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layoutlm-funsd

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1105
  • Answer: {'precision': 0.7390326209223848, 'recall': 0.8121137206427689, 'f1': 0.773851590106007, 'number': 809}
  • Header: {'precision': 0.4206896551724138, 'recall': 0.5126050420168067, 'f1': 0.46212121212121215, 'number': 119}
  • Question: {'precision': 0.8176795580110497, 'recall': 0.8338028169014085, 'f1': 0.8256624825662483, 'number': 1065}
  • Overall Precision: 0.7575
  • Overall Recall: 0.8058
  • Overall F1: 0.7809
  • Overall Accuracy: 0.8108

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Answer Header Question Overall Precision Overall Recall Overall F1 Overall Accuracy
1.7888 1.0 10 1.5431 {'precision': 0.023681377825618945, 'recall': 0.027194066749072928, 'f1': 0.02531645569620253, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.23876404494382023, 'recall': 0.1596244131455399, 'f1': 0.19133370849746764, 'number': 1065} 0.1170 0.0963 0.1057 0.3970
1.3827 2.0 20 1.1583 {'precision': 0.2289156626506024, 'recall': 0.21137206427688504, 'f1': 0.21979434447300772, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.46695652173913044, 'recall': 0.504225352112676, 'f1': 0.48487584650112864, 'number': 1065} 0.3732 0.3552 0.3640 0.6072
1.0226 3.0 30 0.8747 {'precision': 0.521594684385382, 'recall': 0.5822002472187886, 'f1': 0.5502336448598131, 'number': 809} {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} {'precision': 0.6095238095238096, 'recall': 0.6610328638497652, 'f1': 0.6342342342342343, 'number': 1065} 0.5595 0.5896 0.5742 0.7279
0.7648 4.0 40 0.7658 {'precision': 0.5875706214689266, 'recall': 0.7713226205191595, 'f1': 0.667022982362373, 'number': 809} {'precision': 0.22058823529411764, 'recall': 0.12605042016806722, 'f1': 0.16042780748663102, 'number': 119} {'precision': 0.6698357821953328, 'recall': 0.7276995305164319, 'f1': 0.6975697569756976, 'number': 1065} 0.6183 0.7095 0.6607 0.7635
0.6327 5.0 50 0.6972 {'precision': 0.6389473684210526, 'recall': 0.7503090234857849, 'f1': 0.6901648664013643, 'number': 809} {'precision': 0.3448275862068966, 'recall': 0.16806722689075632, 'f1': 0.22598870056497172, 'number': 119} {'precision': 0.7047619047619048, 'recall': 0.7643192488262911, 'f1': 0.7333333333333334, 'number': 1065} 0.6662 0.7230 0.6935 0.7853
0.5253 6.0 60 0.6675 {'precision': 0.667027027027027, 'recall': 0.7626699629171817, 'f1': 0.7116493656286043, 'number': 809} {'precision': 0.2248062015503876, 'recall': 0.24369747899159663, 'f1': 0.2338709677419355, 'number': 119} {'precision': 0.6939586645468998, 'recall': 0.819718309859155, 'f1': 0.751614291863969, 'number': 1065} 0.6570 0.7622 0.7057 0.7984
0.4339 7.0 70 0.6615 {'precision': 0.7005524861878453, 'recall': 0.7836835599505563, 'f1': 0.7397899649941657, 'number': 809} {'precision': 0.24342105263157895, 'recall': 0.31092436974789917, 'f1': 0.27306273062730624, 'number': 119} {'precision': 0.7237386269644334, 'recall': 0.8215962441314554, 'f1': 0.7695690413368513, 'number': 1065} 0.6823 0.7757 0.7260 0.8022
