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library_name: transformers
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6947
- Answer: {'precision': 0.7250554323725056, 'recall': 0.8084054388133498, 'f1': 0.7644652250146114, 'number': 809}
- Header: {'precision': 0.30158730158730157, 'recall': 0.31932773109243695, 'f1': 0.310204081632653, 'number': 119}
- Question: {'precision': 0.767586821015138, 'recall': 0.8093896713615023, 'f1': 0.7879341864716636, 'number': 1065}
- Overall Precision: 0.7225
- Overall Recall: 0.7797
- Overall F1: 0.75
- Overall Accuracy: 0.8070
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.742 | 1.0 | 10 | 1.5266 | {'precision': 0.027950310559006212, 'recall': 0.03337453646477132, 'f1': 0.030422535211267605, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2287292817679558, 'recall': 0.19436619718309858, 'f1': 0.21015228426395938, 'number': 1065} | 0.1251 | 0.1174 | 0.1211 | 0.4247 |
| 1.412 | 2.0 | 20 | 1.2278 | {'precision': 0.19525801952580196, 'recall': 0.173053152039555, 'f1': 0.1834862385321101, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4582560296846011, 'recall': 0.463849765258216, 'f1': 0.4610359309379375, 'number': 1065} | 0.3532 | 0.3181 | 0.3347 | 0.5888 |
| 1.0962 | 3.0 | 30 | 0.9645 | {'precision': 0.4753157290470723, 'recall': 0.511742892459827, 'f1': 0.4928571428571428, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.6110183639398998, 'recall': 0.6873239436619718, 'f1': 0.6469288555015466, 'number': 1065} | 0.5478 | 0.5750 | 0.5611 | 0.7154 |
| 0.838 | 4.0 | 40 | 0.7924 | {'precision': 0.6248671625929861, 'recall': 0.7268232385661311, 'f1': 0.672, 'number': 809} | {'precision': 0.12698412698412698, 'recall': 0.06722689075630252, 'f1': 0.08791208791208792, 'number': 119} | {'precision': 0.6594863297431649, 'recall': 0.7474178403755869, 'f1': 0.7007042253521126, 'number': 1065} | 0.6296 | 0.6984 | 0.6622 | 0.7647 |
| 0.6636 | 5.0 | 50 | 0.7294 | {'precision': 0.6722037652270211, 'recall': 0.7503090234857849, 'f1': 0.7091121495327103, 'number': 809} | {'precision': 0.2077922077922078, 'recall': 0.13445378151260504, 'f1': 0.16326530612244897, 'number': 119} | {'precision': 0.6664086687306502, 'recall': 0.8084507042253521, 'f1': 0.7305897327110734, 'number': 1065} | 0.6532 | 0.7446 | 0.6959 | 0.7781 |
| 0.5632 | 6.0 | 60 | 0.6983 | {'precision': 0.660164271047228, 'recall': 0.7948084054388134, 'f1': 0.7212563095905777, 'number': 809} | {'precision': 0.21739130434782608, 'recall': 0.12605042016806722, 'f1': 0.1595744680851064, 'number': 119} | {'precision': 0.7283842794759825, 'recall': 0.7830985915492957, 'f1': 0.7547511312217194, 'number': 1065} | 0.6819 | 0.7486 | 0.7137 | 0.7905 |
| 0.4868 | 7.0 | 70 | 0.6635 | {'precision': 0.7008830022075055, 'recall': 0.7849196538936959, 'f1': 0.7405247813411079, 'number': 809} | {'precision': 0.25742574257425743, 'recall': 0.2184873949579832, 'f1': 0.23636363636363636, 'number': 119} | {'precision': 0.7467248908296943, 'recall': 0.8028169014084507, 'f1': 0.7737556561085973, 'number': 1065} | 0.7045 | 0.7607 | 0.7315 | 0.7993 |
