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---
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: 0.6675
- Answer: {'precision': 0.7104972375690608, 'recall': 0.7948084054388134, 'f1': 0.750291715285881, 'number': 809}
- Header: {'precision': 0.2892561983471074, 'recall': 0.29411764705882354, 'f1': 0.2916666666666667, 'number': 119}
- Question: {'precision': 0.7677642980935875, 'recall': 0.831924882629108, 'f1': 0.7985579089680036, 'number': 1065}
- Overall Precision: 0.7174
- Overall Recall: 0.7847
- Overall F1: 0.7496
- Overall Accuracy: 0.8194
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7901 | 1.0 | 10 | 1.6070 | {'precision': 0.019525801952580194, 'recall': 0.0173053152039555, 'f1': 0.018348623853211007, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2396486825595985, 'recall': 0.17934272300469484, 'f1': 0.20515574650912996, 'number': 1065} | 0.1354 | 0.1029 | 0.1169 | 0.3392 |
| 1.4547 | 2.0 | 20 | 1.2498 | {'precision': 0.21739130434782608, 'recall': 0.22249690976514216, 'f1': 0.21991447770311545, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.464573268921095, 'recall': 0.5417840375586854, 'f1': 0.5002167316861725, 'number': 1065} | 0.3655 | 0.3798 | 0.3725 | 0.5784 |
| 1.0779 | 3.0 | 30 | 0.9620 | {'precision': 0.46195652173913043, 'recall': 0.42027194066749074, 'f1': 0.4401294498381877, 'number': 809} | {'precision': 0.05405405405405406, 'recall': 0.01680672268907563, 'f1': 0.02564102564102564, 'number': 119} | {'precision': 0.631484794275492, 'recall': 0.6629107981220658, 'f1': 0.6468163078332569, 'number': 1065} | 0.5542 | 0.5258 | 0.5396 | 0.6890 |
| 0.8184 | 4.0 | 40 | 0.7715 | {'precision': 0.624868282402529, 'recall': 0.7330037082818294, 'f1': 0.6746302616609784, 'number': 809} | {'precision': 0.1875, 'recall': 0.10084033613445378, 'f1': 0.1311475409836066, 'number': 119} | {'precision': 0.6657963446475196, 'recall': 0.7183098591549296, 'f1': 0.6910569105691057, 'number': 1065} | 0.6337 | 0.6874 | 0.6594 | 0.7596 |
| 0.6687 | 5.0 | 50 | 0.6994 | {'precision': 0.6322778345250255, 'recall': 0.765142150803461, 'f1': 0.692393736017897, 'number': 809} | {'precision': 0.2857142857142857, 'recall': 0.20168067226890757, 'f1': 0.23645320197044337, 'number': 119} | {'precision': 0.7097902097902098, 'recall': 0.7624413145539906, 'f1': 0.7351742870076958, 'number': 1065} | 0.6593 | 0.7301 | 0.6929 | 0.7815 |
| 0.5553 | 6.0 | 60 | 0.6586 | {'precision': 0.6430769230769231, 'recall': 0.7750309023485785, 'f1': 0.702914798206278, 'number': 809} | {'precision': 0.325, 'recall': 0.2184873949579832, 'f1': 0.26130653266331655, 'number': 119} | {'precision': 0.70863599677159, 'recall': 0.8244131455399061, 'f1': 0.7621527777777778, 'number': 1065} | 0.6674 | 0.7682 | 0.7143 | 0.7961 |
| 0.4897 | 7.0 | 70 | 0.6659 | {'precision': 0.6706521739130434, 'recall': 0.7626699629171817, 'f1': 0.7137073452862926, 'number': 809} | {'precision': 0.26605504587155965, 'recall': 0.24369747899159663, 'f1': 0.2543859649122807, 'number': 119} | {'precision': 0.7519788918205804, 'recall': 0.8028169014084507, 'f1': 0.7765667574931879, 'number': 1065} | 0.6930 | 0.7531 | 0.7218 | 0.7944 |
