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lmurray
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layoutlm-funsd

Token Classification
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layoutlm
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Files changed (4) hide show
  1. README.md +25 -25
  2. logs/events.out.tfevents.1719276189.ae49b29c4439.18029.0 +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: 0.7271
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- - Answer: {'precision': 0.7209821428571429, 'recall': 0.7985166872682324, 'f1': 0.7577712609970675, 'number': 809}
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- - Header: {'precision': 0.3308270676691729, 'recall': 0.3697478991596639, 'f1': 0.3492063492063492, 'number': 119}
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- - Question: {'precision': 0.7732049036777583, 'recall': 0.8291079812206573, 'f1': 0.8001812415043046, 'number': 1065}
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- - Overall Precision: 0.7246
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- - Overall Recall: 0.7893
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- - Overall F1: 0.7555
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- - Overall Accuracy: 0.8054
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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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- | 1.8393 | 1.0 | 10 | 1.5994 | {'precision': 0.02424942263279446, 'recall': 0.02595797280593325, 'f1': 0.02507462686567164, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.22250639386189258, 'recall': 0.16338028169014085, 'f1': 0.18841364374661615, 'number': 1065} | 0.1183 | 0.0978 | 0.1071 | 0.3781 |
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- | 1.4614 | 2.0 | 20 | 1.2520 | {'precision': 0.121765601217656, 'recall': 0.09888751545117429, 'f1': 0.10914051841746247, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.47543713572023316, 'recall': 0.536150234741784, 'f1': 0.503971756398941, 'number': 1065} | 0.3504 | 0.3266 | 0.3381 | 0.5844 |
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- | 1.1144 | 3.0 | 30 | 0.9610 | {'precision': 0.5, 'recall': 0.5278121137206427, 'f1': 0.513529765484065, 'number': 809} | {'precision': 0.037037037037037035, 'recall': 0.008403361344537815, 'f1': 0.0136986301369863, 'number': 119} | {'precision': 0.6224758560140474, 'recall': 0.6657276995305165, 'f1': 0.6433756805807623, 'number': 1065} | 0.5629 | 0.5705 | 0.5667 | 0.7246 |
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- | 0.8509 | 4.0 | 40 | 0.7961 | {'precision': 0.5943396226415094, 'recall': 0.7008652657601978, 'f1': 0.6432217810550198, 'number': 809} | {'precision': 0.3, 'recall': 0.12605042016806722, 'f1': 0.17751479289940827, 'number': 119} | {'precision': 0.6532188841201717, 'recall': 0.7145539906103286, 'f1': 0.6825112107623318, 'number': 1065} | 0.6192 | 0.6739 | 0.6454 | 0.7543 |
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- | 0.6724 | 5.0 | 50 | 0.7346 | {'precision': 0.6362683438155137, 'recall': 0.7503090234857849, 'f1': 0.6885989790130459, 'number': 809} | {'precision': 0.3466666666666667, 'recall': 0.2184873949579832, 'f1': 0.26804123711340205, 'number': 119} | {'precision': 0.6597444089456869, 'recall': 0.7755868544600939, 'f1': 0.7129909365558912, 'number': 1065} | 0.6396 | 0.7321 | 0.6827 | 0.7797 |
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- | 0.5743 | 6.0 | 60 | 0.7086 | {'precision': 0.6481481481481481, 'recall': 0.7787391841779975, 'f1': 0.7074677147669849, 'number': 809} | {'precision': 0.3424657534246575, 'recall': 0.21008403361344538, 'f1': 0.2604166666666667, 'number': 119} | {'precision': 0.7128116938950989, 'recall': 0.7784037558685446, 'f1': 0.7441651705565528, 'number': 1065} | 0.6721 | 0.7446 | 0.7065 | 0.7845 |
