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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: 1.0534
- Answer: {'precision': 0.38023152270703475, 'recall': 0.5278121137206427, 'f1': 0.44202898550724634, 'number': 809}
- Header: {'precision': 0.3333333333333333, 'recall': 0.24369747899159663, 'f1': 0.2815533980582524, 'number': 119}
- Question: {'precision': 0.5214341387373344, 'recall': 0.6281690140845071, 'f1': 0.5698466780238501, 'number': 1065}
- Overall Precision: 0.4513
- Overall Recall: 0.5645
- Overall F1: 0.5016
- Overall Accuracy: 0.6341
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7733 | 1.0 | 10 | 1.5779 | {'precision': 0.03243847874720358, 'recall': 0.03584672435105068, 'f1': 0.03405754550792719, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2723926380368098, 'recall': 0.2084507042253521, 'f1': 0.23617021276595745, 'number': 1065} | 0.1469 | 0.1259 | 0.1356 | 0.3498 |
| 1.4958 | 2.0 | 20 | 1.3947 | {'precision': 0.15568475452196381, 'recall': 0.2978986402966625, 'f1': 0.20449724225710647, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.24971493728620298, 'recall': 0.4112676056338028, 'f1': 0.310748492373182, 'number': 1065} | 0.2047 | 0.3407 | 0.2557 | 0.4093 |
| 1.32 | 3.0 | 30 | 1.2259 | {'precision': 0.2251798561151079, 'recall': 0.3868974042027194, 'f1': 0.28467485220554795, 'number': 809} | {'precision': 0.09090909090909091, 'recall': 0.05042016806722689, 'f1': 0.06486486486486487, 'number': 119} | {'precision': 0.3336864406779661, 'recall': 0.5915492957746479, 'f1': 0.4266847273958686, 'number': 1065} | 0.2838 | 0.4762 | 0.3556 | 0.4708 |
| 1.1874 | 4.0 | 40 | 1.1299 | {'precision': 0.25460992907801416, 'recall': 0.4437577255871446, 'f1': 0.3235691753041911, 'number': 809} | {'precision': 0.30864197530864196, 'recall': 0.21008403361344538, 'f1': 0.25, 'number': 119} | {'precision': 0.3852813852813853, 'recall': 0.5849765258215962, 'f1': 0.4645786726323639, 'number': 1065} | 0.3240 | 0.5053 | 0.3948 | 0.5607 |
| 1.079 | 5.0 | 50 | 1.0967 | {'precision': 0.28809523809523807, 'recall': 0.44870210135970334, 'f1': 0.35089415176413724, 'number': 809} | {'precision': 0.3170731707317073, 'recall': 0.2184873949579832, 'f1': 0.25870646766169153, 'number': 119} | {'precision': 0.4067073170731707, 'recall': 0.6262910798122066, 'f1': 0.4931608133086876, 'number': 1065} | 0.3541 | 0.5299 | 0.4245 | 0.5684 |
| 1.0153 | 6.0 | 60 | 1.0661 | {'precision': 0.32075471698113206, 'recall': 0.5043263288009888, 'f1': 0.39211917347429115, 'number': 809} | {'precision': 0.33783783783783783, 'recall': 0.21008403361344538, 'f1': 0.25906735751295334, 'number': 119} | {'precision': 0.5031055900621118, 'recall': 0.532394366197183, 'f1': 0.5173357664233575, 'number': 1065} | 0.4044 | 0.5018 | 0.4478 | 0.5887 |
| 0.9487 | 7.0 | 70 | 1.0371 | {'precision': 0.3273753527751646, 'recall': 0.43016069221260816, 'f1': 0.37179487179487175, 'number': 809} | {'precision': 0.28440366972477066, 'recall': 0.2605042016806723, 'f1': 0.2719298245614035, 'number': 119} | {'precision': 0.44015696533682147, 'recall': 0.631924882629108, 'f1': 0.5188897455666924, 'number': 1065} | 0.3895 | 0.5278 | 0.4482 | 0.5965 |