| 0.4407 | 8.0 | 80 | 0.6417 | {'precision': 0.6666666666666666, 'recall': 0.7688504326328801, 'f1': 0.7141216991963261, 'number': 809} | {'precision': 0.2692307692307692, 'recall': 0.23529411764705882, 'f1': 0.25112107623318386, 'number': 119} | {'precision': 0.7383966244725738, 'recall': 0.8215962441314554, 'f1': 0.7777777777777778, 'number': 1065} | 0.6863 | 0.7652 | 0.7236 | 0.8050 |
| 0.3954 | 9.0 | 90 | 0.6419 | {'precision': 0.6933333333333334, 'recall': 0.7713226205191595, 'f1': 0.7302516091281451, 'number': 809} | {'precision': 0.2698412698412698, 'recall': 0.2857142857142857, 'f1': 0.27755102040816326, 'number': 119} | {'precision': 0.7418273260687342, 'recall': 0.8309859154929577, 'f1': 0.7838795394154118, 'number': 1065} | 0.6954 | 0.7742 | 0.7327 | 0.8089 |
| 0.3554 | 10.0 | 100 | 0.6524 | {'precision': 0.6996625421822272, 'recall': 0.7688504326328801, 'f1': 0.7326266195524146, 'number': 809} | {'precision': 0.2578125, 'recall': 0.2773109243697479, 'f1': 0.26720647773279355, 'number': 119} | {'precision': 0.7448979591836735, 'recall': 0.8225352112676056, 'f1': 0.781793842034806, 'number': 1065} | 0.6981 | 0.7682 | 0.7315 | 0.8105 |
| 0.3193 | 11.0 | 110 | 0.6687 | {'precision': 0.6944444444444444, 'recall': 0.7725587144622992, 'f1': 0.7314218841427736, 'number': 809} | {'precision': 0.3076923076923077, 'recall': 0.2689075630252101, 'f1': 0.28699551569506726, 'number': 119} | {'precision': 0.7702349869451697, 'recall': 0.8309859154929577, 'f1': 0.7994579945799458, 'number': 1065} | 0.7162 | 0.7737 | 0.7438 | 0.8105 |
| 0.3077 | 12.0 | 120 | 0.6657 | {'precision': 0.7019650655021834, 'recall': 0.7948084054388134, 'f1': 0.7455072463768115, 'number': 809} | {'precision': 0.3125, 'recall': 0.29411764705882354, 'f1': 0.30303030303030304, 'number': 119} | {'precision': 0.7712532865907099, 'recall': 0.8262910798122066, 'f1': 0.7978241160471442, 'number': 1065} | 0.7183 | 0.7817 | 0.7487 | 0.8127 |
| 0.2875 | 13.0 | 130 | 0.6820 | {'precision': 0.6990950226244343, 'recall': 0.7639060568603214, 'f1': 0.7300649734199646, 'number': 809} | {'precision': 0.2608695652173913, 'recall': 0.3025210084033613, 'f1': 0.28015564202334625, 'number': 119} | {'precision': 0.7584415584415585, 'recall': 0.8225352112676056, 'f1': 0.7891891891891892, 'number': 1065} | 0.7028 | 0.7677 | 0.7338 | 0.8094 |
| 0.2763 | 14.0 | 140 | 0.6680 | {'precision': 0.7062706270627063, 'recall': 0.7935723114956736, 'f1': 0.7473806752037252, 'number': 809} | {'precision': 0.28688524590163933, 'recall': 0.29411764705882354, 'f1': 0.2904564315352697, 'number': 119} | {'precision': 0.7674216027874564, 'recall': 0.8272300469483568, 'f1': 0.7962042476276546, 'number': 1065} | 0.7150 | 0.7817 | 0.7469 | 0.8181 |
| 0.2776 | 15.0 | 150 | 0.6675 | {'precision': 0.7104972375690608, 'recall': 0.7948084054388134, 'f1': 0.750291715285881, 'number': 809} | {'precision': 0.2892561983471074, 'recall': 0.29411764705882354, 'f1': 0.2916666666666667, 'number': 119} | {'precision': 0.7677642980935875, 'recall': 0.831924882629108, 'f1': 0.7985579089680036, 'number': 1065} | 0.7174 | 0.7847 | 0.7496 | 0.8194 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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