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---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layout-lm
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. -->
# layout-lm
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: 0.6748
- Answer: {'precision': 0.7245575221238938, 'recall': 0.8096415327564895, 'f1': 0.7647402218330415, 'number': 809}
- Header: {'precision': 0.3464566929133858, 'recall': 0.3697478991596639, 'f1': 0.35772357723577236, 'number': 119}
- Question: {'precision': 0.7756183745583038, 'recall': 0.8244131455399061, 'f1': 0.7992717341829768, 'number': 1065}
- Overall Precision: 0.7291
- Overall Recall: 0.7913
- Overall F1: 0.7589
- Overall Accuracy: 0.8136
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7656 | 1.0 | 10 | 1.5482 | {'precision': 0.030390738060781478, 'recall': 0.02595797280593325, 'f1': 0.028, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.31988041853512705, 'recall': 0.20093896713615023, 'f1': 0.24682814302191464, 'number': 1065} | 0.1728 | 0.1179 | 0.1402 | 0.3707 |
| 1.4041 | 2.0 | 20 | 1.1664 | {'precision': 0.15247252747252749, 'recall': 0.13720642768850433, 'f1': 0.14443721535458687, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.46790409899458624, 'recall': 0.568075117370892, 'f1': 0.5131467345207804, 'number': 1065} | 0.3539 | 0.3593 | 0.3566 | 0.6164 |
| 1.0549 | 3.0 | 30 | 0.8895 | {'precision': 0.521044992743106, 'recall': 0.4437577255871446, 'f1': 0.479305740987984, 'number': 809} | {'precision': 0.25, 'recall': 0.08403361344537816, 'f1': 0.12578616352201258, 'number': 119} | {'precision': 0.5932336742722266, 'recall': 0.707981220657277, 'f1': 0.6455479452054794, 'number': 1065} | 0.5615 | 0.5635 | 0.5625 | 0.7226 |
| 0.8144 | 4.0 | 40 | 0.7445 | {'precision': 0.621978021978022, 'recall': 0.6996291718170581, 'f1': 0.658522396742292, 'number': 809} | {'precision': 0.2753623188405797, 'recall': 0.15966386554621848, 'f1': 0.20212765957446807, 'number': 119} | {'precision': 0.6641477749790092, 'recall': 0.7427230046948357, 'f1': 0.7012411347517731, 'number': 1065} | 0.6341 | 0.6904 | 0.6611 | 0.7620 |
| 0.6601 | 5.0 | 50 | 0.6786 | {'precision': 0.6608505997818975, 'recall': 0.7490729295426453, 'f1': 0.7022016222479722, 'number': 809} | {'precision': 0.34615384615384615, 'recall': 0.226890756302521, 'f1': 0.27411167512690354, 'number': 119} | {'precision': 0.6853932584269663, 'recall': 0.8018779342723005, 'f1': 0.7390739939420164, 'number': 1065} | 0.6635 | 0.7461 | 0.7024 | 0.7912 |
| 0.558 | 6.0 | 60 | 0.6751 | {'precision': 0.6495375128468653, 'recall': 0.7812113720642769, 'f1': 0.7093153759820426, 'number': 809} | {'precision': 0.34615384615384615, 'recall': 0.226890756302521, 'f1': 0.27411167512690354, 'number': 119} | {'precision': 0.7348017621145374, 'recall': 0.7830985915492957, 'f1': 0.7581818181818181, 'number': 1065} | 0.6830 | 0.7491 | 0.7145 | 0.7873 |
| 0.4876 | 7.0 | 70 | 0.6439 | {'precision': 0.6867469879518072, 'recall': 0.7750309023485785, 'f1': 0.7282229965156795, 'number': 809} | {'precision': 0.2672413793103448, 'recall': 0.2605042016806723, 'f1': 0.26382978723404255, 'number': 119} | {'precision': 0.735144312393888, 'recall': 0.8131455399061033, 'f1': 0.7721801159161837, 'number': 1065} | 0.6905 | 0.7647 | 0.7257 | 0.8059 |
