153324287653, 'number': 1065} | 0.7094 | 0.7852 | 0.7454 | 0.7985 |
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- | 0.3307 | 11.0 | 110 | 0.6859 | {'precision': 0.6938775510204082, 'recall': 0.7985166872682324, 'f1': 0.7425287356321839, 'number': 809} | {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119} | {'precision': 0.764402407566638, 'recall': 0.8347417840375587, 'f1': 0.7980251346499103, 'number': 1065} | 0.7118 | 0.7908 | 0.7492 | 0.8004 |
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- | 0.3126 | 12.0 | 120 | 0.6896 | {'precision': 0.697198275862069, 'recall': 0.799752781211372, 'f1': 0.7449625791594704, 'number': 809} | {'precision': 0.36283185840707965, 'recall': 0.3445378151260504, 'f1': 0.35344827586206895, 'number': 119} | {'precision': 0.7788632326820604, 'recall': 0.8234741784037559, 'f1': 0.8005476951163851, 'number': 1065} | 0.7222 | 0.7852 | 0.7524 | 0.8012 |
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- | 0.2979 | 13.0 | 130 | 0.6997 | {'precision': 0.6992399565689468, 'recall': 0.796044499381953, 'f1': 0.7445086705202313, 'number': 809} | {'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119} | {'precision': 0.7763157894736842, 'recall': 0.8309859154929577, 'f1': 0.802721088435374, 'number': 1065} | 0.7199 | 0.7878 | 0.7523 | 0.8007 |
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- | 0.2712 | 14.0 | 140 | 0.7039 | {'precision': 0.7083333333333334, 'recall': 0.7985166872682324, 'f1': 0.7507263219058687, 'number': 809} | {'precision': 0.336, 'recall': 0.35294117647058826, 'f1': 0.3442622950819672, 'number': 119} | {'precision': 0.7771929824561403, 'recall': 0.831924882629108, 'f1': 0.8036281179138323, 'number': 1065} | 0.7230 | 0.7898 | 0.7549 | 0.8028 |
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- | 0.2738 | 15.0 | 150 | 0.7055 | {'precision': 0.7035830618892508, 'recall': 0.8009888751545118, 'f1': 0.7491329479768787, 'number': 809} | {'precision': 0.34146341463414637, 'recall': 0.35294117647058826, 'f1': 0.34710743801652894, 'number': 119} | {'precision': 0.7775816416593115, 'recall': 0.8272300469483568, 'f1': 0.8016378525932666, 'number': 1065} | 0.7216 | 0.7883 | 0.7535 | 0.8028 |
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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:
20
+ - Loss: 0.6788
21
+ - Answer: {'precision': 0.7050592034445641, 'recall': 0.8096415327564895, 'f1': 0.7537399309551209, 'number': 809}
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+ - Header: {'precision': 0.3014705882352941, 'recall': 0.3445378151260504, 'f1': 0.3215686274509804, 'number': 119}
23
+ - Question: {'precision': 0.7795414462081128, 'recall': 0.8300469483568075, 'f1': 0.8040018190086403, 'number': 1065}
24
+ - Overall Precision: 0.7185
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+ - Overall Recall: 0.7928
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+ - Overall F1: 0.7538
27
+ - Overall Accuracy: 0.8119
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
+ |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 1.8004 | 1.0 | 10 | 1.6077 | {'precision': 0.013595166163141994, 'recall': 0.011124845488257108, 'f1': 0.012236573759347382, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.24475524475524477, 'recall': 0.13145539906103287, 'f1': 0.1710445937690898, 'number': 1065} | 0.1207 | 0.0748 | 0.0923 | 0.3510 |
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+ | 1.4746 | 2.0 | 20 | 1.2888 | {'precision': 0.1705521472392638, 'recall': 0.17181705809641531, 'f1': 0.1711822660098522, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4061776061776062, 'recall': 0.49389671361502346, 'f1': 0.4457627118644068, 'number': 1065} | 0.3152 | 0.3337 | 0.3242 | 0.5672 |
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+ | 1.1291 | 3.0 | 30 | 0.9811 | {'precision': 0.5127931769722814, 'recall': 0.5945611866501854, 'f1': 0.5506582713222669, 'number': 809} | {'precision': 0.027777777777777776, 'recall': 0.008403361344537815, 'f1': 0.012903225806451613, 'number': 119} | {'precision': 0.5457317073170732, 'recall': 0.672300469483568, 'f1': 0.6024400504838031, 'number': 1065} | 0.5241 | 0.6011 | 0.5599 | 0.7097 |
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+ | 0.86 | 4.0 | 40 | 0.8044 | {'precision': 0.5934489402697495, 'recall': 0.761433868974042, 'f1': 0.6670276123443422, 'number': 809} | {'precision': 0.18333333333333332, 'recall': 0.09243697478991597, 'f1': 0.12290502793296088, 'number': 119} | {'precision': 0.6554694229112834, 'recall': 0.7145539906103286, 'f1': 0.683737646001797, 'number': 1065} | 0.6144 | 0.6964 | 0.6529 | 0.7534 |
