--- 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: 0.6724 - Answer: {'precision': 0.7072368421052632, 'recall': 0.7972805933250927, 'f1': 0.7495642068564788, 'number': 809} - Header: {'precision': 0.3333333333333333, 'recall': 0.3697478991596639, 'f1': 0.350597609561753, 'number': 119} - Question: {'precision': 0.7901785714285714, 'recall': 0.8309859154929577, 'f1': 0.8100686498855836, 'number': 1065} - Overall Precision: 0.7274 - Overall Recall: 0.7898 - Overall F1: 0.7573 - Overall Accuracy: 0.8170 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 1.7705 | 1.0 | 10 | 1.5739 | {'precision': 0.010057471264367816, 'recall': 0.00865265760197775, 'f1': 0.00930232558139535, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.26591760299625467, 'recall': 0.13333333333333333, 'f1': 0.17761100687929957, 'number': 1065} | 0.1211 | 0.0748 | 0.0925 | 0.3598 | | 1.4468 | 2.0 | 20 | 1.2327 | {'precision': 0.25151148730350664, 'recall': 0.25710754017305315, 'f1': 0.2542787286063569, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4251316779533484, 'recall': 0.5305164319248826, 'f1': 0.4720133667502089, 'number': 1065} | 0.3585 | 0.3879 | 0.3726 | 0.5921 | | 1.1103 | 3.0 | 30 | 0.9608 | {'precision': 0.4880694143167028, 'recall': 0.5562422744128553, 'f1': 0.5199306759098786, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5613598673300166, 'recall': 0.6356807511737089, 'f1': 0.5962131219726994, 'number': 1065} | 0.5257 | 0.5655 | 0.5448 | 0.7058 | | 0.8476 | 4.0 | 40 | 0.7875 | {'precision': 0.5821325648414986, 'recall': 0.7490729295426453, 'f1': 0.6551351351351351, 'number': 809} | {'precision': 0.1702127659574468, 'recall': 0.06722689075630252, 'f1': 0.09638554216867469, 'number': 119} | {'precision': 0.6254071661237784, 'recall': 0.7211267605633803, 'f1': 0.6698648059310947, 'number': 1065} | 0.5967 | 0.6934 | 0.6414 | 0.7538 | | 0.6698 | 5.0 | 50 | 0.6948 | {'precision': 0.6421923474663909, 'recall': 0.7676143386897404, 'f1': 0.6993243243243243, 'number': 809} | {'precision': 0.3424657534246575, 'recall': 0.21008403361344538, 'f1': 0.2604166666666667, 'number': 119} | {'precision': 0.6873977086743044, 'recall': 0.7887323943661971, 'f1': 0.7345867949278532, 'number': 1065} | 0.6569 | 0.7456 | 0.6985 | 0.7860 | | 0.554 | 6.0 | 60 | 0.6717 | {'precision': 0.6506410256410257, 'recall': 0.7527812113720643, 'f1': 0.6979942693409743, 'number': 809} | {'precision': 0.3448275862068966, 'recall': 0.25210084033613445, 'f1': 0.2912621359223301, 'number': 119} | {'precision': 0.716821639898563, 'recall': 0.7962441314553991, 'f1': 0.7544483985765124, 'number': 1065} | 0.6741 | 0.7461 | 0.7083 | 0.7920 | | 0.4787 | 7.0 | 70 | 0.6462 | {'precision': 0.6666666666666666, 'recall': 0.7935723114956736, 'f1': 0.7246049661399547, 'number': 809} | {'precision': 0.3017241379310345, 'recall': 0.29411764705882354, 'f1': 0.29787234042553185, 'number': 119} | {'precision': 0.7368421052631579, 'recall': 0.8018779342723005, 'f1': 0.7679856115107914, 'number': 1065} | 0.6841 | 0.7682 | 0.7237 | 0.8038 | | 0.4182 | 8.0 | 80 | 0.6516 | {'precision': 0.6790890269151139, 