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---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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: 1.0844
- Answer: {'precision': 0.3143100511073254, 'recall': 0.4561186650185414, 'f1': 0.3721633888048412, 'number': 809}
- Header: {'precision': 0.275, 'recall': 0.18487394957983194, 'f1': 0.22110552763819097, 'number': 119}
- Question: {'precision': 0.44804716285924834, 'recall': 0.5708920187793427, 'f1': 0.5020644095788603, 'number': 1065}
- Overall Precision: 0.3826
- Overall Recall: 0.5013
- Overall F1: 0.4340
- Overall Accuracy: 0.5793
## 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: 32
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7974 | 1.0 | 5 | 1.6082 | {'precision': 0.015957446808510637, 'recall': 0.003708281829419036, 'f1': 0.006018054162487462, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.13218390804597702, 'recall': 0.0215962441314554, 'f1': 0.037126715092816794, 'number': 1065} | 0.0718 | 0.0130 | 0.0221 | 0.2950 |
| 1.6031 | 2.0 | 10 | 1.4809 | {'precision': 0.09702549575070822, 'recall': 0.16934487021013597, 'f1': 0.12336785231877535, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2448499117127722, 'recall': 0.39061032863849765, 'f1': 0.301013024602026, 'number': 1065} | 0.1778 | 0.2775 | 0.2167 | 0.3926 |
| 1.4415 | 3.0 | 15 | 1.3965 | {'precision': 0.15503875968992248, 'recall': 0.32138442521631644, 'f1': 0.20917135961383748, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.25329341317365267, 'recall': 0.3971830985915493, 'f1': 0.3093235831809872, 'number': 1065} | 0.2041 | 0.3427 | 0.2558 | 0.4162 |
| 1.3417 | 4.0 | 20 | 1.2882 | {'precision': 0.1925233644859813, 'recall': 0.3819530284301607, 'f1': 0.25600662800331403, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2921832884097035, 'recall': 0.5089201877934272, 'f1': 0.3712328767123288, 'number': 1065} | 0.2457 | 0.4270 | 0.3120 | 0.4305 |
| 1.2673 | 5.0 | 25 | 1.2461 | {'precision': 0.2402555910543131, 'recall': 0.4647713226205192, 'f1': 0.3167649536647009, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.3362183754993342, 'recall': 0.47417840375586856, 'f1': 0.39345539540319435, 'number': 1065} | 0.2828 | 0.4420 | 0.3449 | 0.4621 |
| 1.1953 | 6.0 | 30 | 1.1667 | {'precision': 0.2396469789545146, 'recall': 0.4363411619283066, 'f1': 0.30937773882559155, 'number': 809} | {'precision': 0.1038961038961039, 'recall': 0.06722689075630252, 'f1': 0.08163265306122448, 'number': 119} | {'precision': 0.34711246200607904, 'recall': 0.536150234741784, 'f1': 0.42140221402214023, 'number': 1065} | 0.2917 | 0.4676 | 0.3593 | 0.5048 |
| 1.1257 | 7.0 | 35 | 1.1238 | {'precision': 0.271211022480058, 'recall': 0.4622991347342398, 'f1': 0.34186471663619744, 'number': 809} | {'precision': 0.17708333333333334, 'recall': 0.14285714285714285, 'f1': 0.15813953488372096, 'number': 119} | {'precision': 0.38812154696132595, 'recall': 0.5276995305164319, 'f1': 0.4472741742936729, 'number': 1065} | 0.3260 | 0.4782 | 0.3877 | 0.5539 |
