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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: 1.2144
- Answer: {'precision': 0.46146146146146144, 'recall': 0.5698393077873919, 'f1': 0.5099557522123893, 'number': 809}
- Header: {'precision': 0.4024390243902439, 'recall': 0.2773109243697479, 'f1': 0.3283582089552239, 'number': 119}
- Question: {'precision': 0.5888412017167381, 'recall': 0.644131455399061, 'f1': 0.6152466367713004, 'number': 1065}
- Overall Precision: 0.5254
- Overall Recall: 0.5921
- Overall F1: 0.5567
- Overall Accuracy: 0.6483
## 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: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.5615 | 1.0 | 38 | 1.2309 | {'precision': 0.23910171730515192, 'recall': 0.44746600741656367, 'f1': 0.311665949203616, 'number': 809} | {'precision': 0.2830188679245283, 'recall': 0.12605042016806722, 'f1': 0.1744186046511628, 'number': 119} | {'precision': 0.35969209237228833, 'recall': 0.48262910798122066, 'f1': 0.4121892542101042, 'number': 1065} | 0.2974 | 0.4471 | 0.3572 | 0.4649 |
| 1.1729 | 2.0 | 76 | 1.0880 | {'precision': 0.3109656301145663, 'recall': 0.46971569839307786, 'f1': 0.37419990152634175, 'number': 809} | {'precision': 0.423728813559322, 'recall': 0.21008403361344538, 'f1': 0.2808988764044944, 'number': 119} | {'precision': 0.507488986784141, 'recall': 0.5408450704225352, 'f1': 0.5236363636363637, 'number': 1065} | 0.4060 | 0.4922 | 0.4450 | 0.5557 |
| 1.0126 | 3.0 | 114 | 1.0622 | {'precision': 0.31921110299488675, 'recall': 0.5401730531520396, 'f1': 0.40128558310376494, 'number': 809} | {'precision': 0.38372093023255816, 'recall': 0.2773109243697479, 'f1': 0.32195121951219513, 'number': 119} | {'precision': 0.4930662557781202, 'recall': 0.6009389671361502, 'f1': 0.5416842996191282, 'number': 1065} | 0.4032 | 0.5569 | 0.4678 | 0.5662 |
| 0.9042 | 4.0 | 152 | 1.0144 | {'precision': 0.3859060402684564, 'recall': 0.5686032138442522, 'f1': 0.45977011494252873, 'number': 809} | {'precision': 0.312, 'recall': 0.3277310924369748, 'f1': 0.31967213114754095, 'number': 119} | {'precision': 0.542713567839196, 'recall': 0.6084507042253521, 'f1': 0.5737051792828685, 'number': 1065} | 0.4568 | 0.5755 | 0.5093 | 0.6204 |
| 0.7609 | 5.0 | 190 | 1.0307 | {'precision': 0.41357466063348414, 'recall': 0.5648949320148331, 'f1': 0.47753396029258094, 'number': 809} | {'precision': 0.3373493975903614, 'recall': 0.23529411764705882, 'f1': 0.2772277227722772, 'number': 119} | {'precision': 0.5755711775043937, 'recall': 0.6150234741784038, 'f1': 0.5946436677258284, 'number': 1065} | 0.4901 | 0.5720 | 0.5279 | 0.6340 |
| 0.6792 | 6.0 | 228 | 1.0643 | {'precision': 0.43541102077687444, 'recall': 0.595797280593325, 'f1': 0.5031315240083507, 'number': 809} | {'precision': 0.4142857142857143, 'recall': 0.24369747899159663, 'f1': 0.3068783068783069, 'number': 119} | {'precision': 0.5757575757575758, 'recall': 0.6065727699530516, 'f1': 0.5907636031092821, 'number': 1065} | 0.5033 | 0.5805 | 0.5391 | 0.6180 |
| 0.6081 | 7.0 | 266 | 1.0222 | {'precision': 0.4691780821917808, 'recall': 0.5080346106304079, 'f1': 0.4878338278931751, 'number': 809} | {'precision': 0.26618705035971224, 'recall': 0.31092436974789917, 'f1': 0.2868217054263566, 'number': 119} | {'precision': 0.5478056426332288, 'recall': 0.6563380281690141, 'f1': 0.5971806920119608, 'number': 1065} | 0.5007 | 0.5755 | 0.5355 | 0.6424 |