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- | 0.4963 | 7.0 | 70 | 0.6881 | {'precision': 0.6866952789699571, 'recall': 0.7911001236093943, 'f1': 0.7352096496266514, 'number': 809} | {'precision': 0.30392156862745096, 'recall': 0.2605042016806723, 'f1': 0.28054298642533937, 'number': 119} | {'precision': 0.7263249348392702, 'recall': 0.7849765258215963, 'f1': 0.7545126353790614, 'number': 1065} | 0.6897 | 0.7561 | 0.7214 | 0.7926 |
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- | 0.4392 | 8.0 | 80 | 0.7116 | {'precision': 0.6779487179487179, 'recall': 0.8170580964153276, 'f1': 0.741031390134529, 'number': 809} | {'precision': 0.28431372549019607, 'recall': 0.24369747899159663, 'f1': 0.26244343891402716, 'number': 119} | {'precision': 0.7322175732217573, 'recall': 0.8215962441314554, 'f1': 0.7743362831858407, 'number': 1065} | 0.6888 | 0.7852 | 0.7339 | 0.7886 |
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- | 0.3945 | 9.0 | 90 | 0.7000 | {'precision': 0.7060737527114967, 'recall': 0.8046971569839307, 'f1': 0.7521663778162911, 'number': 809} | {'precision': 0.2920353982300885, 'recall': 0.2773109243697479, 'f1': 0.28448275862068967, 'number': 119} | {'precision': 0.7502183406113537, 'recall': 0.8065727699530516, 'f1': 0.7773755656108599, 'number': 1065} | 0.7078 | 0.7742 | 0.7395 | 0.7989 |
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- | 0.3825 | 10.0 | 100 | 0.7006 | {'precision': 0.717607973421927, 'recall': 0.8009888751545118, 'f1': 0.7570093457943926, 'number': 809} | {'precision': 0.29464285714285715, 'recall': 0.2773109243697479, 'f1': 0.28571428571428575, 'number': 119} | {'precision': 0.7642418930762489, 'recall': 0.8187793427230047, 'f1': 0.7905711695376247, 'number': 1065} | 0.7203 | 0.7792 | 0.7486 | 0.8053 |
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- | 0.327 | 11.0 | 110 | 0.7180 | {'precision': 0.6969376979936642, 'recall': 0.8158220024721878, 'f1': 0.7517084282460138, 'number': 809} | {'precision': 0.2975206611570248, 'recall': 0.3025210084033613, 'f1': 0.3, 'number': 119} | {'precision': 0.7572898799313894, 'recall': 0.8291079812206573, 'f1': 0.7915732855221873, 'number': 1065} | 0.7068 | 0.7923 | 0.7471 | 0.7969 |
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- | 0.3136 | 12.0 | 120 | 0.7147 | {'precision': 0.7283950617283951, 'recall': 0.8022249690976514, 'f1': 0.7635294117647059, 'number': 809} | {'precision': 0.3305084745762712, 'recall': 0.3277310924369748, 'f1': 0.32911392405063294, 'number': 119} | {'precision': 0.7831111111111111, 'recall': 0.8272300469483568, 'f1': 0.8045662100456622, 'number': 1065} | 0.7352 | 0.7873 | 0.7604 | 0.8059 |
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- | 0.2943 | 13.0 | 130 | 0.7297 | {'precision': 0.7136659436008677, 'recall': 0.8133498145859085, 'f1': 0.7602541883304449, 'number': 809} | {'precision': 0.34210526315789475, 'recall': 0.3277310924369748, 'f1': 0.33476394849785407, 'number': 119} | {'precision': 0.7785588752196837, 'recall': 0.831924882629108, 'f1': 0.8043576940535634, 'number': 1065} | 0.7282 | 0.7943 | 0.7598 | 0.8001 |
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- | 0.2727 | 14.0 | 140 | 0.7284 | {'precision': 0.7275784753363229, 'recall': 0.8022249690976514, 'f1': 0.7630805408583187, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3697478991596639, 'f1': 0.350597609561753, 'number': 119} | {'precision': 0.7732049036777583, 'recall': 0.8291079812206573, 'f1': 0.8001812415043046, 'number': 1065} | 0.7276 | 0.7908 | 0.7579 | 0.8046 |
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- | 0.2753 | 15.0 | 150 | 0.7271 | {'precision': 0.7209821428571429, 'recall': 0.7985166872682324, 'f1': 0.7577712609970675, 'number': 809} | {'precision': 0.3308270676691729, 'recall': 0.3697478991596639, 'f1': 0.3492063492063492, 'number': 119} | {'precision': 0.7732049036777583, 'recall': 0.8291079812206573, 'f1': 0.8001812415043046, 'number': 1065} | 0.7246 | 0.7893 | 0.7555 | 0.8054 |