'recall': 0.8108776266996292, 'f1': 0.7391549295774646, 'number': 809} | {'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119} | {'precision': 0.7450643776824034, 'recall': 0.8150234741784037, 'f1': 0.77847533632287, 'number': 1065} | 0.6949 | 0.7817 | 0.7358 | 0.8025 | | 0.3877 | 9.0 | 90 | 0.6652 | {'precision': 0.6976744186046512, 'recall': 0.7787391841779975, 'f1': 0.7359813084112149, 'number': 809} | {'precision': 0.3194444444444444, 'recall': 0.3865546218487395, 'f1': 0.34980988593155893, 'number': 119} | {'precision': 0.7573913043478261, 'recall': 0.8178403755868544, 'f1': 0.7864559819413092, 'number': 1065} | 0.7041 | 0.7762 | 0.7384 | 0.8094 | | 0.3483 | 10.0 | 100 | 0.6568 | {'precision': 0.6876332622601279, 'recall': 0.7972805933250927, 'f1': 0.7384087006296507, 'number': 809} | {'precision': 0.3225806451612903, 'recall': 0.33613445378151263, 'f1': 0.3292181069958848, 'number': 119} | {'precision': 0.7650602409638554, 'recall': 0.8347417840375587, 'f1': 0.7983834755276155, 'number': 1065} | 0.7077 | 0.7898 | 0.7465 | 0.8151 | | 0.3136 | 11.0 | 110 | 0.6698 | {'precision': 0.7006507592190889, 'recall': 0.7985166872682324, 'f1': 0.7463893703061815, 'number': 809} | {'precision': 0.3247863247863248, 'recall': 0.31932773109243695, 'f1': 0.3220338983050848, 'number': 119} | {'precision': 0.7803365810451727, 'recall': 0.8272300469483568, 'f1': 0.8030993618960802, 'number': 1065} | 0.7219 | 0.7852 | 0.7522 | 0.8084 | | 0.3044 | 12.0 | 120 | 0.6667 | {'precision': 0.7058177826564215, 'recall': 0.7948084054388134, 'f1': 0.7476744186046511, 'number': 809} | {'precision': 0.34328358208955223, 'recall': 0.3865546218487395, 'f1': 0.36363636363636365, 'number': 119} | {'precision': 0.785204991087344, 'recall': 0.8272300469483568, 'f1': 0.8056698673982624, 'number': 1065} | 0.7245 | 0.7878 | 0.7548 | 0.8138 | | 0.2853 | 13.0 | 130 | 0.6699 | {'precision': 0.702819956616052, 'recall': 0.8009888751545118, 'f1': 0.7487001733102253, 'number': 809} | {'precision': 0.3442622950819672, 'recall': 0.35294117647058826, 'f1': 0.3485477178423237, 'number': 119} | {'precision': 0.7857769973661106, 'recall': 0.8403755868544601, 'f1': 0.8121597096188747, 'number': 1065} | 0.7261 | 0.7953 | 0.7591 | 0.8136 | | 0.278 | 14.0 | 140 | 0.6716 | {'precision': 0.7009750812567714, 'recall': 0.799752781211372, 'f1': 0.7471131639722863, 'number': 809} | {'precision': 0.3233082706766917, 'recall': 0.36134453781512604, 'f1': 0.3412698412698413, 'number': 119} | {'precision': 0.7873665480427047, 'recall': 0.8309859154929577, 'f1': 0.808588396528095, 'number': 1065} | 0.7225 | 0.7903 | 0.7549 | 0.8154 | | 0.2672 | 15.0 | 150 | 0.6724 | {'precision': 0.7072368421052632, 'recall': 0.7972805933250927, 'f1': 0.7495642068564788, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3697478991596639, 'f1': 0.350597609561753, 'number': 119} | {'precision': 0.7901785714285714, 'recall': 0.8309859154929577, 'f1': 0.8100686498855836, 'number': 1065} | 0.7274 | 0.7898 | 0.7573 | 0.8170 | ### Framework versions - Transformers 4.25.1 - Pytorch 1.9.0 - Datasets 2.8.0 - Tokenizers 0.13.2