| 0.4332 | 8.0 | 80 | 0.6626 | {'precision': 0.6882168925964547, 'recall': 0.8158220024721878, 'f1': 0.7466063348416289, 'number': 809} | {'precision': 0.2727272727272727, 'recall': 0.226890756302521, 'f1': 0.24770642201834864, 'number': 119} | {'precision': 0.7463456577815993, 'recall': 0.8150234741784037, 'f1': 0.7791741472172352, 'number': 1065} | 0.7001 | 0.7802 | 0.7380 | 0.7992 |
| 0.3853 | 9.0 | 90 | 0.6623 | {'precision': 0.7160220994475138, 'recall': 0.8009888751545118, 'f1': 0.7561260210035006, 'number': 809} | {'precision': 0.30927835051546393, 'recall': 0.25210084033613445, 'f1': 0.2777777777777778, 'number': 119} | {'precision': 0.753448275862069, 'recall': 0.8206572769953052, 'f1': 0.7856179775280899, 'number': 1065} | 0.7179 | 0.7787 | 0.7471 | 0.8031 |
| 0.3733 | 10.0 | 100 | 0.6695 | {'precision': 0.7180327868852459, 'recall': 0.8121137206427689, 'f1': 0.7621809744779582, 'number': 809} | {'precision': 0.28846153846153844, 'recall': 0.25210084033613445, 'f1': 0.26905829596412556, 'number': 119} | {'precision': 0.77068345323741, 'recall': 0.8046948356807512, 'f1': 0.7873220027560864, 'number': 1065} | 0.7245 | 0.7747 | 0.7488 | 0.8085 |
| 0.3201 | 11.0 | 110 | 0.6826 | {'precision': 0.7122381477398015, 'recall': 0.7985166872682324, 'f1': 0.752913752913753, 'number': 809} | {'precision': 0.32142857142857145, 'recall': 0.3025210084033613, 'f1': 0.3116883116883117, 'number': 119} | {'precision': 0.7510620220900595, 'recall': 0.8300469483568075, 'f1': 0.7885816235504014, 'number': 1065} | 0.7131 | 0.7858 | 0.7477 | 0.8048 |
| 0.3027 | 12.0 | 120 | 0.6841 | {'precision': 0.7213656387665198, 'recall': 0.8096415327564895, 'f1': 0.762958648806057, 'number': 809} | {'precision': 0.34210526315789475, 'recall': 0.3277310924369748, 'f1': 0.33476394849785407, 'number': 119} | {'precision': 0.7768744354110207, 'recall': 0.8075117370892019, 'f1': 0.7918968692449355, 'number': 1065} | 0.7299 | 0.7797 | 0.7540 | 0.8068 |
| 0.2902 | 13.0 | 130 | 0.6871 | {'precision': 0.7210065645514223, 'recall': 0.8145859085290482, 'f1': 0.7649448636099826, 'number': 809} | {'precision': 0.32142857142857145, 'recall': 0.3025210084033613, 'f1': 0.3116883116883117, 'number': 119} | {'precision': 0.7732506643046945, 'recall': 0.819718309859155, 'f1': 0.7958067456700091, 'number': 1065} | 0.7276 | 0.7868 | 0.7560 | 0.8073 |
| 0.2694 | 14.0 | 140 | 0.6911 | {'precision': 0.7197802197802198, 'recall': 0.8096415327564895, 'f1': 0.7620709714950552, 'number': 809} | {'precision': 0.32456140350877194, 'recall': 0.31092436974789917, 'f1': 0.31759656652360513, 'number': 119} | {'precision': 0.7796762589928058, 'recall': 0.8140845070422535, 'f1': 0.7965089572806615, 'number': 1065} | 0.7299 | 0.7822 | 0.7551 | 0.8083 |
| 0.2721 | 15.0 | 150 | 0.6947 | {'precision': 0.7250554323725056, 'recall': 0.8084054388133498, 'f1': 0.7644652250146114, 'number': 809} | {'precision': 0.30158730158730157, 'recall': 0.31932773109243695, 'f1': 0.310204081632653, 'number': 119} | {'precision': 0.767586821015138, 'recall': 0.8093896713615023, 'f1': 0.7879341864716636, 'number': 1065} | 0.7225 | 0.7797 | 0.75 | 0.8070 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Tokenizers 0.21.0
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