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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: 0.6766
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+ - Answer: {'precision': 0.6937033084311632, 'recall': 0.8034610630407911, 'f1': 0.7445589919816725, 'number': 809}
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+ - Header: {'precision': 0.34146341463414637, 'recall': 0.35294117647058826, 'f1': 0.34710743801652894, 'number': 119}
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+ - Question: {'precision': 0.7879858657243817, 'recall': 0.8375586854460094, 'f1': 0.812016385980883, 'number': 1065}
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+ - Overall Precision: 0.7226
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+ - Overall Recall: 0.7948
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+ - Overall F1: 0.7570
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+ - Overall Accuracy: 0.8076
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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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 1.7798 | 1.0 | 10 | 1.5839 | {'precision': 0.01451378809869376, 'recall': 0.012360939431396786, 'f1': 0.01335113484646195, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1924342105263158, 'recall': 0.10985915492957747, 'f1': 0.1398684997011357, 'number': 1065} | 0.0979 | 0.0637 | 0.0772 | 0.3437 |
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+ | 1.4432 | 2.0 | 20 | 1.2365 | {'precision': 0.24876604146100692, 'recall': 0.311495673671199, 'f1': 0.27661909989023054, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4279411764705882, 'recall': 0.5464788732394367, 'f1': 0.48000000000000004, 'number': 1065} | 0.3515 | 0.4185 | 0.3820 | 0.5956 |
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+ | 1.0756 | 3.0 | 30 | 0.9004 | {'precision': 0.5348399246704332, 'recall': 0.7021013597033374, 'f1': 0.6071619454836986, 'number': 809} | {'precision': 0.03225806451612903, 'recall': 0.008403361344537815, 'f1': 0.013333333333333332, 'number': 119} | {'precision': 0.5821917808219178, 'recall': 0.7183098591549296, 'f1': 0.6431273644388399, 'number': 1065} | 0.5542 | 0.6693 | 0.6064 | 0.7160 |
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+ | 0.8288 | 4.0 | 40 | 0.7621 | {'precision': 0.6007984031936128, 'recall': 0.7441285537700866, 'f1': 0.6648260629486471, 'number': 809} | {'precision': 0.10526315789473684, 'recall': 0.06722689075630252, 'f1': 0.08205128205128205, 'number': 119} | {'precision': 0.6632825719120136, 'recall': 0.7361502347417841, 'f1': 0.6978193146417446, 'number': 1065} | 0.6168 | 0.6994 | 0.6555 | 0.7647 |
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+ | 0.6673 | 5.0 | 50 | 0.7300 | {'precision': 0.6344314558979809, 'recall': 0.7379480840543882, 'f1': 0.6822857142857143, 'number': 809} | {'precision': 0.2247191011235955, 'recall': 0.16806722689075632, 'f1': 0.19230769230769232, 'number': 119} | {'precision': 0.6439909297052154, 'recall': 0.8, 'f1': 0.71356783919598, 'number': 1065} | 0.6243 | 0.7371 | 0.6760 | 0.7704 |
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+ | 0.5732 | 6.0 | 60 | 0.6729 | {'precision': 0.6424974823766365, 'recall': 0.788627935723115, 'f1': 0.7081021087680356, 'number': 809} | {'precision': 0.21, 'recall': 0.17647058823529413, 'f1': 0.19178082191780824, 'number': 119} | {'precision': 0.7048494983277592, 'recall': 0.7915492957746478, 'f1': 0.7456877487837241, 'number': 1065} | 0.6562 | 0.7536 | 0.7015 | 0.7928 |
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+ | 0.5005 | 7.0 | 70 | 0.6515 | {'precision': 0.6611740473738414, 'recall': 0.7935723114956736, 'f1': 0.7213483146067416, 'number': 809} | {'precision': 0.2288135593220339, 'recall': 0.226890756302521, 'f1': 0.22784810126582278, 'number': 119} | {'precision': 0.7432784041630529, 'recall': 0.8046948356807512, 'f1': 0.7727682596934174, 'number': 1065} | 0.6806 | 0.7657 | 0.7207 | 0.8005 |