0.3775 8.0 80 0.6598 {'precision': 0.7138009049773756, 'recall': 0.7799752781211372, 'f1': 0.7454223272297696, 'number': 809} {'precision': 0.27007299270072993, 'recall': 0.31092436974789917, 'f1': 0.2890625, 'number': 119} {'precision': 0.7389491242702252, 'recall': 0.831924882629108, 'f1': 0.7826855123674912, 'number': 1065} 0.7 0.7797 0.7377 0.8078
0.3219 9.0 90 0.6811 {'precision': 0.7013422818791947, 'recall': 0.7750309023485785, 'f1': 0.7363476218438051, 'number': 809} {'precision': 0.30158730158730157, 'recall': 0.31932773109243695, 'f1': 0.310204081632653, 'number': 119} {'precision': 0.7541268462206777, 'recall': 0.8150234741784037, 'f1': 0.7833935018050542, 'number': 1065} 0.7061 0.7692 0.7363 0.8004
0.2732 10.0 100 0.6725 {'precision': 0.7259668508287292, 'recall': 0.8121137206427689, 'f1': 0.766627771295216, 'number': 809} {'precision': 0.3053435114503817, 'recall': 0.33613445378151263, 'f1': 0.32000000000000006, 'number': 119} {'precision': 0.7798573975044564, 'recall': 0.8215962441314554, 'f1': 0.800182898948331, 'number': 1065} 0.7285 0.7888 0.7574 0.8113
0.2275 11.0 110 0.7076 {'precision': 0.7237569060773481, 'recall': 0.8096415327564895, 'f1': 0.764294049008168, 'number': 809} {'precision': 0.3381294964028777, 'recall': 0.3949579831932773, 'f1': 0.3643410852713178, 'number': 119} {'precision': 0.7820284697508897, 'recall': 0.8253521126760563, 'f1': 0.8031064412973961, 'number': 1065} 0.7292 0.7933 0.7599 0.8109
0.195 12.0 120 0.7281 {'precision': 0.7301231802911534, 'recall': 0.8059332509270705, 'f1': 0.7661574618096357, 'number': 809} {'precision': 0.3141025641025641, 'recall': 0.4117647058823529, 'f1': 0.3563636363636363, 'number': 119} {'precision': 0.7923497267759563, 'recall': 0.8169014084507042, 'f1': 0.8044382801664355, 'number': 1065} 0.7317 0.7883 0.7589 0.8085
0.1765 13.0 130 0.7390 {'precision': 0.7318181818181818, 'recall': 0.796044499381953, 'f1': 0.7625814091178211, 'number': 809} {'precision': 0.358974358974359, 'recall': 0.47058823529411764, 'f1': 0.40727272727272734, 'number': 119} {'precision': 0.781195079086116, 'recall': 0.8347417840375587, 'f1': 0.8070812528370404, 'number': 1065} 0.7309 0.7973 0.7627 0.8164
0.1463 14.0 140 0.7731 {'precision': 0.7437923250564334, 'recall': 0.8145859085290482, 'f1': 0.7775811209439528, 'number': 809} {'precision': 0.3368421052631579, 'recall': 0.5378151260504201, 'f1': 0.4142394822006472, 'number': 119} {'precision': 0.7963800904977375, 'recall': 0.8262910798122066, 'f1': 0.8110599078341013, 'number': 1065} 0.7350 0.8043 0.7681 0.8163
0.1292 15.0 150 0.7745 {'precision': 0.738933030646992, 'recall': 0.8046971569839307, 'f1': 0.770414201183432, 'number': 809} {'precision': 0.3719512195121951, 'recall': 0.5126050420168067, 'f1': 0.431095406360424, 'number': 119} {'precision': 0.7852650494159928, 'recall': 0.8206572769953052, 'f1': 0.8025711662075299, 'number': 1065} 0.7349 0.7958 0.7642 0.8111
0.1209 16.0 160 0.8221 {'precision': 0.7181719260065288, 'recall': 0.8158220024721878, 'f1': 0.7638888888888888, 'number': 809} {'precision': 0.3333333333333333, 'recall': 0.4789915966386555, 'f1': 0.393103448275862, 'number': 119} {'precision': 0.7963302752293578, 'recall': 0.8150234741784037, 'f1': 0.805568445475638, 'number': 1065} 0.7271 0.7953 0.7596 0.8063
