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dodogigi
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layoutlm-funsd

Token Classification
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layoutlm
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  1. README.md +24 -24
  2. logs/events.out.tfevents.1664936090.AiD-DLS-1.58602.0 +2 -2
  3. pytorch_model.bin +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 the funsd dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1386
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- - Answer: {'precision': 0.30710659898477155, 'recall': 0.29913473423980225, 'f1': 0.3030682529743269, 'number': 809}
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  - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
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- - Question: {'precision': 0.5028901734104047, 'recall': 0.571830985915493, 'f1': 0.5351493848857645, 'number': 1065}
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- - Overall Precision: 0.4247
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- - Overall Recall: 0.4270
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- - Overall F1: 0.4258
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- - Overall Accuracy: 0.6220
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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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- | 1.9493 | 1.0 | 2 | 1.8316 | {'precision': 0.04491161012900143, 'recall': 0.1161928306551298, 'f1': 0.0647829083390765, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.04848966613672496, 'recall': 0.11455399061032864, 'f1': 0.06813739179000279, 'number': 1065} | 0.0459 | 0.1084 | 0.0645 | 0.2414 |
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- | 1.8128 | 2.0 | 4 | 1.7172 | {'precision': 0.043029259896729774, 'recall': 0.09270704573547589, 'f1': 0.05877742946708463, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.06945337620578779, 'recall': 0.10140845070422536, 'f1': 0.08244274809160307, 'number': 1065} | 0.0554 | 0.0918 | 0.0691 | 0.3412 |
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- | 1.7055 | 3.0 | 6 | 1.6336 | {'precision': 0.026881720430107527, 'recall': 0.037082818294190356, 'f1': 0.03116883116883117, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.13069544364508393, 'recall': 0.10234741784037558, 'f1': 0.11479726171669301, 'number': 1065} | 0.0713 | 0.0697 | 0.0705 | 0.3750 |
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- | 1.618 | 4.0 | 8 | 1.5747 | {'precision': 0.028535980148883373, 'recall': 0.02843016069221261, 'f1': 0.02848297213622291, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2175925925925926, 'recall': 0.1323943661971831, 'f1': 0.1646234676007005, 'number': 1065} | 0.1128 | 0.0823 | 0.0952 | 0.3794 |
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- | 1.5703 | 5.0 | 10 | 1.5192 | {'precision': 0.03393939393939394, 'recall': 0.034610630407911, 'f1': 0.03427172582619339, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2924187725631769, 'recall': 0.22816901408450704, 'f1': 0.25632911392405067, 'number': 1065} | 0.1636 | 0.1360 | 0.1485 | 0.4119 |
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- | 1.499 | 6.0 | 12 | 1.4574 | {'precision': 0.05172413793103448, 'recall': 0.05562422744128554, 'f1': 0.053603335318642045, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3345132743362832, 'recall': 0.35492957746478876, 'f1': 0.34441913439635535, 'number': 1065} | 0.2115 | 0.2122 | 0.2119 | 0.4623 |
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- | 1.4485 | 7.0 | 14 | 1.3976 | {'precision': 0.06690561529271206, 'recall': 0.069221260815822, 'f1': 0.06804374240583232, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.35199386503067487, 'recall': 0.4309859154929577, 'f1': 0.3875052764879696, 'number': 1065} | 0.2405 | 0.2584 | 0.2492 | 0.5090 |
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- | 1.4014 | 8.0 | 16 | 1.3413 | {'precision': 0.10366624525916561, 'recall': 0.10135970333745364, 'f1': 0.1025, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3722576079263977, 'recall': 0.49389671361502346, 'f1': 0.4245359160613398, 'number': 1065} | 0.2759 | 0.3051 | 0.2897 | 0.5445 |
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- | 1.3465 | 9.0 | 18 | 1.2908 | {'precision': 0.14323962516733602, 'recall': 0.13226205191594562, 'f1': 0.13753213367609254, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.40387374461979914, 'recall': 0.5286384976525822, 'f1': 0.45790971939812936, 'number': 1065} | 0.3129 | 0.3362 | 0.3241 | 0.5679 |
