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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: 0.6771
- Answer: {'precision': 0.7107258938244854, 'recall': 0.8108776266996292, 'f1': 0.7575057736720554, 'number': 809}
- Header: {'precision': 0.3543307086614173, 'recall': 0.37815126050420167, 'f1': 0.3658536585365853, 'number': 119}
- Question: {'precision': 0.7716814159292036, 'recall': 0.8187793427230047, 'f1': 0.7945330296127562, 'number': 1065}
- Overall Precision: 0.7216
- Overall Recall: 0.7893
- Overall F1: 0.7539
- Overall Accuracy: 0.8139
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8027 | 1.0 | 10 | 1.5884 | {'precision': 0.01997780244173141, 'recall': 0.022249690976514216, 'f1': 0.02105263157894737, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.18858307849133538, 'recall': 0.17370892018779344, 'f1': 0.18084066471163246, 'number': 1065} | 0.1079 | 0.1019 | 0.1048 | 0.3753 |
| 1.4071 | 2.0 | 20 | 1.2076 | {'precision': 0.23890339425587467, 'recall': 0.22620519159456118, 'f1': 0.23238095238095238, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.41302791696492486, 'recall': 0.5417840375586854, 'f1': 0.4687246141348498, 'number': 1065} | 0.3512 | 0.3813 | 0.3656 | 0.5772 |
| 1.0593 | 3.0 | 30 | 0.9154 | {'precision': 0.4750542299349241, 'recall': 0.5414091470951793, 'f1': 0.5060658578856152, 'number': 809} | {'precision': 0.11363636363636363, 'recall': 0.04201680672268908, 'f1': 0.06134969325153375, 'number': 119} | {'precision': 0.5922493681550126, 'recall': 0.6600938967136151, 'f1': 0.6243339253996447, 'number': 1065} | 0.5323 | 0.5750 | 0.5528 | 0.7136 |
| 0.802 | 4.0 | 40 | 0.7552 | {'precision': 0.5981404958677686, 'recall': 0.715698393077874, 'f1': 0.6516601012943164, 'number': 809} | {'precision': 0.20253164556962025, 'recall': 0.13445378151260504, 'f1': 0.1616161616161616, 'number': 119} | {'precision': 0.6680707666385847, 'recall': 0.7446009389671362, 'f1': 0.7042628774422734, 'number': 1065} | 0.6213 | 0.6964 | 0.6567 | 0.7659 |
| 0.6561 | 5.0 | 50 | 0.7030 | {'precision': 0.6381856540084389, 'recall': 0.7478368355995055, 'f1': 0.6886738759248718, 'number': 809} | {'precision': 0.3, 'recall': 0.226890756302521, 'f1': 0.25837320574162675, 'number': 119} | {'precision': 0.6780766096169519, 'recall': 0.7812206572769953, 'f1': 0.7260034904013962, 'number': 1065} | 0.6464 | 0.7346 | 0.6876 | 0.7889 |
| 0.5591 | 6.0 | 60 | 0.6842 | {'precision': 0.6502100840336135, 'recall': 0.765142150803461, 'f1': 0.7030096536059057, 'number': 809} | {'precision': 0.3132530120481928, 'recall': 0.2184873949579832, 'f1': 0.25742574257425743, 'number': 119} | {'precision': 0.7165820642978004, 'recall': 0.7953051643192488, 'f1': 0.7538940809968847, 'number': 1065} | 0.6730 | 0.7486 | 0.7088 | 0.7942 |
| 0.4858 | 7.0 | 70 | 0.6508 | {'precision': 0.6569948186528497, 'recall': 0.7836835599505563, 'f1': 0.7147688838782412, 'number': 809} | {'precision': 0.34210526315789475, 'recall': 0.3277310924369748, 'f1': 0.33476394849785407, 'number': 119} | {'precision': 0.7205503009458297, 'recall': 0.7868544600938967, 'f1': 0.7522441651705565, 'number': 1065} | 0.6740 | 0.7582 | 0.7136 | 0.8063 |