0.1095 17.0 170 0.8437 {'precision': 0.7384441939120632, 'recall': 0.8096415327564895, 'f1': 0.7724056603773586, 'number': 809} {'precision': 0.3673469387755102, 'recall': 0.453781512605042, 'f1': 0.406015037593985, 'number': 119} {'precision': 0.7715030408340573, 'recall': 0.8338028169014085, 'f1': 0.8014440433212997, 'number': 1065} 0.7309 0.8013 0.7645 0.8098
0.0888 18.0 180 0.8539 {'precision': 0.7426556991774383, 'recall': 0.7812113720642769, 'f1': 0.7614457831325301, 'number': 809} {'precision': 0.3716216216216216, 'recall': 0.46218487394957986, 'f1': 0.41198501872659177, 'number': 119} {'precision': 0.784070796460177, 'recall': 0.831924882629108, 'f1': 0.8072892938496583, 'number': 1065} 0.7388 0.7893 0.7632 0.8063
0.0796 19.0 190 0.8697 {'precision': 0.7249154453213078, 'recall': 0.7948084054388134, 'f1': 0.7582547169811321, 'number': 809} {'precision': 0.39416058394160586, 'recall': 0.453781512605042, 'f1': 0.421875, 'number': 119} {'precision': 0.7835232252410167, 'recall': 0.8394366197183099, 'f1': 0.8105167724388033, 'number': 1065} 0.7349 0.7983 0.7653 0.8091
0.0747 20.0 200 0.9098 {'precision': 0.7414965986394558, 'recall': 0.8084054388133498, 'f1': 0.7735068007096393, 'number': 809} {'precision': 0.33695652173913043, 'recall': 0.5210084033613446, 'f1': 0.40924092409240925, 'number': 119} {'precision': 0.8124419684308264, 'recall': 0.8215962441314554, 'f1': 0.8169934640522875, 'number': 1065} 0.7424 0.7983 0.7693 0.8074
0.0654 21.0 210 0.9180 {'precision': 0.7411225658648339, 'recall': 0.799752781211372, 'f1': 0.7693222354340071, 'number': 809} {'precision': 0.3905325443786982, 'recall': 0.5546218487394958, 'f1': 0.4583333333333333, 'number': 119} {'precision': 0.8036866359447005, 'recall': 0.8187793427230047, 'f1': 0.8111627906976744, 'number': 1065} 0.7452 0.7953 0.7694 0.8082
0.0545 22.0 220 0.9456 {'precision': 0.7479768786127168, 'recall': 0.799752781211372, 'f1': 0.7729988052568698, 'number': 809} {'precision': 0.34502923976608185, 'recall': 0.4957983193277311, 'f1': 0.4068965517241379, 'number': 119} {'precision': 0.7932910244786945, 'recall': 0.8215962441314554, 'f1': 0.8071955719557196, 'number': 1065} 0.7391 0.7933 0.7652 0.8064
0.0526 23.0 230 0.9552 {'precision': 0.7383720930232558, 'recall': 0.7849196538936959, 'f1': 0.7609346914319952, 'number': 809} {'precision': 0.36075949367088606, 'recall': 0.4789915966386555, 'f1': 0.41155234657039713, 'number': 119} {'precision': 0.7947794779477948, 'recall': 0.8291079812206573, 'f1': 0.8115808823529413, 'number': 1065} 0.7398 0.7903 0.7642 0.8048
0.0465 24.0 240 0.9799 {'precision': 0.7411111111111112, 'recall': 0.8244746600741656, 'f1': 0.780573434757168, 'number': 809} {'precision': 0.44274809160305345, 'recall': 0.48739495798319327, 'f1': 0.464, 'number': 119} {'precision': 0.7863475177304965, 'recall': 0.8328638497652582, 'f1': 0.8089375284997721, 'number': 1065} 0.7466 0.8088 0.7765 0.8082
0.0402 25.0 250 0.9705 {'precision': 0.7559591373439274, 'recall': 0.823238566131026, 'f1': 0.7881656804733728, 'number': 809} {'precision': 0.40559440559440557, 'recall': 0.48739495798319327, 'f1': 0.4427480916030534, 'number': 119} {'precision': 0.785204991087344, 'recall': 0.8272300469483568, 'f1': 0.8056698673982624, 'number': 1065} 0.7479 0.8053 0.7755 0.8091
