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library_name: transformers
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: 0.7293
- Answer: {'precision': 0.7119205298013245, 'recall': 0.7972805933250927, 'f1': 0.7521865889212828, 'number': 809}
- Header: {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119}
- Question: {'precision': 0.7780701754385965, 'recall': 0.8328638497652582, 'f1': 0.8045351473922903, 'number': 1065}
- Overall Precision: 0.7237
- Overall Recall: 0.7873
- Overall F1: 0.7541
- Overall Accuracy: 0.7994
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8121 | 1.0 | 10 | 1.5778 | {'precision': 0.02838221381267739, 'recall': 0.037082818294190356, 'f1': 0.03215434083601286, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.19402985074626866, 'recall': 0.20751173708920187, 'f1': 0.20054446460980033, 'number': 1065} | 0.1143 | 0.1259 | 0.1198 | 0.4198 |
| 1.4301 | 2.0 | 20 | 1.2246 | {'precision': 0.15695067264573992, 'recall': 0.12978986402966625, 'f1': 0.14208389715832204, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.46811702925731435, 'recall': 0.5859154929577465, 'f1': 0.5204336947456214, 'number': 1065} | 0.3641 | 0.3658 | 0.3650 | 0.5873 |
| 1.0718 | 3.0 | 30 | 0.9296 | {'precision': 0.48633879781420764, 'recall': 0.5500618046971569, 'f1': 0.5162412993039442, 'number': 809} | {'precision': 0.029411764705882353, 'recall': 0.008403361344537815, 'f1': 0.013071895424836602, 'number': 119} | {'precision': 0.5914927768860353, 'recall': 0.692018779342723, 'f1': 0.6378191259195154, 'number': 1065} | 0.5390 | 0.5936 | 0.5649 | 0.7243 |
| 0.814 | 4.0 | 40 | 0.7678 | {'precision': 0.5895765472312704, 'recall': 0.6711990111248455, 'f1': 0.6277456647398845, 'number': 809} | {'precision': 0.1724137931034483, 'recall': 0.12605042016806722, 'f1': 0.14563106796116504, 'number': 119} | {'precision': 0.64, 'recall': 0.7661971830985915, 'f1': 0.6974358974358974, 'number': 1065} | 0.6018 | 0.6894 | 0.6427 | 0.7677 |
| 0.6527 | 5.0 | 50 | 0.7178 | {'precision': 0.6438653637350705, 'recall': 0.7330037082818294, 'f1': 0.6855491329479768, 'number': 809} | {'precision': 0.3, 'recall': 0.17647058823529413, 'f1': 0.22222222222222224, 'number': 119} | {'precision': 0.675, 'recall': 0.8112676056338028, 'f1': 0.7368869936034115, 'number': 1065} | 0.6508 | 0.7416 | 0.6932 | 0.7851 |
| 0.5605 | 6.0 | 60 | 0.6839 | {'precision': 0.6655982905982906, 'recall': 0.7700865265760197, 'f1': 0.7140401146131805, 'number': 809} | {'precision': 0.29411764705882354, 'recall': 0.21008403361344538, 'f1': 0.2450980392156863, 'number': 119} | {'precision': 0.7222222222222222, 'recall': 0.8178403755868544, 'f1': 0.7670629678555702, 'number': 1065} | 0.6821 | 0.7622 | 0.7199 | 0.7950 |
| 0.4793 | 7.0 | 70 | 0.6672 | {'precision': 0.6862955032119914, 'recall': 0.792336217552534, 'f1': 0.7355134825014343, 'number': 809} | {'precision': 0.25961538461538464, 'recall': 0.226890756302521, 'f1': 0.242152466367713, 'number': 119} | {'precision': 0.7552083333333334, 'recall': 0.8169014084507042, 'f1': 0.7848443843031124, 'number': 1065} | 0.7023 | 0.7717 | 0.7354 | 0.8014 |