| 0.5218 | 8.0 | 304 | 1.0641 | {'precision': 0.42940038684719534, 'recall': 0.5488257107540173, 'f1': 0.481823114487249, 'number': 809} | {'precision': 0.3409090909090909, 'recall': 0.25210084033613445, 'f1': 0.2898550724637681, 'number': 119} | {'precision': 0.5291512915129152, 'recall': 0.6732394366197183, 'f1': 0.5925619834710744, 'number': 1065} | 0.4808 | 0.5976 | 0.5329 | 0.6167 |
| 0.468 | 9.0 | 342 | 1.1145 | {'precision': 0.4584942084942085, 'recall': 0.5871446229913473, 'f1': 0.5149051490514905, 'number': 809} | {'precision': 0.3924050632911392, 'recall': 0.2605042016806723, 'f1': 0.31313131313131315, 'number': 119} | {'precision': 0.5921501706484642, 'recall': 0.6516431924882629, 'f1': 0.6204738489047832, 'number': 1065} | 0.5247 | 0.6021 | 0.5607 | 0.6527 |
| 0.4159 | 10.0 | 380 | 1.1606 | {'precision': 0.4683281412253375, 'recall': 0.5574783683559951, 'f1': 0.5090293453724605, 'number': 809} | {'precision': 0.367816091954023, 'recall': 0.2689075630252101, 'f1': 0.31067961165048547, 'number': 119} | {'precision': 0.5958369470945359, 'recall': 0.6450704225352113, 'f1': 0.6194770063119928, 'number': 1065} | 0.5311 | 0.5871 | 0.5577 | 0.6521 |
| 0.3764 | 11.0 | 418 | 1.2370 | {'precision': 0.4515828677839851, 'recall': 0.5995055624227441, 'f1': 0.5151354221986192, 'number': 809} | {'precision': 0.3888888888888889, 'recall': 0.29411764705882354, 'f1': 0.3349282296650718, 'number': 119} | {'precision': 0.6041083099906629, 'recall': 0.6075117370892019, 'f1': 0.6058052434456929, 'number': 1065} | 0.5221 | 0.5855 | 0.5520 | 0.6248 |
| 0.3393 | 12.0 | 456 | 1.2263 | {'precision': 0.46161515453639085, 'recall': 0.5723114956736712, 'f1': 0.5110375275938189, 'number': 809} | {'precision': 0.35135135135135137, 'recall': 0.2184873949579832, 'f1': 0.26943005181347146, 'number': 119} | {'precision': 0.5891132572431957, 'recall': 0.6300469483568075, 'f1': 0.6088929219600726, 'number': 1065} | 0.5235 | 0.5820 | 0.5512 | 0.6369 |
| 0.3253 | 13.0 | 494 | 1.2059 | {'precision': 0.4658590308370044, 'recall': 0.522867737948084, 'f1': 0.49271986022131625, 'number': 809} | {'precision': 0.3402061855670103, 'recall': 0.2773109243697479, 'f1': 0.3055555555555556, 'number': 119} | {'precision': 0.5623028391167192, 'recall': 0.6694835680751173, 'f1': 0.6112301757393913, 'number': 1065} | 0.5143 | 0.5866 | 0.5481 | 0.6376 |
| 0.2996 | 14.0 | 532 | 1.2311 | {'precision': 0.46296296296296297, 'recall': 0.5871446229913473, 'f1': 0.5177111716621253, 'number': 809} | {'precision': 0.3263157894736842, 'recall': 0.2605042016806723, 'f1': 0.2897196261682243, 'number': 119} | {'precision': 0.5991189427312775, 'recall': 0.6384976525821596, 'f1': 0.6181818181818182, 'number': 1065} | 0.5257 | 0.5951 | 0.5582 | 0.6350 |
| 0.2892 | 15.0 | 570 | 1.2144 | {'precision': 0.46146146146146144, 'recall': 0.5698393077873919, 'f1': 0.5099557522123893, 'number': 809} | {'precision': 0.4024390243902439, 'recall': 0.2773109243697479, 'f1': 0.3283582089552239, 'number': 119} | {'precision': 0.5888412017167381, 'recall': 0.644131455399061, 'f1': 0.6152466367713004, 'number': 1065} | 0.5254 | 0.5921 | 0.5567 | 0.6483 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
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