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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.1691749507.56f85528113b.2378.0 +2 -2
  3. pytorch_model.bin +1 -1
README.md CHANGED
@@ -16,14 +16,14 @@ 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: 1.3215
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- - Answer: {'precision': 0.10096818810511757, 'recall': 0.09023485784919653, 'f1': 0.09530026109660573, 'number': 809}
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  - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
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- - Question: {'precision': 0.3980815347721823, 'recall': 0.4676056338028169, 'f1': 0.43005181347150256, 'number': 1065}
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- - Overall Precision: 0.2891
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- - Overall Recall: 0.2865
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- - Overall F1: 0.2878
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- - Overall Accuracy: 0.5339
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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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- | 1.9471 | 1.0 | 10 | 1.8844 | {'precision': 0.022006141248720572, 'recall': 0.05315203955500618, 'f1': 0.031125588128845458, 'number': 809} | {'precision': 0.00702576112412178, 'recall': 0.05042016806722689, 'f1': 0.012332990750256937, 'number': 119} | {'precision': 0.054583995760466346, 'recall': 0.09671361502347418, 'f1': 0.06978319783197831, 'number': 1065} | 0.0324 | 0.0763 | 0.0455 | 0.2491 |
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- | 1.8584 | 2.0 | 20 | 1.8099 | {'precision': 0.018408941485864562, 'recall': 0.034610630407911, 'f1': 0.024034334763948496, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.08241758241758242, 'recall': 0.11267605633802817, 'f1': 0.09520031733439112, 'number': 1065} | 0.0469 | 0.0743 | 0.0575 | 0.3139 |
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- | 1.7841 | 3.0 | 30 | 1.7444 | {'precision': 0.02190395956192081, 'recall': 0.032138442521631644, 'f1': 0.026052104208416832, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.10752688172043011, 'recall': 0.12206572769953052, 'f1': 0.11433597185576078, 'number': 1065} | 0.0645 | 0.0783 | 0.0707 | 0.3426 |
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- | 1.7255 | 4.0 | 40 | 1.6851 | {'precision': 0.026865671641791045, 'recall': 0.03337453646477132, 'f1': 0.029768467475192944, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.15547024952015356, 'recall': 0.15211267605633802, 'f1': 0.1537731371618415, 'number': 1065} | 0.0922 | 0.0948 | 0.0935 | 0.3647 |
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- | 1.6607 | 5.0 | 50 | 1.6287 | {'precision': 0.036458333333333336, 'recall': 0.04326328800988875, 'f1': 0.03957037874505371, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2018348623853211, 'recall': 0.20657276995305165, 'f1': 0.20417633410672859, 'number': 1065} | 0.1244 | 0.1279 | 0.1261 | 0.3943 |
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- | 1.6127 | 6.0 | 60 | 1.5738 | {'precision': 0.045, 'recall': 0.05562422744128554, 'f1': 0.04975124378109452, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.24034334763948498, 'recall': 0.26291079812206575, 'f1': 0.25112107623318386, 'number': 1065} | 0.1501 | 0.1631 | 0.1563 | 0.4234 |
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- | 1.5582 | 7.0 | 70 | 1.5242 | {'precision': 0.05465587044534413, 'recall': 0.06674907292954264, 'f1': 0.060100166944908176, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.26282051282051283, 'recall': 0.307981220657277, 'f1': 0.2836143536532642, 'number': 1065} | 0.1708 | 0.1917 | 0.1807 | 0.4483 |
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- | 1.5135 | 8.0 | 80 | 1.4789 | {'precision': 0.05976520811099253, 'recall': 0.069221260815822, 'f1': 0.06414662084765177, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.29073482428115016, 'recall': 0.34178403755868547, 'f1': 0.31419939577039274, 'number': 1065} | 0.1919 | 0.2107 | 0.2009 | 0.4679 |
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- | 1.4676 | 9.0 | 90 | 1.4380 | {'precision': 0.06818181818181818, 'recall': 0.07416563658838071, 'f1': 0.07104795737122557, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3149480415667466, 'recall': 0.3699530516431925, 'f1': 0.34024179620034545, 'number': 1065} | 0.2130 | 0.2278 | 0.2202 | 0.4851 |
