---
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: 0.7158
- Answer: {'precision': 0.7149220489977728, 'recall': 0.7935723114956736, 'f1': 0.7521968365553603, 'number': 809}
- Header: {'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119}
- Question: {'precision': 0.7892857142857143, 'recall': 0.8300469483568075, 'f1': 0.8091533180778031, 'number': 1065}
- Overall Precision: 0.7327
- Overall Recall: 0.7827
- Overall F1: 0.7569
- Overall Accuracy: 0.8108
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8132 | 1.0 | 10 | 1.6191 | {'precision': 0.015122873345935728, 'recall': 0.019777503090234856, 'f1': 0.01713979646491698, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1685508735868448, 'recall': 0.1539906103286385, 'f1': 0.16094210009813542, 'number': 1065} | 0.0886 | 0.0903 | 0.0895 | 0.3534 |
| 1.4783 | 2.0 | 20 | 1.2483 | {'precision': 0.12857142857142856, 'recall': 0.12237330037082818, 'f1': 0.1253958201393287, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4541955350269438, 'recall': 0.5539906103286385, 'f1': 0.4991539763113368, 'number': 1065} | 0.3330 | 0.3457 | 0.3392 | 0.5682 |
| 1.1072 | 3.0 | 30 | 0.9718 | {'precision': 0.42777155655095184, 'recall': 0.4721878862793572, 'f1': 0.4488836662749706, 'number': 809} | {'precision': 0.04, 'recall': 0.008403361344537815, 'f1': 0.01388888888888889, 'number': 119} | {'precision': 0.6266205704407951, 'recall': 0.6807511737089202, 'f1': 0.6525652565256526, 'number': 1065} | 0.5340 | 0.5559 | 0.5447 | 0.7070 |
| 0.8444 | 4.0 | 40 | 0.7957 | {'precision': 0.6296296296296297, 'recall': 0.7354758961681088, 'f1': 0.6784492588369442, 'number': 809} | {'precision': 0.19230769230769232, 'recall': 0.08403361344537816, 'f1': 0.11695906432748539, 'number': 119} | {'precision': 0.6831168831168831, 'recall': 0.7408450704225352, 'f1': 0.7108108108108109, 'number': 1065} | 0.6478 | 0.6994 | 0.6726 | 0.7651 |
| 0.6845 | 5.0 | 50 | 0.7443 | {'precision': 0.6530612244897959, 'recall': 0.7515451174289246, 'f1': 0.6988505747126437, 'number': 809} | {'precision': 0.23684210526315788, 'recall': 0.15126050420168066, 'f1': 0.1846153846153846, 'number': 119} | {'precision': 0.7318181818181818, 'recall': 0.755868544600939, 'f1': 0.74364896073903, 'number': 1065} | 0.6792 | 0.7180 | 0.6980 | 0.7736 |
| 0.5597 | 6.0 | 60 | 0.6918 | {'precision': 0.6673706441393875, 'recall': 0.7812113720642769, 'f1': 0.7198177676537586, 'number': 809} | {'precision': 0.2857142857142857, 'recall': 0.15126050420168066, 'f1': 0.1978021978021978, 'number': 119} | {'precision': 0.7344150298889838, 'recall': 0.8075117370892019, 'f1': 0.7692307692307693, 'number': 1065} | 0.6923 | 0.7577 | 0.7235 | 0.7933 |
| 0.4929 | 7.0 | 70 | 0.6803 | {'precision': 0.6694825765575502, 'recall': 0.7836835599505563, 'f1': 0.7220956719817767, 'number': 809} | {'precision': 0.21818181818181817, 'recall': 0.20168067226890757, 'f1': 0.2096069868995633, 'number': 119} | {'precision': 0.7467134092900964, 'recall': 0.8, 'f1': 0.772438803263826, 'number': 1065} | 0.6870 | 0.7577 | 0.7206 | 0.7945 |