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- | 1.2943 | 10.0 | 20 | 1.2491 | {'precision': 0.18072289156626506, 'recall': 0.1668726823238566, 'f1': 0.17352185089974292, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4293233082706767, 'recall': 0.536150234741784, 'f1': 0.47682672233820456, 'number': 1065} | 0.3399 | 0.3542 | 0.3469 | 0.5816 |
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- | 1.2334 | 11.0 | 22 | 1.2138 | {'precision': 0.21903520208604954, 'recall': 0.207663782447466, 'f1': 0.2131979695431472, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.46141732283464565, 'recall': 0.5502347417840375, 'f1': 0.5019271948608137, 'number': 1065} | 0.3702 | 0.3783 | 0.3742 | 0.5934 |
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- | 1.2339 | 12.0 | 24 | 1.1840 | {'precision': 0.24804177545691905, 'recall': 0.23485784919653893, 'f1': 0.24126984126984127, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.48806584362139915, 'recall': 0.5568075117370892, 'f1': 0.5201754385964912, 'number': 1065} | 0.3945 | 0.3929 | 0.3937 | 0.6030 |
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- | 1.1924 | 13.0 | 26 | 1.1607 | {'precision': 0.2782051282051282, 'recall': 0.26823238566131025, 'f1': 0.27312775330396477, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.49793899422918386, 'recall': 0.5671361502347417, 'f1': 0.5302897278314311, 'number': 1065} | 0.4111 | 0.4119 | 0.4115 | 0.6143 |
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- | 1.1666 | 14.0 | 28 | 1.1454 | {'precision': 0.2970550576184379, 'recall': 0.2867737948084054, 'f1': 0.2918238993710692, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5008250825082509, 'recall': 0.5699530516431925, 'f1': 0.5331576635924462, 'number': 1065} | 0.4199 | 0.4210 | 0.4204 | 0.6193 |
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- | 1.1426 | 15.0 | 30 | 1.1386 | {'precision': 0.30710659898477155, 'recall': 0.29913473423980225, 'f1': 0.3030682529743269, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5028901734104047, 'recall': 0.571830985915493, 'f1': 0.5351493848857645, 'number': 1065} | 0.4247 | 0.4270 | 0.4258 | 0.6220 |
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  ### Framework versions
 
15
 
16
  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
17
  It achieves the following results on the evaluation set:
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+ - Loss: 1.1641
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+ - Answer: {'precision': 0.2191977077363897, 'recall': 0.18912237330037082, 'f1': 0.2030524220305242, 'number': 809}
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  - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
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+ - Question: {'precision': 0.498793242156074, 'recall': 0.5821596244131455, 'f1': 0.537261698440208, 'number': 1065}
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+ - Overall Precision: 0.3978
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+ - Overall Recall: 0.3879
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+ - Overall F1: 0.3928
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+ - Overall Accuracy: 0.6124
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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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+ | 1.9962 | 1.0 | 2 | 1.8687 | {'precision': 0.03483768804433888, 'recall': 0.1631644004944376, 'f1': 0.05741626794258374, 'number': 809} | {'precision': 0.003629764065335753, 'recall': 0.01680672268907563, 'f1': 0.0059701492537313425, 'number': 119} | {'precision': 0.0681198910081744, 'recall': 0.046948356807511735, 'f1': 0.05558643690939411, 'number': 1065} | 0.0363 | 0.0923 | 0.0521 | 0.2218 |
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+ | 1.8538 | 2.0 | 4 | 1.7624 | {'precision': 0.024963994239078253, 'recall': 0.06427688504326329, 'f1': 0.0359612724757953, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.11979166666666667, 'recall': 0.0863849765258216, 'f1': 0.10038188761593017, 'number': 1065} | 0.0489 | 0.0723 | 0.0583 | 0.3158 |
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+ | 1.7559 | 3.0 | 6 | 1.6781 | {'precision': 0.02198768689533861, 'recall': 0.030902348578491966, 'f1': 0.025693730729701957, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.14264264264264265, 'recall': 0.0892018779342723, 'f1': 0.1097631426920855, 'number': 1065} | 0.0662 | 0.0602 | 0.0631 | 0.3594 |
