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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: 1.1103
- Answer: {'precision': 0.4171539961013645, 'recall': 0.5290482076637825, 'f1': 0.4664850136239782, 'number': 809}
- Header: {'precision': 0.26595744680851063, 'recall': 0.21008403361344538, 'f1': 0.23474178403755866, 'number': 119}
- Question: {'precision': 0.5105058365758754, 'recall': 0.615962441314554, 'f1': 0.5582978723404256, 'number': 1065}
- Overall Precision: 0.4611
- Overall Recall: 0.5564
- Overall F1: 0.5043
- Overall Accuracy: 0.6256
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7545 | 1.0 | 10 | 1.4910 | {'precision': 0.04744787922358016, 'recall': 0.0815822002472188, 'f1': 0.06000000000000001, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2316715542521994, 'recall': 0.29671361502347415, 'f1': 0.2601893783449979, 'number': 1065} | 0.1386 | 0.1917 | 0.1608 | 0.3843 |
| 1.4327 | 2.0 | 20 | 1.3684 | {'precision': 0.1908983451536643, 'recall': 0.3992583436341162, 'f1': 0.258296681327469, 'number': 809} | {'precision': 0.08333333333333333, 'recall': 0.01680672268907563, 'f1': 0.027972027972027972, 'number': 119} | {'precision': 0.2686771761480466, 'recall': 0.36807511737089205, 'f1': 0.3106180665610142, 'number': 1065} | 0.2258 | 0.3598 | 0.2775 | 0.4199 |
| 1.3 | 3.0 | 30 | 1.2336 | {'precision': 0.23386581469648562, 'recall': 0.45241038318912236, 'f1': 0.3083403538331929, 'number': 809} | {'precision': 0.23404255319148937, 'recall': 0.09243697478991597, 'f1': 0.13253012048192772, 'number': 119} | {'precision': 0.3207196029776675, 'recall': 0.48544600938967136, 'f1': 0.3862532685842361, 'number': 1065} | 0.2773 | 0.4486 | 0.3427 | 0.4777 |
| 1.1799 | 4.0 | 40 | 1.1284 | {'precision': 0.26886145404663925, 'recall': 0.484548825710754, 'f1': 0.3458314953683282, 'number': 809} | {'precision': 0.2903225806451613, 'recall': 0.226890756302521, 'f1': 0.25471698113207547, 'number': 119} | {'precision': 0.369108049311095, 'recall': 0.4779342723004695, 'f1': 0.41653027823240585, 'number': 1065} | 0.3167 | 0.4656 | 0.3770 | 0.5629 |
| 1.0681 | 5.0 | 50 | 1.1019 | {'precision': 0.2949346405228758, 'recall': 0.446229913473424, 'f1': 0.35514018691588783, 'number': 809} | {'precision': 0.3373493975903614, 'recall': 0.23529411764705882, 'f1': 0.2772277227722772, 'number': 119} | {'precision': 0.38892345986309895, 'recall': 0.5868544600938967, 'f1': 0.46781437125748504, 'number': 1065} | 0.3480 | 0.5088 | 0.4133 | 0.5724 |
| 0.9791 | 6.0 | 60 | 1.2060 | {'precision': 0.33286810886252616, 'recall': 0.5896168108776267, 'f1': 0.4255129348795718, 'number': 809} | {'precision': 0.4, 'recall': 0.20168067226890757, 'f1': 0.2681564245810056, 'number': 119} | {'precision': 0.45607476635514016, 'recall': 0.4582159624413146, 'f1': 0.45714285714285713, 'number': 1065} | 0.3859 | 0.4962 | 0.4342 | 0.5718 |
| 0.9138 | 7.0 | 70 | 1.0604 | {'precision': 0.37743589743589745, 'recall': 0.45488257107540175, 'f1': 0.4125560538116592, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.25210084033613445, 'f1': 0.28708133971291866, 'number': 119} | {'precision': 0.4469026548672566, 'recall': 0.5690140845070423, 'f1': 0.5006195786864932, 'number': 1065} | 0.4147 | 0.5038 | 0.4549 | 0.5983 |