| 0.8939 | 8.0 | 80 | 1.0279 | {'precision': 0.3353711790393013, 'recall': 0.4746600741656366, 'f1': 0.39303991811668376, 'number': 809} | {'precision': 0.4166666666666667, 'recall': 0.21008403361344538, 'f1': 0.2793296089385475, 'number': 119} | {'precision': 0.4401008827238335, 'recall': 0.6553990610328638, 'f1': 0.5265937382119954, 'number': 1065} | 0.3966 | 0.5554 | 0.4628 | 0.6073 |
| 0.8226 | 9.0 | 90 | 1.0434 | {'precision': 0.36496980155306297, 'recall': 0.522867737948084, 'f1': 0.4298780487804878, 'number': 809} | {'precision': 0.2765957446808511, 'recall': 0.2184873949579832, 'f1': 0.24413145539906103, 'number': 119} | {'precision': 0.524451939291737, 'recall': 0.584037558685446, 'f1': 0.5526432696579298, 'number': 1065} | 0.4391 | 0.5374 | 0.4833 | 0.6047 |
| 0.8109 | 10.0 | 100 | 1.0504 | {'precision': 0.3830755232029117, 'recall': 0.5203955500618047, 'f1': 0.44129979035639416, 'number': 809} | {'precision': 0.3258426966292135, 'recall': 0.24369747899159663, 'f1': 0.27884615384615385, 'number': 119} | {'precision': 0.5186104218362283, 'recall': 0.5887323943661972, 'f1': 0.5514511873350924, 'number': 1065} | 0.4493 | 0.5404 | 0.4907 | 0.6087 |
| 0.7313 | 11.0 | 110 | 1.0353 | {'precision': 0.35545454545454547, 'recall': 0.48331273176761436, 'f1': 0.4096385542168675, 'number': 809} | {'precision': 0.34615384615384615, 'recall': 0.226890756302521, 'f1': 0.27411167512690354, 'number': 119} | {'precision': 0.486411149825784, 'recall': 0.6553990610328638, 'f1': 0.5584, 'number': 1065} | 0.4271 | 0.5600 | 0.4846 | 0.6283 |
| 0.7183 | 12.0 | 120 | 1.0649 | {'precision': 0.3668639053254438, 'recall': 0.5364647713226205, 'f1': 0.43574297188755023, 'number': 809} | {'precision': 0.35802469135802467, 'recall': 0.24369747899159663, 'f1': 0.29000000000000004, 'number': 119} | {'precision': 0.5118483412322274, 'recall': 0.6084507042253521, 'f1': 0.5559845559845559, 'number': 1065} | 0.4391 | 0.5575 | 0.4913 | 0.6293 |
| 0.6865 | 13.0 | 130 | 1.0692 | {'precision': 0.37521514629948366, 'recall': 0.5389369592088998, 'f1': 0.44241501775748354, 'number': 809} | {'precision': 0.38461538461538464, 'recall': 0.25210084033613445, 'f1': 0.30456852791878175, 'number': 119} | {'precision': 0.5404255319148936, 'recall': 0.596244131455399, 'f1': 0.5669642857142857, 'number': 1065} | 0.4559 | 0.5524 | 0.4995 | 0.6258 |
| 0.6566 | 14.0 | 140 | 1.0435 | {'precision': 0.3845446182152714, 'recall': 0.5166872682323856, 'f1': 0.4409282700421941, 'number': 809} | {'precision': 0.3488372093023256, 'recall': 0.25210084033613445, 'f1': 0.2926829268292683, 'number': 119} | {'precision': 0.5181747873163186, 'recall': 0.6291079812206573, 'f1': 0.568278201865988, 'number': 1065} | 0.4534 | 0.5610 | 0.5015 | 0.6295 |
| 0.6437 | 15.0 | 150 | 1.0534 | {'precision': 0.38023152270703475, 'recall': 0.5278121137206427, 'f1': 0.44202898550724634, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.24369747899159663, 'f1': 0.2815533980582524, 'number': 119} | {'precision': 0.5214341387373344, 'recall': 0.6281690140845071, 'f1': 0.5698466780238501, 'number': 1065} | 0.4513 | 0.5645 | 0.5016 | 0.6341 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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