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+ | 1.6555 | 4.0 | 8 | 1.6110 | {'precision': 0.02254791431792559, 'recall': 0.024721878862793572, 'f1': 0.023584905660377357, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.21360544217687075, 'recall': 0.14741784037558686, 'f1': 0.17444444444444446, 'number': 1065} | 0.1091 | 0.0888 | 0.0979 | 0.3716 |
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+ | 1.5984 | 5.0 | 10 | 1.5498 | {'precision': 0.025933609958506226, 'recall': 0.030902348578491966, 'f1': 0.028200789622109423, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.25252525252525254, 'recall': 0.2347417840375587, 'f1': 0.24330900243309003, 'number': 1065} | 0.1407 | 0.1380 | 0.1393 | 0.4088 |
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+ | 1.5398 | 6.0 | 12 | 1.4863 | {'precision': 0.0392156862745098, 'recall': 0.049443757725587144, 'f1': 0.04373974849644614, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.30129240710823907, 'recall': 0.3502347417840376, 'f1': 0.3239253148067737, 'number': 1065} | 0.1829 | 0.2072 | 0.1943 | 0.4586 |
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+ | 1.4917 | 7.0 | 14 | 1.4250 | {'precision': 0.056910569105691054, 'recall': 0.069221260815822, 'f1': 0.06246514221974344, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.33839150227617604, 'recall': 0.4187793427230047, 'f1': 0.37431808644565673, 'number': 1065} | 0.2181 | 0.2519 | 0.2338 | 0.4970 |
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+ | 1.4358 | 8.0 | 16 | 1.3673 | {'precision': 0.07244785949506037, 'recall': 0.0815822002472188, 'f1': 0.07674418604651162, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.37908011869436203, 'recall': 0.47981220657276996, 'f1': 0.42353916286779947, 'number': 1065} | 0.2554 | 0.2895 | 0.2714 | 0.5278 |
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+ | 1.3702 | 9.0 | 18 | 1.3178 | {'precision': 0.08588957055214724, 'recall': 0.0865265760197775, 'f1': 0.08620689655172414, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4203338391502276, 'recall': 0.5201877934272301, 'f1': 0.46496013428451527, 'number': 1065} | 0.2925 | 0.3131 | 0.3025 | 0.5501 |
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+ | 1.3251 | 10.0 | 20 | 1.2767 | {'precision': 0.11455525606469003, 'recall': 0.10506798516687268, 'f1': 0.1096067053513862, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.44755244755244755, 'recall': 0.5408450704225352, 'f1': 0.489795918367347, 'number': 1065} | 0.3258 | 0.3317 | 0.3287 | 0.5662 |
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+ | 1.2664 | 11.0 | 22 | 1.2394 | {'precision': 0.13525179856115108, 'recall': 0.1161928306551298, 'f1': 0.125, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4676034348165496, 'recall': 0.5624413145539906, 'f1': 0.5106564364876385, 'number': 1065} | 0.3507 | 0.3477 | 0.3492 | 0.5804 |
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+ | 1.262 | 12.0 | 24 | 1.2080 | {'precision': 0.17008797653958943, 'recall': 0.1433868974042027, 'f1': 0.15560026827632462, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4767899291896145, 'recall': 0.5690140845070423, 'f1': 0.5188356164383563, 'number': 1065} | 0.3697 | 0.3623 | 0.3659 | 0.5941 |
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+ | 1.2172 | 13.0 | 26 | 1.1847 | {'precision': 0.19476744186046513, 'recall': 0.16563658838071693, 'f1': 0.17902471609886442, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.48330683624801274, 'recall': 0.5708920187793427, 'f1': 0.5234610417563496, 'number': 1065} | 0.3811 | 0.3723 | 0.3766 | 0.6026 |
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+ | 1.2014 | 14.0 | 28 | 1.1703 | {'precision': 0.2178932178932179, 'recall': 0.18665018541409148, 'f1': 0.20106524633821574, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.49437299035369775, 'recall': 0.5774647887323944, 'f1': 0.5326981377219576, 'number': 1065} | 0.3950 | 0.3843 | 0.3896 | 0.6100 |
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+ | 1.1677 | 15.0 | 30 | 1.1641 | {'precision': 0.2191977077363897, 'recall': 0.18912237330037082, 'f1': 0.2030524220305242, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.498793242156074, 'recall': 0.5821596244131455, 'f1': 0.537261698440208, 'number': 1065} | 0.3978 | 0.3879 | 0.3928 | 0.6124 |
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  ### Framework versions
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@@ -1,3 +1,3 @@
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