| 0.431 | 8.0 | 80 | 0.6333 | {'precision': 0.7019650655021834, 'recall': 0.7948084054388134, 'f1': 0.7455072463768115, 'number': 809} | {'precision': 0.3157894736842105, 'recall': 0.3025210084033613, 'f1': 0.30901287553648066, 'number': 119} | {'precision': 0.7440878378378378, 'recall': 0.8272300469483568, 'f1': 0.7834593152512227, 'number': 1065} | 0.7046 | 0.7827 | 0.7416 | 0.8119 |
| 0.3849 | 9.0 | 90 | 0.6338 | {'precision': 0.713495575221239, 'recall': 0.7972805933250927, 'f1': 0.7530647985989491, 'number': 809} | {'precision': 0.3465346534653465, 'recall': 0.29411764705882354, 'f1': 0.3181818181818182, 'number': 119} | {'precision': 0.7697022767075307, 'recall': 0.8253521126760563, 'f1': 0.7965564114182148, 'number': 1065} | 0.7261 | 0.7822 | 0.7531 | 0.8189 |
| 0.3741 | 10.0 | 100 | 0.6533 | {'precision': 0.7054429028815368, 'recall': 0.8170580964153276, 'f1': 0.7571592210767468, 'number': 809} | {'precision': 0.31092436974789917, 'recall': 0.31092436974789917, 'f1': 0.31092436974789917, 'number': 119} | {'precision': 0.7736185383244206, 'recall': 0.8150234741784037, 'f1': 0.7937814357567444, 'number': 1065} | 0.7190 | 0.7858 | 0.7509 | 0.8133 |
| 0.3184 | 11.0 | 110 | 0.6556 | {'precision': 0.7065803667745415, 'recall': 0.8096415327564895, 'f1': 0.7546082949308756, 'number': 809} | {'precision': 0.3203125, 'recall': 0.3445378151260504, 'f1': 0.33198380566801616, 'number': 119} | {'precision': 0.7630901287553649, 'recall': 0.8347417840375587, 'f1': 0.7973094170403587, 'number': 1065} | 0.7140 | 0.7953 | 0.7524 | 0.8104 |
| 0.3038 | 12.0 | 120 | 0.6681 | {'precision': 0.72271714922049, 'recall': 0.8022249690976514, 'f1': 0.7603983596953721, 'number': 809} | {'precision': 0.3305084745762712, 'recall': 0.3277310924369748, 'f1': 0.32911392405063294, 'number': 119} | {'precision': 0.7851387645478961, 'recall': 0.8234741784037559, 'f1': 0.8038496791934006, 'number': 1065} | 0.7337 | 0.7852 | 0.7586 | 0.8155 |
| 0.2922 | 13.0 | 130 | 0.6667 | {'precision': 0.7233809001097695, 'recall': 0.8145859085290482, 'f1': 0.7662790697674419, 'number': 809} | {'precision': 0.36036036036036034, 'recall': 0.33613445378151263, 'f1': 0.34782608695652173, 'number': 119} | {'precision': 0.7810599478714162, 'recall': 0.844131455399061, 'f1': 0.8113718411552348, 'number': 1065} | 0.7354 | 0.8018 | 0.7672 | 0.8150 |
| 0.2685 | 14.0 | 140 | 0.6738 | {'precision': 0.7296996662958843, 'recall': 0.8108776266996292, 'f1': 0.7681498829039812, 'number': 809} | {'precision': 0.3384615384615385, 'recall': 0.3697478991596639, 'f1': 0.35341365461847385, 'number': 119} | {'precision': 0.7788546255506608, 'recall': 0.8300469483568075, 'f1': 0.8036363636363637, 'number': 1065} | 0.7320 | 0.7948 | 0.7621 | 0.8131 |
| 0.2668 | 15.0 | 150 | 0.6748 | {'precision': 0.7245575221238938, 'recall': 0.8096415327564895, 'f1': 0.7647402218330415, 'number': 809} | {'precision': 0.3464566929133858, 'recall': 0.3697478991596639, 'f1': 0.35772357723577236, 'number': 119} | {'precision': 0.7756183745583038, 'recall': 0.8244131455399061, 'f1': 0.7992717341829768, 'number': 1065} | 0.7291 | 0.7913 | 0.7589 | 0.8136 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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