| 0.4447 | 8.0 | 80 | 0.6814 | {'precision': 0.6866158868335147, 'recall': 0.7799752781211372, 'f1': 0.7303240740740741, 'number': 809} | {'precision': 0.26506024096385544, 'recall': 0.18487394957983194, 'f1': 0.21782178217821785, 'number': 119} | {'precision': 0.7810283687943262, 'recall': 0.8272300469483568, 'f1': 0.8034655722754217, 'number': 1065} | 0.7202 | 0.7697 | 0.7441 | 0.8024 |
| 0.3953 | 9.0 | 90 | 0.6739 | {'precision': 0.7015765765765766, 'recall': 0.7700865265760197, 'f1': 0.7342368886269888, 'number': 809} | {'precision': 0.2920353982300885, 'recall': 0.2773109243697479, 'f1': 0.28448275862068967, 'number': 119} | {'precision': 0.7753496503496503, 'recall': 0.8328638497652582, 'f1': 0.8030783159800814, 'number': 1065} | 0.7193 | 0.7742 | 0.7458 | 0.8115 |
| 0.3538 | 10.0 | 100 | 0.6853 | {'precision': 0.7081497797356828, 'recall': 0.7948084054388134, 'f1': 0.7489807804309844, 'number': 809} | {'precision': 0.32673267326732675, 'recall': 0.2773109243697479, 'f1': 0.30000000000000004, 'number': 119} | {'precision': 0.7804878048780488, 'recall': 0.8413145539906103, 'f1': 0.8097605061003164, 'number': 1065} | 0.7288 | 0.7888 | 0.7576 | 0.8152 |
| 0.3262 | 11.0 | 110 | 0.6948 | {'precision': 0.7058177826564215, 'recall': 0.7948084054388134, 'f1': 0.7476744186046511, 'number': 809} | {'precision': 0.35051546391752575, 'recall': 0.2857142857142857, 'f1': 0.3148148148148148, 'number': 119} | {'precision': 0.8032638259292838, 'recall': 0.831924882629108, 'f1': 0.8173431734317343, 'number': 1065} | 0.7404 | 0.7842 | 0.7617 | 0.8129 |
| 0.3094 | 12.0 | 120 | 0.6989 | {'precision': 0.7128603104212861, 'recall': 0.7948084054388134, 'f1': 0.7516072472238456, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.29411764705882354, 'f1': 0.3125, 'number': 119} | {'precision': 0.7987364620938628, 'recall': 0.8309859154929577, 'f1': 0.8145421076852278, 'number': 1065} | 0.7390 | 0.7842 | 0.7610 | 0.8138 |
| 0.2941 | 13.0 | 130 | 0.7134 | {'precision': 0.7239819004524887, 'recall': 0.7911001236093943, 'f1': 0.7560543414057885, 'number': 809} | {'precision': 0.32710280373831774, 'recall': 0.29411764705882354, 'f1': 0.3097345132743363, 'number': 119} | {'precision': 0.7998204667863554, 'recall': 0.8366197183098592, 'f1': 0.8178063331803579, 'number': 1065} | 0.7439 | 0.7858 | 0.7643 | 0.8115 |
| 0.2813 | 14.0 | 140 | 0.7138 | {'precision': 0.7106710671067107, 'recall': 0.7985166872682324, 'f1': 0.7520372526193247, 'number': 809} | {'precision': 0.3119266055045872, 'recall': 0.2857142857142857, 'f1': 0.2982456140350877, 'number': 119} | {'precision': 0.7935656836461126, 'recall': 0.8338028169014085, 'f1': 0.8131868131868133, 'number': 1065} | 0.7337 | 0.7868 | 0.7593 | 0.8109 |
| 0.2812 | 15.0 | 150 | 0.7158 | {'precision': 0.7149220489977728, 'recall': 0.7935723114956736, 'f1': 0.7521968365553603, 'number': 809} | {'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119} | {'precision': 0.7892857142857143, 'recall': 0.8300469483568075, 'f1': 0.8091533180778031, 'number': 1065} | 0.7327 | 0.7827 | 0.7569 | 0.8108 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1