--- library_name: transformers license: mit base_model: microsoft/layoutlm-base-uncased 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: 0.7672 - Answer: {'precision': 0.7376681614349776, 'recall': 0.8133498145859085, 'f1': 0.7736625514403291, 'number': 809} - Header: {'precision': 0.37857142857142856, 'recall': 0.44537815126050423, 'f1': 0.4092664092664093, 'number': 119} - Question: {'precision': 0.8122109158186864, 'recall': 0.8244131455399061, 'f1': 0.8182665424044735, 'number': 1065} - Overall Precision: 0.7520 - Overall Recall: 0.7973 - Overall F1: 0.7740 - Overall Accuracy: 0.8111 ## 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: 8 - 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 | |:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 1.6896 | 1.0 | 19 | 1.4068 | {'precision': 0.0572737686139748, 'recall': 0.06180469715698393, 'f1': 0.05945303210463733, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4044834307992203, 'recall': 0.38967136150234744, 'f1': 0.3969392635102822, 'number': 1065} | 0.2449 | 0.2333 | 0.2390 | 0.4540 | | 1.1462 | 2.0 | 38 | 0.8626 | {'precision': 0.5445075757575758, 'recall': 0.7107540173053152, 'f1': 0.6166219839142091, 'number': 809} | {'precision': 0.04878048780487805, 'recall': 0.01680672268907563, 'f1': 0.025, 'number': 119} | {'precision': 0.6331615120274914, 'recall': 0.692018779342723, 'f1': 0.661283086585913, 'number': 1065} | 0.5812 | 0.6593 | 0.6178 | 0.7285 | | 0.7687 | 3.0 | 57 | 0.7051 | {'precision': 0.6217264791464597, 'recall': 0.792336217552534, 'f1': 0.6967391304347825, 'number': 809} | {'precision': 0.18421052631578946, 'recall': 0.11764705882352941, 'f1': 0.14358974358974358, 'number': 119} | {'precision': 0.6805084745762712, 'recall': 0.7539906103286385, 'f1': 0.7153674832962139, 'number': 1065} | 0.6375 | 0.7316 | 0.6813 | 0.7678 | | 0.5961 | 4.0 | 76 | 0.6423 | {'precision': 0.6527918781725889, 'recall': 0.7948084054388134, 'f1': 0.7168338907469342, 'number': 809} | {'precision': 0.26785714285714285, 'recall': 0.25210084033613445, 'f1': 0.2597402597402597, 'number': 119} | {'precision': 0.7100949094046591, 'recall': 0.7727699530516432, 'f1': 0.7401079136690648, 'number': 1065} | 0.6631 | 0.7506 | 0.7042 | 0.7923 | | 0.4739 | 5.0 | 95 | 0.6263 | {'precision': 0.7024972855591748, 'recall': 0.799752781211372, 'f1': 0.7479768786127167, 'number': 809} | {'precision': 0.33070866141732286, 'recall': 0.35294117647058826, 'f1': 0.34146341463414637, 'number': 119} | {'precision': 0.7435897435897436, 'recall': 0.8169014084507042, 'f1': 0.778523489932886, 'number': 1065} | 0.7029 | 0.7822 | 0.7404 | 0.8059 | | 0.3893 | 6.0 | 114 | 0.6456 | {'precision': 0.6912681912681913, 'recall': 0.8220024721878862, 'f1': 0.7509881422924901, 'number': 809} | {'precision': 0.28169014084507044, 'recall': 0.33613445378151263, 'f1': 0.30651340996168586, 'number': 119} | {'precision': 0.768270944741533, 'recall': 0.8093896713615023, 'f1': 0.7882944673068131, 'number': 1065} | 0.7040 | 0.7863 | 0.7428 | 0.8050 | | 0.3267 | 7.0 | 133 | 0.6813 | {'precision': 0.7297605473204105, 'recall': 0.7911001236093943, 'f1': 0.7591933570581256, 'number': 809} | {'precision': 0.33070866141732286, 'recall': 0.35294117647058826, 'f1': 0.34146341463414637, 'number': 