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--- |
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base_model: microsoft/layoutlm-base-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- funsd |
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model-index: |
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- name: layoutlm-funsd |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# layoutlm-funsd |
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3725 |
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- Answer: {'precision': 0.07982261640798226, 'recall': 0.08899876390605686, 'f1': 0.0841613091759205, 'number': 809} |
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- Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} |
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- Question: {'precision': 0.4174242424242424, 'recall': 0.5173708920187794, 'f1': 0.46205450733752623, 'number': 1065} |
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- Overall Precision: 0.2804 |
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- Overall Recall: 0.3126 |
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- Overall F1: 0.2956 |
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- Overall Accuracy: 0.5437 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 15 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 1.8773 | 1.0 | 10 | 1.8489 | {'precision': 0.00547645125958379, 'recall': 0.006180469715698393, 'f1': 0.005807200929152149, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.04874446085672083, 'recall': 0.030985915492957747, 'f1': 0.03788748564867968, 'number': 1065} | 0.0227 | 0.0191 | 0.0207 | 0.2819 | |
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| 1.807 | 2.0 | 20 | 1.7831 | {'precision': 0.005925925925925926, 'recall': 0.004944375772558714, 'f1': 0.005390835579514824, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.06716417910447761, 'recall': 0.03380281690140845, 'f1': 0.04497189256714553, 'number': 1065} | 0.0327 | 0.0201 | 0.0249 | 0.2996 | |
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| 1.7516 | 3.0 | 30 | 1.7272 | {'precision': 0.0071633237822349575, 'recall': 0.006180469715698393, 'f1': 0.006635700066357001, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.10175438596491228, 'recall': 0.054460093896713614, 'f1': 0.0709480122324159, 'number': 1065} | 0.0496 | 0.0316 | 0.0386 | 0.3189 | |
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| 1.7057 | 4.0 | 40 | 1.6785 | {'precision': 0.012626262626262626, 'recall': 0.012360939431396786, 'f1': 0.012492192379762648, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.16886930983847284, 'recall': 0.107981220657277, 'f1': 0.13172966781214204, 'number': 1065} | 0.0849 | 0.0627 | 0.0721 | 0.3426 | |
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| 1.6571 | 5.0 | 50 | 1.6336 | {'precision': 0.016286644951140065, 'recall': 0.018541409147095178, 'f1': 0.017341040462427744, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2211764705882353, 'recall': 0.17652582159624414, 'f1': 0.19634464751958225, 'number': 1065} | 0.1146 | 0.1019 | 0.1079 | 0.3714 | |
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| 1.6219 | 6.0 | 60 | 1.5894 | {'precision': 0.03238095238095238, 'recall': 0.042027194066749075, 'f1': 0.036578805809575045, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.26129666011787817, 'recall': 0.24976525821596243, 'f1': 0.2554008641382621, 'number': 1065} | 0.1451 | 0.1505 | 0.1477 | 0.4028 | |
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| 1.5748 | 7.0 | 70 | 1.5484 | {'precision': 0.03796296296296296, 'recall': 0.05067985166872682, 'f1': 0.04340921122286924, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.28073394495412846, 'recall': 0.28732394366197184, 'f1': 0.28399071925754066, 'number': 1065} | 0.1599 | 0.1741 | 0.1667 | 0.4319 | |
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| 1.5387 | 8.0 | 80 | 1.5098 | {'precision': 0.044036697247706424, 'recall': 0.059332509270704575, 'f1': 0.05055292259083728, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.30583333333333335, 'recall': 0.34460093896713617, 'f1': 0.3240618101545254, 'number': 1065} | 0.1812 | 0.2082 | 0.1938 | 0.4623 | |
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| 1.5004 | 9.0 | 90 | 1.4753 | {'precision': 0.05149812734082397, 'recall': 0.06798516687268233, 'f1': 0.05860415556739478, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3378812199036918, 'recall': 0.39530516431924884, 'f1': 0.36434443963652097, 'number': 1065} | 0.2057 | 0.2388 | 0.2210 | 0.4887 | |
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| 1.4659 | 10.0 | 100 | 1.4462 | {'precision': 0.058823529411764705, 'recall': 0.0754017305315204, 'f1': 0.06608884073672806, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3586530931871574, 'recall': 0.4300469483568075, 'f1': 0.39111870196413323, 'number': 1065} | 0.2243 | 0.2604 | 0.2410 | 0.5046 | |
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| 1.4314 | 11.0 | 110 | 1.4207 | {'precision': 0.06769230769230769, 'recall': 0.0815822002472188, 'f1': 0.07399103139013452, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.38271604938271603, 'recall': 0.46572769953051646, 'f1': 0.42016094875052945, 'number': 1065} | 0.2475 | 0.2820 | 0.2636 | 0.5184 | |
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| 1.4242 | 12.0 | 120 | 1.4003 | {'precision': 0.07203389830508475, 'recall': 0.08405438813349815, 'f1': 0.0775812892184826, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.40076628352490423, 'recall': 0.49107981220657276, 'f1': 0.4413502109704641, 'number': 1065} | 0.2628 | 0.2965 | 0.2786 | 0.5273 | |
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| 1.3939 | 13.0 | 130 | 1.3855 | {'precision': 0.07792207792207792, 'recall': 0.08899876390605686, 'f1': 0.0830929024812464, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.40953822861468586, 'recall': 0.507981220657277, 'f1': 0.45347862531433364, 'number': 1065} | 0.2731 | 0.3076 | 0.2893 | 0.5367 | |
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| 1.3837 | 14.0 | 140 | 1.3764 | {'precision': 0.08021978021978023, 'recall': 0.09023485784919653, 'f1': 0.08493310063990692, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.41635124905374715, 'recall': 0.5164319248826291, 'f1': 0.4610226320201173, 'number': 1065} | 0.2792 | 0.3126 | 0.2950 | 0.5410 | |
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| 1.3603 | 15.0 | 150 | 1.3725 | {'precision': 0.07982261640798226, 'recall': 0.08899876390605686, 'f1': 0.0841613091759205, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4174242424242424, 'recall': 0.5173708920187794, 'f1': 0.46205450733752623, 'number': 1065} | 0.2804 | 0.3126 | 0.2956 | 0.5437 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.0 |
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- Tokenizers 0.13.3 |
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