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+ | 0.6873 | 5.0 | 50 | 0.7263 | {'precision': 0.6666666666666666, 'recall': 0.7416563658838071, 'f1': 0.7021650087770627, 'number': 809} | {'precision': 0.2777777777777778, 'recall': 0.21008403361344538, 'f1': 0.23923444976076552, 'number': 119} | {'precision': 0.6648648648648648, 'recall': 0.8084507042253521, 'f1': 0.7296610169491525, 'number': 1065} | 0.6503 | 0.7456 | 0.6947 | 0.7838 |
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+ | 0.5806 | 6.0 | 60 | 0.6815 | {'precision': 0.6598569969356486, 'recall': 0.7985166872682324, 'f1': 0.7225950782997763, 'number': 809} | {'precision': 0.25806451612903225, 'recall': 0.20168067226890757, 'f1': 0.22641509433962265, 'number': 119} | {'precision': 0.7074829931972789, 'recall': 0.7812206572769953, 'f1': 0.7425256581883088, 'number': 1065} | 0.6681 | 0.7536 | 0.7083 | 0.7901 |
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+ | 0.5036 | 7.0 | 70 | 0.6550 | {'precision': 0.6694473409801877, 'recall': 0.7935723114956736, 'f1': 0.7262443438914026, 'number': 809} | {'precision': 0.23076923076923078, 'recall': 0.226890756302521, 'f1': 0.22881355932203387, 'number': 119} | {'precision': 0.7226962457337884, 'recall': 0.7953051643192488, 'f1': 0.7572641931157801, 'number': 1065} | 0.6744 | 0.7607 | 0.7149 | 0.7975 |
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+ | 0.4447 | 8.0 | 80 | 0.6628 | {'precision': 0.67570385818561, 'recall': 0.8009888751545118, 'f1': 0.7330316742081447, 'number': 809} | {'precision': 0.24390243902439024, 'recall': 0.25210084033613445, 'f1': 0.24793388429752067, 'number': 119} | {'precision': 0.7413494809688581, 'recall': 0.8046948356807512, 'f1': 0.7717244484466456, 'number': 1065} | 0.6859 | 0.7702 | 0.7256 | 0.7973 |
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+ | 0.392 | 9.0 | 90 | 0.6465 | {'precision': 0.6974248927038627, 'recall': 0.8034610630407911, 'f1': 0.7466973004020677, 'number': 809} | {'precision': 0.31451612903225806, 'recall': 0.3277310924369748, 'f1': 0.32098765432098764, 'number': 119} | {'precision': 0.7433476394849785, 'recall': 0.8131455399061033, 'f1': 0.7766816143497759, 'number': 1065} | 0.7001 | 0.7802 | 0.7380 | 0.8060 |
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+ | 0.3844 | 10.0 | 100 | 0.6466 | {'precision': 0.6900212314225053, 'recall': 0.8034610630407911, 'f1': 0.7424328954882924, 'number': 809} | {'precision': 0.28440366972477066, 'recall': 0.2605042016806723, 'f1': 0.2719298245614035, 'number': 119} | {'precision': 0.7697022767075307, 'recall': 0.8253521126760563, 'f1': 0.7965564114182148, 'number': 1065} | 0.7114 | 0.7827 | 0.7453 | 0.8170 |
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+ | 0.323 | 11.0 | 110 | 0.6688 | {'precision': 0.7047930283224401, 'recall': 0.799752781211372, 'f1': 0.7492762015055008, 'number': 809} | {'precision': 0.2808219178082192, 'recall': 0.3445378151260504, 'f1': 0.309433962264151, 'number': 119} | {'precision': 0.7660869565217391, 'recall': 0.8272300469483568, 'f1': 0.7954853273137698, 'number': 1065} | 0.7087 | 0.7873 | 0.7459 | 0.8081 |
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+ | 0.3034 | 12.0 | 120 | 0.6660 | {'precision': 0.7082429501084598, 'recall': 0.8071693448702101, 'f1': 0.754477180820335, 'number': 809} | {'precision': 0.3559322033898305, 'recall': 0.35294117647058826, 'f1': 0.35443037974683544, 'number': 119} | {'precision': 0.7871956717763751, 'recall': 0.819718309859155, 'f1': 0.8031278748850045, 'number': 1065} | 0.7296 | 0.7868 | 0.7571 | 0.8138 |
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+ | 0.2884 | 13.0 | 130 | 0.6788 | {'precision': 0.7159956474428727, 'recall': 0.8133498145859085, 'f1': 0.7615740740740741, 'number': 809} | {'precision': 0.328125, 'recall': 0.35294117647058826, 'f1': 0.340080971659919, 'number': 119} | {'precision': 0.7803365810451727, 'recall': 0.8272300469483568, 'f1': 0.8030993618960802, 'number': 1065} | 0.7266 | 0.7933 | 0.7585 | 0.8110 |
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+ | 0.2674 | 14.0 | 140 | 0.6781 | {'precision': 0.7114967462039046, 'recall': 0.8108776266996292, 'f1': 0.7579433853264009, 'number': 809} | {'precision': 0.30597014925373134, 'recall': 0.3445378151260504, 'f1': 0.3241106719367589, 'number': 119} | {'precision': 0.7848888888888889, 'recall': 0.8291079812206573, 'f1': 0.8063926940639269, 'number': 1065} | 0.7244 | 0.7928 | 0.7571 | 0.8133 |
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+ | 0.271 | 15.0 | 150 | 0.6788 | {'precision': 0.7050592034445641, 'recall': 0.8096415327564895, 'f1': 0.7537399309551209, 'number': 809} | {'precision': 0.3014705882352941, 'recall': 0.3445378151260504, 'f1': 0.3215686274509804, 'number': 119} | {'precision': 0.7795414462081128, 'recall': 0.8300469483568075, 'f1': 0.8040018190086403, 'number': 1065} | 0.7185 | 0.7928 | 0.7538 | 0.8119 |
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  ### Framework versions
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