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+ | 0.4473 | 8.0 | 80 | 0.6629 | {'precision': 0.6748717948717948, 'recall': 0.8133498145859085, 'f1': 0.7376681614349776, 'number': 809} | {'precision': 0.27586206896551724, 'recall': 0.2689075630252101, 'f1': 0.27234042553191484, 'number': 119} | {'precision': 0.7614520311149524, 'recall': 0.8272300469483568, 'f1': 0.7929792979297929, 'number': 1065} | 0.6988 | 0.7883 | 0.7409 | 0.7983 |
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+ | 0.3953 | 9.0 | 90 | 0.6507 | {'precision': 0.6750524109014675, 'recall': 0.796044499381953, 'f1': 0.7305728871242201, 'number': 809} | {'precision': 0.3008849557522124, 'recall': 0.2857142857142857, 'f1': 0.29310344827586204, 'number': 119} | {'precision': 0.7689625108979947, 'recall': 0.828169014084507, 'f1': 0.7974683544303798, 'number': 1065} | 0.7046 | 0.7827 | 0.7416 | 0.8031 |
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+ | 0.3926 | 10.0 | 100 | 0.6539 | {'precision': 0.6704663212435233, 'recall': 0.799752781211372, 'f1': 0.729425028184893, 'number': 809} | {'precision': 0.3217391304347826, 'recall': 0.31092436974789917, 'f1': 0.3162393162393162, 'number': 119} | {'precision': 0.7722513089005235, 'recall': 0.8309859154929577, 'f1': 0.8005427408412483, 'number': 1065} | 0.7049 | 0.7873 | 0.7438 | 0.8046 |
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+ | 0.3344 | 11.0 | 110 | 0.6626 | {'precision': 0.6831578947368421, 'recall': 0.8022249690976514, 'f1': 0.7379192723138146, 'number': 809} | {'precision': 0.3140495867768595, 'recall': 0.31932773109243695, 'f1': 0.31666666666666665, 'number': 119} | {'precision': 0.7677029360967185, 'recall': 0.8347417840375587, 'f1': 0.7998200629779576, 'number': 1065} | 0.7070 | 0.7908 | 0.7466 | 0.8054 |
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+ | 0.316 | 12.0 | 120 | 0.6678 | {'precision': 0.6871035940803383, 'recall': 0.8034610630407911, 'f1': 0.7407407407407406, 'number': 809} | {'precision': 0.31666666666666665, 'recall': 0.31932773109243695, 'f1': 0.3179916317991632, 'number': 119} | {'precision': 0.7860300618921309, 'recall': 0.8347417840375587, 'f1': 0.8096539162112932, 'number': 1065} | 0.7178 | 0.7913 | 0.7527 | 0.8071 |
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+ | 0.3001 | 13.0 | 130 | 0.6758 | {'precision': 0.6856540084388185, 'recall': 0.8034610630407911, 'f1': 0.7398975526465565, 'number': 809} | {'precision': 0.325, 'recall': 0.3277310924369748, 'f1': 0.3263598326359833, 'number': 119} | {'precision': 0.792, 'recall': 0.8366197183098592, 'f1': 0.8136986301369863, 'number': 1065} | 0.7205 | 0.7928 | 0.7549 | 0.8082 |
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+ | 0.279 | 14.0 | 140 | 0.6750 | {'precision': 0.6875, 'recall': 0.8022249690976514, 'f1': 0.7404449515116942, 'number': 809} | {'precision': 0.31451612903225806, 'recall': 0.3277310924369748, 'f1': 0.32098765432098764, 'number': 119} | {'precision': 0.7816901408450704, 'recall': 0.8338028169014085, 'f1': 0.8069059518400727, 'number': 1065} | 0.7151 | 0.7908 | 0.7510 | 0.8066 |
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+ | 0.2793 | 15.0 | 150 | 0.6766 | {'precision': 0.6937033084311632, 'recall': 0.8034610630407911, 'f1': 0.7445589919816725, 'number': 809} | {'precision': 0.34146341463414637, 'recall': 0.35294117647058826, 'f1': 0.34710743801652894, 'number': 119} | {'precision': 0.7879858657243817, 'recall': 0.8375586854460094, 'f1': 0.812016385980883, 'number': 1065} | 0.7226 | 0.7948 | 0.7570 | 0.8076 |
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  ### Framework versions
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