| 0.4262 | 8.0 | 80 | 0.6747 | {'precision': 0.6762886597938145, 'recall': 0.8108776266996292, 'f1': 0.7374929735806633, 'number': 809} | {'precision': 0.24509803921568626, 'recall': 0.21008403361344538, 'f1': 0.22624434389140272, 'number': 119} | {'precision': 0.7618228718830611, 'recall': 0.831924882629108, 'f1': 0.7953321364452423, 'number': 1065} | 0.7011 | 0.7863 | 0.7412 | 0.8000 |
| 0.3773 | 9.0 | 90 | 0.6885 | {'precision': 0.6932314410480349, 'recall': 0.7849196538936959, 'f1': 0.736231884057971, 'number': 809} | {'precision': 0.30701754385964913, 'recall': 0.29411764705882354, 'f1': 0.30042918454935624, 'number': 119} | {'precision': 0.7628865979381443, 'recall': 0.8338028169014085, 'f1': 0.7967698519515478, 'number': 1065} | 0.7101 | 0.7817 | 0.7442 | 0.8015 |
| 0.3709 | 10.0 | 100 | 0.6915 | {'precision': 0.6982758620689655, 'recall': 0.8009888751545118, 'f1': 0.7461139896373058, 'number': 809} | {'precision': 0.3106796116504854, 'recall': 0.2689075630252101, 'f1': 0.28828828828828823, 'number': 119} | {'precision': 0.7681660899653979, 'recall': 0.8338028169014085, 'f1': 0.7996398018910401, 'number': 1065} | 0.7170 | 0.7868 | 0.7502 | 0.8041 |
| 0.3123 | 11.0 | 110 | 0.7102 | {'precision': 0.7045951859956237, 'recall': 0.796044499381953, 'f1': 0.7475333720255369, 'number': 809} | {'precision': 0.3, 'recall': 0.3025210084033613, 'f1': 0.301255230125523, 'number': 119} | {'precision': 0.7717013888888888, 'recall': 0.8347417840375587, 'f1': 0.8019846639603068, 'number': 1065} | 0.7177 | 0.7873 | 0.7509 | 0.8003 |
| 0.2944 | 12.0 | 120 | 0.7214 | {'precision': 0.7073707370737073, 'recall': 0.7948084054388134, 'f1': 0.7485448195576251, 'number': 809} | {'precision': 0.34285714285714286, 'recall': 0.3025210084033613, 'f1': 0.32142857142857145, 'number': 119} | {'precision': 0.774390243902439, 'recall': 0.8347417840375587, 'f1': 0.8034342521464076, 'number': 1065} | 0.7253 | 0.7868 | 0.7548 | 0.8031 |
| 0.286 | 13.0 | 130 | 0.7283 | {'precision': 0.7105263157894737, 'recall': 0.8009888751545118, 'f1': 0.7530505520046484, 'number': 809} | {'precision': 0.336283185840708, 'recall': 0.31932773109243695, 'f1': 0.32758620689655166, 'number': 119} | {'precision': 0.7801418439716312, 'recall': 0.8262910798122066, 'f1': 0.8025535795713634, 'number': 1065} | 0.7274 | 0.7858 | 0.7554 | 0.7994 |
| 0.2609 | 14.0 | 140 | 0.7260 | {'precision': 0.7144444444444444, 'recall': 0.7948084054388134, 'f1': 0.7524868344060853, 'number': 809} | {'precision': 0.3217391304347826, 'recall': 0.31092436974789917, 'f1': 0.3162393162393162, 'number': 119} | {'precision': 0.7796312554872695, 'recall': 0.8338028169014085, 'f1': 0.8058076225045372, 'number': 1065} | 0.7279 | 0.7868 | 0.7562 | 0.8000 |
| 0.264 | 15.0 | 150 | 0.7293 | {'precision': 0.7119205298013245, 'recall': 0.7972805933250927, 'f1': 0.7521865889212828, 'number': 809} | {'precision': 0.30327868852459017, 'recall': 0.31092436974789917, 'f1': 0.3070539419087137, 'number': 119} | {'precision': 0.7780701754385965, 'recall': 0.8328638497652582, 'f1': 0.8045351473922903, 'number': 1065} | 0.7237 | 0.7873 | 0.7541 | 0.7994 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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