0.0426 26.0 260 0.9772 {'precision': 0.7350917431192661, 'recall': 0.792336217552534, 'f1': 0.7626412849494348, 'number': 809} {'precision': 0.40816326530612246, 'recall': 0.5042016806722689, 'f1': 0.45112781954887216, 'number': 119} {'precision': 0.8149498632634458, 'recall': 0.8394366197183099, 'f1': 0.8270120259019426, 'number': 1065} 0.7538 0.8003 0.7763 0.8103
0.0377 27.0 270 0.9820 {'precision': 0.7414965986394558, 'recall': 0.8084054388133498, 'f1': 0.7735068007096393, 'number': 809} {'precision': 0.4405594405594406, 'recall': 0.5294117647058824, 'f1': 0.48091603053435117, 'number': 119} {'precision': 0.8223866790009251, 'recall': 0.8347417840375587, 'f1': 0.8285181733457596, 'number': 1065} 0.7626 0.8058 0.7836 0.8106
0.031 28.0 280 1.0041 {'precision': 0.734717416378316, 'recall': 0.7873918417799752, 'f1': 0.7601431980906921, 'number': 809} {'precision': 0.4064516129032258, 'recall': 0.5294117647058824, 'f1': 0.4598540145985402, 'number': 119} {'precision': 0.8052064631956912, 'recall': 0.8422535211267606, 'f1': 0.8233134465351079, 'number': 1065} 0.7477 0.8013 0.7736 0.8075
0.0322 29.0 290 1.0408 {'precision': 0.7292817679558011, 'recall': 0.8158220024721878, 'f1': 0.7701283547257876, 'number': 809} {'precision': 0.4027777777777778, 'recall': 0.48739495798319327, 'f1': 0.44106463878326996, 'number': 119} {'precision': 0.8104693140794224, 'recall': 0.8431924882629108, 'f1': 0.8265071329958583, 'number': 1065} 0.7492 0.8108 0.7788 0.8062
0.0292 30.0 300 1.0359 {'precision': 0.7351598173515982, 'recall': 0.796044499381953, 'f1': 0.7643916913946588, 'number': 809} {'precision': 0.3727810650887574, 'recall': 0.5294117647058824, 'f1': 0.4375, 'number': 119} {'precision': 0.8218283582089553, 'recall': 0.8272300469483568, 'f1': 0.8245203556387459, 'number': 1065} 0.7501 0.7968 0.7727 0.8045
0.0259 31.0 310 1.0452 {'precision': 0.7275784753363229, 'recall': 0.8022249690976514, 'f1': 0.7630805408583187, 'number': 809} {'precision': 0.41732283464566927, 'recall': 0.44537815126050423, 'f1': 0.43089430894308944, 'number': 119} {'precision': 0.8238532110091743, 'recall': 0.8431924882629108, 'f1': 0.8334106728538283, 'number': 1065} 0.7587 0.8028 0.7801 0.8082
0.0268 32.0 320 1.0653 {'precision': 0.7338618346545867, 'recall': 0.8009888751545118, 'f1': 0.7659574468085106, 'number': 809} {'precision': 0.39473684210526316, 'recall': 0.5042016806722689, 'f1': 0.4428044280442805, 'number': 119} {'precision': 0.7909252669039146, 'recall': 0.8347417840375587, 'f1': 0.8122430333485611, 'number': 1065} 0.7397 0.8013 0.7693 0.8040
0.0258 33.0 330 1.0603 {'precision': 0.7429537767756482, 'recall': 0.8145859085290482, 'f1': 0.777122641509434, 'number': 809} {'precision': 0.3885350318471338, 'recall': 0.5126050420168067, 'f1': 0.4420289855072464, 'number': 119} {'precision': 0.7969314079422383, 'recall': 0.8291079812206573, 'f1': 0.8127013345605153, 'number': 1065} 0.7449 0.8043 0.7735 0.8077