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- | 1.4233 | 10.0 | 100 | 1.4035 | {'precision': 0.07664670658682635, 'recall': 0.07911001236093942, 'f1': 0.0778588807785888, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3413848631239936, 'recall': 0.39812206572769954, 'f1': 0.3675769397485913, 'number': 1065} | 0.2350 | 0.2449 | 0.2398 | 0.4988 |
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- | 1.3864 | 11.0 | 110 | 1.3744 | {'precision': 0.0810126582278481, 'recall': 0.07911001236093942, 'f1': 0.08005003126954345, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3583535108958838, 'recall': 0.4169014084507042, 'f1': 0.38541666666666663, 'number': 1065} | 0.2504 | 0.2549 | 0.2526 | 0.5113 |
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- | 1.3746 | 12.0 | 120 | 1.3519 | {'precision': 0.0870712401055409, 'recall': 0.0815822002472188, 'f1': 0.08423739629865987, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3806818181818182, 'recall': 0.4403755868544601, 'f1': 0.40835872877666524, 'number': 1065} | 0.2688 | 0.2684 | 0.2686 | 0.5175 |
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- | 1.3417 | 13.0 | 130 | 1.3352 | {'precision': 0.09568733153638814, 'recall': 0.08776266996291718, 'f1': 0.09155383623468731, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.39403706688154716, 'recall': 0.4591549295774648, 'f1': 0.4241110147441457, 'number': 1065} | 0.2824 | 0.2810 | 0.2817 | 0.5272 |
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- | 1.3318 | 14.0 | 140 | 1.3254 | {'precision': 0.09686221009549795, 'recall': 0.08776266996291718, 'f1': 0.09208819714656291, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3942307692307692, 'recall': 0.4619718309859155, 'f1': 0.4254215304798963, 'number': 1065} | 0.2841 | 0.2825 | 0.2833 | 0.5314 |
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- | 1.3086 | 15.0 | 150 | 1.3215 | {'precision': 0.10096818810511757, 'recall': 0.09023485784919653, 'f1': 0.09530026109660573, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3980815347721823, 'recall': 0.4676056338028169, 'f1': 0.43005181347150256, 'number': 1065} | 0.2891 | 0.2865 | 0.2878 | 0.5339 |
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  ### Framework versions
 
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17
  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
18
  It achieves the following results on the evaluation set:
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+ - Loss: 1.3293
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+ - Answer: {'precision': 0.11451135241855874, 'recall': 0.1433868974042027, 'f1': 0.12733260153677278, 'number': 809}
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  - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
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+ - Question: {'precision': 0.41704374057315236, 'recall': 0.5192488262910798, 'f1': 0.46256796319531585, 'number': 1065}
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+ - Overall Precision: 0.2860
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+ - Overall Recall: 0.3357
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+ - Overall F1: 0.3089
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+ - Overall Accuracy: 0.5623
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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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+ | 1.9774 | 1.0 | 10 | 1.9285 | {'precision': 0.018331226295828066, 'recall': 0.03584672435105068, 'f1': 0.024257632789627767, 'number': 809} | {'precision': 0.00787878787878788, 'recall': 0.1092436974789916, 'f1': 0.014697569248162805, 'number': 119} | {'precision': 0.06559356136820925, 'recall': 0.15305164319248826, 'f1': 0.09183098591549295, 'number': 1065} | 0.0359 | 0.1029 | 0.0532 | 0.1843 |
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+ | 1.8918 | 2.0 | 20 | 1.8488 | {'precision': 0.02769385699899295, 'recall': 0.06798516687268233, 'f1': 0.03935599284436494, 'number': 809} | {'precision': 0.003703703703703704, 'recall': 0.008403361344537815, 'f1': 0.0051413881748071984, 'number': 119} | {'precision': 0.07554585152838428, 'recall': 0.1624413145539906, 'f1': 0.10312965722801788, 'number': 1065} | 0.0504 | 0.1149 | 0.0700 | 0.2606 |
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+ | 1.8117 | 3.0 | 30 | 1.7797 | {'precision': 0.02564102564102564, 'recall': 0.0580964153275649, 'f1': 0.03557910673732021, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.0943496801705757, 'recall': 0.16619718309859155, 'f1': 0.120367222033322, 'number': 1065} | 0.0601 | 0.1124 | 0.0783 | 0.3026 |
