--- tags: - generated_from_trainer datasets: - funsd model-index: - name: layoutlm-funsd results: [] --- # layoutlm-funsd This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. It achieves the following results on the evaluation set: - Loss: 0.7376 - Answer: {'precision': 0.6796116504854369, 'recall': 0.7787391841779975, 'f1': 0.7258064516129031, 'number': 809} - Header: {'precision': 0.3076923076923077, 'recall': 0.33613445378151263, 'f1': 0.321285140562249, 'number': 119} - Question: {'precision': 0.7692307692307693, 'recall': 0.8356807511737089, 'f1': 0.8010801080108011, 'number': 1065} - Overall Precision: 0.7046 - Overall Recall: 0.7827 - Overall F1: 0.7416 - Overall Accuracy: 0.7934 ## 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: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 1.8071 | 1.0 | 10 | 1.6362 | {'precision': 0.01038961038961039, 'recall': 0.004944375772558714, 'f1': 0.006700167504187604, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.336, 'recall': 0.11830985915492957, 'f1': 0.17500000000000002, 'number': 1065} | 0.1711 | 0.0652 | 0.0944 | 0.3269 | | 1.5124 | 2.0 | 20 | 1.3120 | {'precision': 0.12598425196850394, 'recall': 0.11866501854140915, 'f1': 0.12221514958625079, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4277456647398844, 'recall': 0.4863849765258216, 'f1': 0.4551845342706503, 'number': 1065} | 0.3112 | 0.3081 | 0.3096 | 0.5731 | | 1.1643 | 3.0 | 30 | 1.0389 | {'precision': 0.4329411764705882, 'recall': 0.45488257107540175, 'f1': 0.44364074743821574, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5088122605363985, 'recall': 0.6234741784037559, 'f1': 0.560337552742616, 'number': 1065} | 0.4727 | 0.5178 | 0.4943 | 0.6785 | | 0.8925 | 4.0 | 40 | 0.8687 | {'precision': 0.5737704918032787, 'recall': 0.6489493201483313, 'f1': 0.6090487238979118, 'number': 809} | {'precision': 0.2222222222222222, 'recall': 0.08403361344537816, 'f1': 0.1219512195121951, 'number': 119} | {'precision': 0.664568345323741, 'recall': 0.6938967136150235, 'f1': 0.6789159393661002, 'number': 1065} | 0.6149 | 0.6392 | 0.6268 | 0.7348 | | 0.7169 | 5.0 | 50 | 0.7748 | {'precision': 0.6039707419017764, 'recall': 0.7144622991347342, 'f1': 0.6545866364665911, 'number': 809} | {'precision': 0.25274725274725274, 'recall': 0.19327731092436976, 'f1': 0.21904761904761905, 'number': 119} | {'precision': 0.6873935264054515, 'recall': 0.7577464788732394, 'f1': 0.7208575256811076, 'number': 1065} | 0.6337 | 0.7065 | 0.6681 | 0.7647 | | 0.5923 | 6.0 | 60 | 0.7214 | {'precision': 0.6291322314049587, 'recall': 0.7527812113720643, 'f1': 0.6854248733821047, 'number': 809} | {'precision': 0.323943661971831, 'recall': 0.19327731092436976, 'f1': 0.24210526315789474, 'number': 119} | {'precision': 0.6884735202492211, 'recall': 0.8300469483568075, 'f1': 0.752660706683695, 'number': 1065} | 0.6526 | 0.7607 | 0.7025 | 0.7795 | | 0.5208 | 7.0 | 70 | 0.7338 | {'precision': 0.6555793991416309, 'recall': 0.7552533992583437, 'f1': 0.7018954623779436, 'number': 809} | {'precision': 0.27450980392156865, 'recall': 0.23529411764705882, 'f1': 0.2533936651583711, 'number': 119} | {'precision': 0.7224523612261806, 'recall': 0.8187793427230047, 'f1': 0.7676056338028169, 