0.0236 34.0 340 1.0683 {'precision': 0.7353951890034365, 'recall': 0.7935723114956736, 'f1': 0.7633769322235434, 'number': 809} {'precision': 0.36470588235294116, 'recall': 0.5210084033613446, 'f1': 0.4290657439446367, 'number': 119} {'precision': 0.8066298342541437, 'recall': 0.8225352112676056, 'f1': 0.814504881450488, 'number': 1065} 0.7421 0.7928 0.7666 0.8057
0.0219 35.0 350 1.0757 {'precision': 0.7254464285714286, 'recall': 0.8034610630407911, 'f1': 0.7624633431085045, 'number': 809} {'precision': 0.43609022556390975, 'recall': 0.48739495798319327, 'f1': 0.46031746031746035, 'number': 119} {'precision': 0.7964444444444444, 'recall': 0.8413145539906103, 'f1': 0.8182648401826484, 'number': 1065} 0.7447 0.8048 0.7736 0.8054
0.0229 36.0 360 1.1042 {'precision': 0.7369020501138952, 'recall': 0.799752781211372, 'f1': 0.7670420865441612, 'number': 809} {'precision': 0.3584905660377358, 'recall': 0.4789915966386555, 'f1': 0.41007194244604317, 'number': 119} {'precision': 0.809040590405904, 'recall': 0.8234741784037559, 'f1': 0.8161935784085622, 'number': 1065} 0.7454 0.7933 0.7686 0.8040
0.0205 37.0 370 1.0980 {'precision': 0.7352614015572859, 'recall': 0.8170580964153276, 'f1': 0.7740046838407495, 'number': 809} {'precision': 0.36423841059602646, 'recall': 0.46218487394957986, 'f1': 0.4074074074074074, 'number': 119} {'precision': 0.8087431693989071, 'recall': 0.8338028169014085, 'f1': 0.8210818307905686, 'number': 1065} 0.7467 0.8048 0.7747 0.8085
0.0194 38.0 380 1.0869 {'precision': 0.740909090909091, 'recall': 0.8059332509270705, 'f1': 0.7720544701006513, 'number': 809} {'precision': 0.3972602739726027, 'recall': 0.48739495798319327, 'f1': 0.43773584905660373, 'number': 119} {'precision': 0.7982062780269058, 'recall': 0.8356807511737089, 'f1': 0.8165137614678898, 'number': 1065} 0.7473 0.8028 0.7741 0.8052
0.0217 39.0 390 1.0871 {'precision': 0.7366071428571429, 'recall': 0.8158220024721878, 'f1': 0.7741935483870966, 'number': 809} {'precision': 0.39864864864864863, 'recall': 0.4957983193277311, 'f1': 0.44194756554307113, 'number': 119} {'precision': 0.8102189781021898, 'recall': 0.8338028169014085, 'f1': 0.8218417399352151, 'number': 1065} 0.7509 0.8063 0.7776 0.8078
0.0186 40.0 400 1.0944 {'precision': 0.7315436241610739, 'recall': 0.8084054388133498, 'f1': 0.7680563711098063, 'number': 809} {'precision': 0.41216216216216217, 'recall': 0.5126050420168067, 'f1': 0.4569288389513108, 'number': 119} {'precision': 0.7991031390134529, 'recall': 0.8366197183098592, 'f1': 0.8174311926605504, 'number': 1065} 0.7446 0.8058 0.7740 0.8069
0.0172 41.0 410 1.0907 {'precision': 0.7425968109339408, 'recall': 0.8059332509270705, 'f1': 0.7729697688203911, 'number': 809} {'precision': 0.41843971631205673, 'recall': 0.4957983193277311, 'f1': 0.4538461538461538, 'number': 119} {'precision': 0.8132474701011959, 'recall': 0.8300469483568075, 'f1': 0.8215613382899627, 'number': 1065} 0.7574 0.8003 0.7782 0.8115
0.0163 42.0 420 1.1016 {'precision': 0.7427293064876958, 'recall': 0.8207663782447466, 'f1': 0.7798003523194362, 'number': 809} {'precision': 0.3945578231292517, 'recall': 0.48739495798319327, 'f1': 0.43609022556390975, 'number': 119} {'precision': 0.8060109289617486, 'recall': 0.8309859154929577, 'f1': 0.8183079056865464, 'number': 1065} 0.7513 0.8063 0.7778 0.8099