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+ | 1.7441 | 4.0 | 40 | 1.7198 | {'precision': 0.019028871391076115, 'recall': 0.03584672435105068, 'f1': 0.024860694384912133, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.12127512127512127, 'recall': 0.1643192488262911, 'f1': 0.13955342902711323, 'number': 1065} | 0.0686 | 0.1024 | 0.0822 | 0.3324 |
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+ | 1.6818 | 5.0 | 50 | 1.6641 | {'precision': 0.0196078431372549, 'recall': 0.03337453646477132, 'f1': 0.024702653247941447, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.15128593040847202, 'recall': 0.18779342723004694, 'f1': 0.16757436112274823, 'number': 1065} | 0.0841 | 0.1139 | 0.0968 | 0.3537 |
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+ | 1.6335 | 6.0 | 60 | 1.6097 | {'precision': 0.02643171806167401, 'recall': 0.04449938195302843, 'f1': 0.03316444035006909, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18782870022539444, 'recall': 0.2347417840375587, 'f1': 0.20868113522537562, 'number': 1065} | 0.1062 | 0.1435 | 0.1221 | 0.3821 |
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+ | 1.5742 | 7.0 | 70 | 1.5578 | {'precision': 0.033409263477600606, 'recall': 0.054388133498145856, 'f1': 0.04139228598306679, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.22088068181818182, 'recall': 0.292018779342723, 'f1': 0.2515163768701982, 'number': 1065} | 0.1303 | 0.1781 | 0.1505 | 0.4189 |
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+ | 1.5302 | 8.0 | 80 | 1.5083 | {'precision': 0.0456656346749226, 'recall': 0.07292954264524104, 'f1': 0.05616373155640171, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.24610169491525424, 'recall': 0.3408450704225352, 'f1': 0.2858267716535433, 'number': 1065} | 0.1525 | 0.2117 | 0.1773 | 0.4559 |
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+ | 1.4774 | 9.0 | 90 | 1.4639 | {'precision': 0.05325914149443561, 'recall': 0.08281829419035847, 'f1': 0.0648282535074988, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.28843537414965986, 'recall': 0.39812206572769954, 'f1': 0.33451676528599605, 'number': 1065} | 0.1800 | 0.2464 | 0.2080 | 0.4889 |
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+ | 1.4389 | 10.0 | 100 | 1.4263 | {'precision': 0.059574468085106386, 'recall': 0.0865265760197775, 'f1': 0.07056451612903225, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.32748948106591863, 'recall': 0.4384976525821596, 'f1': 0.3749498193496587, 'number': 1065} | 0.2065 | 0.2694 | 0.2338 | 0.5120 |
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+ | 1.4007 | 11.0 | 110 | 1.3933 | {'precision': 0.07123534715960325, 'recall': 0.09765142150803462, 'f1': 0.08237747653806049, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.360773085182534, 'recall': 0.4732394366197183, 'f1': 0.40942323314378554, 'number': 1065} | 0.2326 | 0.2925 | 0.2592 | 0.5334 |
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+ | 1.3866 | 12.0 | 120 | 1.3665 | {'precision': 0.09439252336448598, 'recall': 0.12484548825710753, 'f1': 0.10750399148483236, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.38648052902277735, 'recall': 0.49389671361502346, 'f1': 0.4336356141797197, 'number': 1065} | 0.2579 | 0.3146 | 0.2835 | 0.5428 |
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+ | 1.3482 | 13.0 | 130 | 1.3469 | {'precision': 0.10622009569377991, 'recall': 0.13720642768850433, 'f1': 0.11974110032362459, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.40044411547002223, 'recall': 0.507981220657277, 'f1': 0.44784768211920534, 'number': 1065} | 0.2721 | 0.3271 | 0.2971 | 0.5537 |
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+ | 1.3355 | 14.0 | 140 | 1.3345 | {'precision': 0.11078431372549019, 'recall': 0.13967861557478367, 'f1': 0.12356478950246036, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4114114114114114, 'recall': 0.5145539906103287, 'f1': 0.4572382144347101, 'number': 1065} | 0.2810 | 0.3317 | 0.3043 | 0.5588 |
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+ | 1.3066 | 15.0 | 150 | 1.3293 | {'precision': 0.11451135241855874, 'recall': 0.1433868974042027, 'f1': 0.12733260153677278, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.41704374057315236, 'recall': 0.5192488262910798, 'f1': 0.46256796319531585, 'number': 1065} | 0.2860 | 0.3357 | 0.3089 | 0.5623 |
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  ### Framework versions
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