| 0.431 | 8.0 | 80 | 0.6674 | {'precision': 0.6578140960163432, 'recall': 0.796044499381953, 'f1': 0.7203579418344519, 'number': 809} | {'precision': 0.35964912280701755, 'recall': 0.3445378151260504, 'f1': 0.351931330472103, 'number': 119} | {'precision': 0.7482517482517482, 'recall': 0.8037558685446009, 'f1': 0.775011317338162, 'number': 1065} | 0.6889 | 0.7732 | 0.7286 | 0.7969 |
| 0.3878 | 9.0 | 90 | 0.6526 | {'precision': 0.6787564766839378, 'recall': 0.8096415327564895, 'f1': 0.7384441939120632, 'number': 809} | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119} | {'precision': 0.7586206896551724, 'recall': 0.7849765258215963, 'f1': 0.7715736040609138, 'number': 1065} | 0.7014 | 0.7672 | 0.7328 | 0.8073 |
| 0.3744 | 10.0 | 100 | 0.6519 | {'precision': 0.6854410201912858, 'recall': 0.7972805933250927, 'f1': 0.7371428571428571, 'number': 809} | {'precision': 0.3130434782608696, 'recall': 0.3025210084033613, 'f1': 0.3076923076923077, 'number': 119} | {'precision': 0.7611940298507462, 'recall': 0.8140845070422535, 'f1': 0.7867513611615246, 'number': 1065} | 0.7052 | 0.7767 | 0.7393 | 0.8120 |
| 0.3161 | 11.0 | 110 | 0.6696 | {'precision': 0.6948257655755016, 'recall': 0.8133498145859085, 'f1': 0.7494305239179954, 'number': 809} | {'precision': 0.3283582089552239, 'recall': 0.3697478991596639, 'f1': 0.34782608695652173, 'number': 119} | {'precision': 0.7604166666666666, 'recall': 0.8225352112676056, 'f1': 0.7902571041948578, 'number': 1065} | 0.7067 | 0.7918 | 0.7468 | 0.8060 |
| 0.3039 | 12.0 | 120 | 0.6656 | {'precision': 0.7007534983853606, 'recall': 0.8046971569839307, 'f1': 0.7491369390103566, 'number': 809} | {'precision': 0.3524590163934426, 'recall': 0.36134453781512604, 'f1': 0.35684647302904565, 'number': 119} | {'precision': 0.7695769576957696, 'recall': 0.8028169014084507, 'f1': 0.7858455882352942, 'number': 1065} | 0.7165 | 0.7772 | 0.7456 | 0.8131 |
| 0.2877 | 13.0 | 130 | 0.6742 | {'precision': 0.6927138331573389, 'recall': 0.8108776266996292, 'f1': 0.7471526195899771, 'number': 809} | {'precision': 0.32592592592592595, 'recall': 0.3697478991596639, 'f1': 0.3464566929133859, 'number': 119} | {'precision': 0.7651715039577837, 'recall': 0.8169014084507042, 'f1': 0.7901907356948229, 'number': 1065} | 0.7075 | 0.7878 | 0.7455 | 0.8109 |
| 0.2681 | 14.0 | 140 | 0.6743 | {'precision': 0.7128927410617552, 'recall': 0.8133498145859085, 'f1': 0.7598152424942264, 'number': 809} | {'precision': 0.36220472440944884, 'recall': 0.3865546218487395, 'f1': 0.37398373983739847, 'number': 119} | {'precision': 0.7734513274336283, 'recall': 0.8206572769953052, 'f1': 0.7963553530751709, 'number': 1065} | 0.7239 | 0.7918 | 0.7563 | 0.8148 |
| 0.2609 | 15.0 | 150 | 0.6771 | {'precision': 0.7107258938244854, 'recall': 0.8108776266996292, 'f1': 0.7575057736720554, 'number': 809} | {'precision': 0.3543307086614173, 'recall': 0.37815126050420167, 'f1': 0.3658536585365853, 'number': 119} | {'precision': 0.7716814159292036, 'recall': 0.8187793427230047, 'f1': 0.7945330296127562, 'number': 1065} | 0.7216 | 0.7893 | 0.7539 | 0.8139 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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