'number': 1065} | 0.6743 | 0.7582 | 0.7137 | 0.7853 | | 0.4668 | 8.0 | 80 | 0.6981 | {'precision': 0.6534446764091858, 'recall': 0.7737948084054388, 'f1': 0.708545557441992, 'number': 809} | {'precision': 0.27884615384615385, 'recall': 0.24369747899159663, 'f1': 0.2600896860986547, 'number': 119} | {'precision': 0.7394190871369295, 'recall': 0.8366197183098592, 'f1': 0.785022026431718, 'number': 1065} | 0.6820 | 0.7757 | 0.7258 | 0.7917 | | 0.413 | 9.0 | 90 | 0.7140 | {'precision': 0.6777408637873754, 'recall': 0.7564894932014833, 'f1': 0.7149532710280373, 'number': 809} | {'precision': 0.2755905511811024, 'recall': 0.29411764705882354, 'f1': 0.2845528455284553, 'number': 119} | {'precision': 0.7495784148397976, 'recall': 0.8347417840375587, 'f1': 0.7898711683696135, 'number': 1065} | 0.6931 | 0.7707 | 0.7299 | 0.7910 | | 0.372 | 10.0 | 100 | 0.7031 | {'precision': 0.6843853820598007, 'recall': 0.7639060568603214, 'f1': 0.72196261682243, 'number': 809} | {'precision': 0.31092436974789917, 'recall': 0.31092436974789917, 'f1': 0.31092436974789917, 'number': 119} | {'precision': 0.749793559042114, 'recall': 0.8525821596244132, 'f1': 0.7978910369068541, 'number': 1065} | 0.7000 | 0.7842 | 0.7397 | 0.7998 | | 0.3317 | 11.0 | 110 | 0.7231 | {'precision': 0.677765843179377, 'recall': 0.7799752781211372, 'f1': 0.725287356321839, 'number': 809} | {'precision': 0.3162393162393162, 'recall': 0.31092436974789917, 'f1': 0.3135593220338983, 'number': 119} | {'precision': 0.7748917748917749, 'recall': 0.8403755868544601, 'f1': 0.8063063063063063, 'number': 1065} | 0.7095 | 0.7842 | 0.7450 | 0.7931 | | 0.3156 | 12.0 | 120 | 0.7385 | {'precision': 0.6800433839479393, 'recall': 0.7750309023485785, 'f1': 0.7244367417677644, 'number': 809} | {'precision': 0.3253968253968254, 'recall': 0.3445378151260504, 'f1': 0.33469387755102037, 'number': 119} | {'precision': 0.7756521739130435, 'recall': 0.8375586854460094, 'f1': 0.8054176072234764, 'number': 1065} | 0.7097 | 0.7827 | 0.7445 | 0.7924 | | 0.2945 | 13.0 | 130 | 0.7311 | {'precision': 0.6856516976998904, 'recall': 0.7737948084054388, 'f1': 0.727061556329849, 'number': 809} | {'precision': 0.3203125, 'recall': 0.3445378151260504, 'f1': 0.33198380566801616, 'number': 119} | {'precision': 0.7658662092624356, 'recall': 0.8384976525821596, 'f1': 0.8005378753922008, 'number': 1065} | 0.7068 | 0.7827 | 0.7429 | 0.7937 | | 0.2867 | 14.0 | 140 | 0.7304 | {'precision': 0.6826086956521739, 'recall': 0.7762669962917181, 'f1': 0.7264314632735686, 'number': 809} | {'precision': 0.31496062992125984, 'recall': 0.33613445378151263, 'f1': 0.3252032520325203, 'number': 119} | {'precision': 0.7632933104631218, 'recall': 0.8356807511737089, 'f1': 0.7978484984311968, 'number': 1065} | 0.7040 | 0.7817 | 0.7408 | 0.7934 | | 0.2865 | 15.0 | 150 | 0.7376 | {'precision': 0.6796116504854369, 'recall': 0.7787391841779975, 'f1': 0.7258064516129031, 'number': 809} | {'precision': 0.3076923076923077, 'recall': 0.33613445378151263, 'f1': 0.321285140562249, 'number': 119} | {'precision': 0.7692307692307693, 'recall': 0.8356807511737089, 'f1': 0.8010801080108011, 'number': 1065} | 0.7046 | 0.7827 | 0.7416 | 0.7934 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3