| 0.8555 | 8.0 | 80 | 1.0361 | {'precision': 0.3559928443649374, 'recall': 0.4919653893695921, 'f1': 0.4130773222625843, 'number': 809} | {'precision': 0.3076923076923077, 'recall': 0.20168067226890757, 'f1': 0.2436548223350254, 'number': 119} | {'precision': 0.45045045045045046, 'recall': 0.6103286384976526, 'f1': 0.5183413078149921, 'number': 1065} | 0.4062 | 0.5379 | 0.4629 | 0.6104 |
| 0.8062 | 9.0 | 90 | 1.0676 | {'precision': 0.37511520737327186, 'recall': 0.5030902348578492, 'f1': 0.4297782470960929, 'number': 809} | {'precision': 0.31521739130434784, 'recall': 0.24369747899159663, 'f1': 0.27488151658767773, 'number': 119} | {'precision': 0.4796310530361261, 'recall': 0.5859154929577465, 'f1': 0.5274725274725274, 'number': 1065} | 0.4278 | 0.5319 | 0.4742 | 0.6094 |
| 0.7981 | 10.0 | 100 | 1.0901 | {'precision': 0.3904109589041096, 'recall': 0.4932014833127318, 'f1': 0.4358274167121791, 'number': 809} | {'precision': 0.3132530120481928, 'recall': 0.2184873949579832, 'f1': 0.25742574257425743, 'number': 119} | {'precision': 0.47112462006079026, 'recall': 0.5821596244131455, 'f1': 0.5207895842083158, 'number': 1065} | 0.4316 | 0.5243 | 0.4735 | 0.6113 |
| 0.7159 | 11.0 | 110 | 1.1141 | {'precision': 0.3889908256880734, 'recall': 0.5241038318912238, 'f1': 0.4465508162190627, 'number': 809} | {'precision': 0.26732673267326734, 'recall': 0.226890756302521, 'f1': 0.24545454545454548, 'number': 119} | {'precision': 0.5027844073190135, 'recall': 0.5934272300469483, 'f1': 0.5443583118001722, 'number': 1065} | 0.4424 | 0.5434 | 0.4877 | 0.6139 |
| 0.7242 | 12.0 | 120 | 1.0786 | {'precision': 0.39233576642335766, 'recall': 0.5315203955500618, 'f1': 0.4514435695538058, 'number': 809} | {'precision': 0.2926829268292683, 'recall': 0.20168067226890757, 'f1': 0.23880597014925373, 'number': 119} | {'precision': 0.5096674400618716, 'recall': 0.6187793427230047, 'f1': 0.5589482612383375, 'number': 1065} | 0.4504 | 0.5585 | 0.4987 | 0.6172 |
| 0.6895 | 13.0 | 130 | 1.1184 | {'precision': 0.4066427289048474, 'recall': 0.5599505562422744, 'f1': 0.4711388455538222, 'number': 809} | {'precision': 0.2696629213483146, 'recall': 0.20168067226890757, 'f1': 0.23076923076923078, 'number': 119} | {'precision': 0.5230125523012552, 'recall': 0.5868544600938967, 'f1': 0.5530973451327434, 'number': 1065} | 0.4595 | 0.5529 | 0.5019 | 0.6134 |
| 0.6605 | 14.0 | 140 | 1.1015 | {'precision': 0.4114737883283877, 'recall': 0.5142150803461063, 'f1': 0.45714285714285713, 'number': 809} | {'precision': 0.2631578947368421, 'recall': 0.21008403361344538, 'f1': 0.23364485981308414, 'number': 119} | {'precision': 0.5068702290076336, 'recall': 0.6234741784037559, 'f1': 0.5591578947368421, 'number': 1065} | 0.4574 | 0.5544 | 0.5012 | 0.6242 |
| 0.6498 | 15.0 | 150 | 1.1103 | {'precision': 0.4171539961013645, 'recall': 0.5290482076637825, 'f1': 0.4664850136239782, 'number': 809} | {'precision': 0.26595744680851063, 'recall': 0.21008403361344538, 'f1': 0.23474178403755866, 'number': 119} | {'precision': 0.5105058365758754, 'recall': 0.615962441314554, 'f1': 0.5582978723404256, 'number': 1065} | 0.4611 | 0.5564 | 0.5043 | 0.6256 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
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