| 1.0703 | 8.0 | 40 | 1.0882 | {'precision': 0.2758340113913751, 'recall': 0.41903584672435107, 'f1': 0.33267909715407257, 'number': 809} | {'precision': 0.1919191919191919, 'recall': 0.15966386554621848, 'f1': 0.17431192660550457, 'number': 119} | {'precision': 0.4045307443365696, 'recall': 0.5868544600938967, 'f1': 0.47892720306513414, 'number': 1065} | 0.3422 | 0.4932 | 0.4040 | 0.5809 |
| 1.0172 | 9.0 | 45 | 1.0768 | {'precision': 0.277602523659306, 'recall': 0.43510506798516685, 'f1': 0.3389504092441021, 'number': 809} | {'precision': 0.24096385542168675, 'recall': 0.16806722689075632, 'f1': 0.19801980198019803, 'number': 119} | {'precision': 0.40967092008059103, 'recall': 0.5727699530516432, 'f1': 0.4776820673453407, 'number': 1065} | 0.3458 | 0.4927 | 0.4064 | 0.5803 |
| 0.9713 | 10.0 | 50 | 1.0884 | {'precision': 0.3041700735895339, 'recall': 0.45982694684796044, 'f1': 0.3661417322834645, 'number': 809} | {'precision': 0.2631578947368421, 'recall': 0.16806722689075632, 'f1': 0.20512820512820512, 'number': 119} | {'precision': 0.4506024096385542, 'recall': 0.5267605633802817, 'f1': 0.4857142857142857, 'number': 1065} | 0.3746 | 0.4782 | 0.4201 | 0.5781 |
| 0.9434 | 11.0 | 55 | 1.1220 | {'precision': 0.29082426127527217, 'recall': 0.4622991347342398, 'f1': 0.35704057279236273, 'number': 809} | {'precision': 0.2727272727272727, 'recall': 0.17647058823529413, 'f1': 0.21428571428571427, 'number': 119} | {'precision': 0.4404934687953556, 'recall': 0.5699530516431925, 'f1': 0.4969300040933278, 'number': 1065} | 0.3656 | 0.5028 | 0.4233 | 0.5669 |
| 0.9288 | 12.0 | 60 | 1.0876 | {'precision': 0.298372513562387, 'recall': 0.4079110012360939, 'f1': 0.34464751958224543, 'number': 809} | {'precision': 0.23958333333333334, 'recall': 0.19327731092436976, 'f1': 0.21395348837209302, 'number': 119} | {'precision': 0.4299933642999336, 'recall': 0.6084507042253521, 'f1': 0.5038880248833593, 'number': 1065} | 0.3695 | 0.5023 | 0.4258 | 0.5784 |
| 0.9043 | 13.0 | 65 | 1.1185 | {'precision': 0.31703204047217537, 'recall': 0.4647713226205192, 'f1': 0.3769423558897243, 'number': 809} | {'precision': 0.2894736842105263, 'recall': 0.18487394957983194, 'f1': 0.22564102564102564, 'number': 119} | {'precision': 0.4605263157894737, 'recall': 0.5258215962441315, 'f1': 0.49101271372205174, 'number': 1065} | 0.3866 | 0.4807 | 0.4285 | 0.5679 |
| 0.8884 | 14.0 | 70 | 1.1097 | {'precision': 0.31260364842454397, 'recall': 0.46600741656365885, 'f1': 0.37419354838709684, 'number': 809} | {'precision': 0.29333333333333333, 'recall': 0.18487394957983194, 'f1': 0.2268041237113402, 'number': 119} | {'precision': 0.4597791798107255, 'recall': 0.5474178403755868, 'f1': 0.4997856836690956, 'number': 1065} | 0.3852 | 0.4927 | 0.4324 | 0.5710 |
| 0.8759 | 15.0 | 75 | 1.0844 | {'precision': 0.3143100511073254, 'recall': 0.4561186650185414, 'f1': 0.3721633888048412, 'number': 809} | {'precision': 0.275, 'recall': 0.18487394957983194, 'f1': 0.22110552763819097, 'number': 119} | {'precision': 0.44804716285924834, 'recall': 0.5708920187793427, 'f1': 0.5020644095788603, 'number': 1065} | 0.3826 | 0.5013 | 0.4340 | 0.5793 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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