119} | {'precision': 0.7810283687943262, 'recall': 0.8272300469483568, 'f1': 0.8034655722754217, 'number': 1065} | 0.7331 | 0.7842 | 0.7578 | 0.8019 | | 0.2731 | 8.0 | 152 | 0.6628 | {'precision': 0.7153846153846154, 'recall': 0.8046971569839307, 'f1': 0.7574171029668412, 'number': 809} | {'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119} | {'precision': 0.7877145438121048, 'recall': 0.8187793427230047, 'f1': 0.8029465930018417, 'number': 1065} | 0.7336 | 0.7863 | 0.7590 | 0.8137 | | 0.2425 | 9.0 | 171 | 0.6992 | {'precision': 0.7209302325581395, 'recall': 0.8046971569839307, 'f1': 0.7605140186915887, 'number': 809} | {'precision': 0.3851851851851852, 'recall': 0.4369747899159664, 'f1': 0.4094488188976378, 'number': 119} | {'precision': 0.801980198019802, 'recall': 0.8366197183098592, 'f1': 0.8189338235294118, 'number': 1065} | 0.7417 | 0.7998 | 0.7697 | 0.8104 | | 0.2145 | 10.0 | 190 | 0.7271 | {'precision': 0.7373167981961668, 'recall': 0.8084054388133498, 'f1': 0.7712264150943396, 'number': 809} | {'precision': 0.36075949367088606, 'recall': 0.4789915966386555, 'f1': 0.41155234657039713, 'number': 119} | {'precision': 0.8250950570342205, 'recall': 0.8150234741784037, 'f1': 0.8200283419933868, 'number': 1065} | 0.7530 | 0.7923 | 0.7721 | 0.8047 | | 0.1882 | 11.0 | 209 | 0.7348 | {'precision': 0.7400681044267877, 'recall': 0.8059332509270705, 'f1': 0.7715976331360946, 'number': 809} | {'precision': 0.375, 'recall': 0.453781512605042, 'f1': 0.4106463878326997, 'number': 119} | {'precision': 0.8254716981132075, 'recall': 0.8215962441314554, 'f1': 0.8235294117647057, 'number': 1065} | 0.7583 | 0.7933 | 0.7754 | 0.8103 | | 0.1668 | 12.0 | 228 | 0.7541 | {'precision': 0.7360178970917226, 'recall': 0.8133498145859085, 'f1': 0.7727539635936582, 'number': 809} | {'precision': 0.3984375, 'recall': 0.42857142857142855, 'f1': 0.41295546558704455, 'number': 119} | {'precision': 0.8007246376811594, 'recall': 0.8300469483568075, 'f1': 0.8151221761180267, 'number': 1065} | 0.7493 | 0.7993 | 0.7735 | 0.8097 | | 0.1595 | 13.0 | 247 | 0.7616 | {'precision': 0.7370786516853932, 'recall': 0.8108776266996292, 'f1': 0.7722189523248971, 'number': 809} | {'precision': 0.38345864661654133, 'recall': 0.42857142857142855, 'f1': 0.4047619047619047, 'number': 119} | {'precision': 0.8153988868274582, 'recall': 0.8253521126760563, 'f1': 0.8203453103126458, 'number': 1065} | 0.7549 | 0.7958 | 0.7748 | 0.8122 | | 0.1451 | 14.0 | 266 | 0.7638 | {'precision': 0.7361894024802705, 'recall': 0.8071693448702101, 'f1': 0.7700471698113207, 'number': 809} | {'precision': 0.375886524822695, 'recall': 0.44537815126050423, 'f1': 0.40769230769230774, 'number': 119} | {'precision': 0.8150557620817844, 'recall': 0.8234741784037559, 'f1': 0.8192433442316676, 'number': 1065} | 0.7524 | 0.7943 | 0.7728 | 0.8110 | | 0.1449 | 15.0 | 285 | 0.7672 | {'precision': 0.7376681614349776, 'recall': 0.8133498145859085, 'f1': 0.7736625514403291, 'number': 809} | {'precision': 0.37857142857142856, 'recall': 0.44537815126050423, 'f1': 0.4092664092664093, 'number': 119} | {'precision': 0.8122109158186864, 'recall': 0.8244131455399061, 'f1': 0.8182665424044735, 'number': 1065} | 0.7520 | 0.7973 | 0.7740 | 0.8111 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1