0.0165 43.0 430 1.1055 {'precision': 0.7410714285714286, 'recall': 0.8207663782447466, 'f1': 0.7788856304985337, 'number': 809} {'precision': 0.39864864864864863, 'recall': 0.4957983193277311, 'f1': 0.44194756554307113, 'number': 119} {'precision': 0.8113553113553114, 'recall': 0.831924882629108, 'f1': 0.8215113583681039, 'number': 1065} 0.7533 0.8073 0.7794 0.8090
0.0157 44.0 440 1.1047 {'precision': 0.7334826427771557, 'recall': 0.8096415327564895, 'f1': 0.7696827262044654, 'number': 809} {'precision': 0.4166666666666667, 'recall': 0.5042016806722689, 'f1': 0.4562737642585551, 'number': 119} {'precision': 0.8155963302752294, 'recall': 0.8347417840375587, 'f1': 0.8250580046403712, 'number': 1065} 0.7541 0.8048 0.7786 0.8103
0.0147 45.0 450 1.1064 {'precision': 0.7329608938547486, 'recall': 0.8108776266996292, 'f1': 0.7699530516431925, 'number': 809} {'precision': 0.41216216216216217, 'recall': 0.5126050420168067, 'f1': 0.4569288389513108, 'number': 119} {'precision': 0.8107370336669699, 'recall': 0.8366197183098592, 'f1': 0.8234750462107209, 'number': 1065} 0.7507 0.8068 0.7778 0.8106
0.0136 46.0 460 1.1085 {'precision': 0.7334826427771557, 'recall': 0.8096415327564895, 'f1': 0.7696827262044654, 'number': 809} {'precision': 0.4206896551724138, 'recall': 0.5126050420168067, 'f1': 0.46212121212121215, 'number': 119} {'precision': 0.8111313868613139, 'recall': 0.8347417840375587, 'f1': 0.822767237390097, 'number': 1065} 0.7521 0.8053 0.7778 0.8098
0.0168 47.0 470 1.1110 {'precision': 0.7368421052631579, 'recall': 0.8133498145859085, 'f1': 0.7732079905992949, 'number': 809} {'precision': 0.41496598639455784, 'recall': 0.5126050420168067, 'f1': 0.4586466165413534, 'number': 119} {'precision': 0.810958904109589, 'recall': 0.8338028169014085, 'f1': 0.8222222222222222, 'number': 1065} 0.7527 0.8063 0.7786 0.8106
0.0137 48.0 480 1.1121 {'precision': 0.7370786516853932, 'recall': 0.8108776266996292, 'f1': 0.7722189523248971, 'number': 809} {'precision': 0.39869281045751637, 'recall': 0.5126050420168067, 'f1': 0.4485294117647059, 'number': 119} {'precision': 0.8113553113553114, 'recall': 0.831924882629108, 'f1': 0.8215113583681039, 'number': 1065} 0.7508 0.8043 0.7766 0.8103
0.0137 49.0 490 1.1106 {'precision': 0.7382022471910112, 'recall': 0.8121137206427689, 'f1': 0.7733961153619776, 'number': 809} {'precision': 0.4178082191780822, 'recall': 0.5126050420168067, 'f1': 0.460377358490566, 'number': 119} {'precision': 0.8143382352941176, 'recall': 0.831924882629108, 'f1': 0.8230376219228982, 'number': 1065} 0.7552 0.8048 0.7792 0.8104
0.0134 50.0 500 1.1105 {'precision': 0.7390326209223848, 'recall': 0.8121137206427689, 'f1': 0.773851590106007, 'number': 809} {'precision': 0.4206896551724138, 'recall': 0.5126050420168067, 'f1': 0.46212121212121215, 'number': 119} {'precision': 0.8176795580110497, 'recall': 0.8338028169014085, 'f1': 0.8256624825662483, 'number': 1065} 0.7575 0.8058 0.7809 0.8108

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Tokenizers 0.15.0
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