--- 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: 0.6712 - Answer: {'precision': 0.6719409282700421, 'recall': 0.7873918417799752, 'f1': 0.7250996015936254, 'number': 809} - Header: {'precision': 0.3153153153153153, 'recall': 0.29411764705882354, 'f1': 0.30434782608695654, 'number': 119} - Question: {'precision': 0.7069109075770191, 'recall': 0.7971830985915493, 'f1': 0.7493380406001765, 'number': 1065} - Overall Precision: 0.6730 - Overall Recall: 0.7632 - Overall F1: 0.7153 - Overall Accuracy: 0.7909 ## 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 1.7528 | 1.0 | 10 | 1.5450 | {'precision': 0.04079497907949791, 'recall': 0.048207663782447466, 'f1': 0.04419263456090652, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1806060606060606, 'recall': 0.13990610328638498, 'f1': 0.15767195767195766, 'number': 1065} | 0.1056 | 0.0943 | 0.0996 | 0.3786 | | 1.4294 | 2.0 | 20 | 1.2643 | {'precision': 0.20842824601366744, 'recall': 0.22620519159456118, 'f1': 0.2169531713100178, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4424778761061947, 'recall': 0.5164319248826291, 'f1': 0.4766031195840555, 'number': 1065} | 0.3456 | 0.3678 | 0.3563 | 0.5767 | | 1.1277 | 3.0 | 30 | 0.9879 | {'precision': 0.4243845252051583, 'recall': 0.44746600741656367, 'f1': 0.4356197352587245, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5726141078838174, 'recall': 0.647887323943662, 'f1': 0.6079295154185022, 'number': 1065} | 0.5092 | 0.5278 | 0.5184 | 0.6932 | | 0.8834 | 4.0 | 40 | 0.8188 | {'precision': 0.574052812858783, 'recall': 0.6180469715698393, 'f1': 0.5952380952380952, 'number': 809} | {'precision': 0.12, 'recall': 0.05042016806722689, 'f1': 0.07100591715976332, 'number': 119} | {'precision': 0.6459369817578773, 'recall': 0.7314553990610329, 'f1': 0.6860413914575078, 'number': 1065} | 0.6041 | 0.6448 | 0.6238 | 0.7497 | | 0.7042 | 5.0 | 50 | 0.7333 | {'precision': 0.628385698808234, 'recall': 0.7169344870210136, 'f1': 0.6697459584295612, 'number': 809} | {'precision': 0.29411764705882354, 'recall': 0.16806722689075632, 'f1': 0.21390374331550802, 'number': 119} | {'precision': 0.6616242038216561, 'recall': 0.780281690140845, 'f1': 0.7160706591986213, 'number': 1065} | 0.6368 | 0.7180 | 0.6750 | 0.7748 | | 0.6134 | 6.0 | 60 | 0.7075 | {'precision': 0.6507276507276507, 'recall': 0.7737948084054388, 'f1': 0.7069452286843592, 'number': 809} | {'precision': 0.2987012987012987, 'recall': 0.19327731092436976, 'f1': 0.23469387755102045, 'number': 119} | {'precision': 0.7140366172624237, 'recall': 0.7690140845070422, 'f1': 0.7405063291139241, 'number': 1065} | 0.6715 | 0.7366 | 0.7026 | 0.7789 | | 0.5519 | 7.0 | 70 | 0.6817 | {'precision': 0.6593521421107628, 'recall': 0.7799752781211372, 'f1': 0.7146092865232163, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.24369747899159663, 'f1': 0.2815533980582524, 'number': 119} | {'precision': 0.7023608768971332, 'recall': 0.7821596244131456, 'f1': 0.7401155042203464, 'number': 1065} | 0.6695 | 0.7491 | 0.7071 | 0.7857 | | 0.5105 | 8.0 | 80 | 0.6738 | {'precision': 0.6628630705394191, 'recall': 0.7898640296662547, 'f1': 0.7208121827411168, 'number': 809} | {'precision': 0.2912621359223301, 'recall': 0.25210084033613445, 'f1': 0.2702702702702703, 'number': 119} | {'precision': 0.709106239460371, 'recall': 0.7896713615023474, 'f1': 0.7472234562416704, 'number': 1065} | 0.6702 | 0.7577 | 0.7113 | 0.7899 | | 0.4684 | 9.0 | 90 | 0.6721 | {'precision': 0.6656217345872518, 'recall': 0.7873918417799752, 'f1': 0.7214043035107587, 'number': 809} | {'precision': 0.3090909090909091, 'recall': 0.2857142857142857, 'f1': 0.296943231441048, 'number': 119} | {'precision': 0.703150912106136, 'recall': 0.7962441314553991, 'f1': 0.7468075737560547, 'number': 1065} | 0.6683 | 0.7622 | 0.7121 | 0.7906 | | 0.4814 | 10.0 | 100 | 0.6712 | {'precision': 0.6719409282700421, 'recall': 0.7873918417799752, 'f1': 0.7250996015936254, 'number': 809} | {'precision': 0.3153153153153153, 'recall': 0.29411764705882354, 'f1': 0.30434782608695654, 'number': 119} | {'precision': 0.7069109075770191, 'recall': 0.7971830985915493, 'f1': 0.7493380406001765, 'number': 1065} | 0.6730 | 0.7632 | 0.7153 | 0.7909 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1