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---
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: 0.6741
- Answer: {'precision': 0.6960167714884696, 'recall': 0.8207663782447466, 'f1': 0.7532614861032332, 'number': 809}
- Header: {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119}
- Question: {'precision': 0.7824529991047449, 'recall': 0.8206572769953052, 'f1': 0.8010999083409717, 'number': 1065}
- Overall Precision: 0.7178
- Overall Recall: 0.7913
- Overall F1: 0.7527
- Overall Accuracy: 0.8085
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.8231 | 1.0 | 10 | 1.5809 | {'precision': 0.02072538860103627, 'recall': 0.024721878862793572, 'f1': 0.022547914317925594, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.20584795321637428, 'recall': 0.1652582159624413, 'f1': 0.18333333333333335, 'number': 1065} | 0.1077 | 0.0983 | 0.1028 | 0.4047 |
| 1.4405 | 2.0 | 20 | 1.2298 | {'precision': 0.12202380952380952, 'recall': 0.10135970333745364, 'f1': 0.11073598919648886, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4417808219178082, 'recall': 0.6056338028169014, 'f1': 0.5108910891089109, 'number': 1065} | 0.3407 | 0.3648 | 0.3523 | 0.5730 |
| 1.111 | 3.0 | 30 | 0.9547 | {'precision': 0.47729672650475186, 'recall': 0.5587144622991347, 'f1': 0.5148063781321185, 'number': 809} | {'precision': 0.06451612903225806, 'recall': 0.01680672268907563, 'f1': 0.026666666666666665, 'number': 119} | {'precision': 0.6218274111675127, 'recall': 0.6901408450704225, 'f1': 0.6542056074766356, 'number': 1065} | 0.5505 | 0.5966 | 0.5726 | 0.7074 |
| 0.8317 | 4.0 | 40 | 0.7729 | {'precision': 0.5933649289099526, 'recall': 0.7737948084054388, 'f1': 0.6716738197424893, 'number': 809} | {'precision': 0.24528301886792453, 'recall': 0.1092436974789916, 'f1': 0.1511627906976744, 'number': 119} | {'precision': 0.6753574432296047, 'recall': 0.7539906103286385, 'f1': 0.7125110913930789, 'number': 1065} | 0.6278 | 0.7235 | 0.6723 | 0.7637 |
| 0.656 | 5.0 | 50 | 0.7105 | {'precision': 0.640973630831643, 'recall': 0.7812113720642769, 'f1': 0.7041782729805014, 'number': 809} | {'precision': 0.2898550724637681, 'recall': 0.16806722689075632, 'f1': 0.2127659574468085, 'number': 119} | {'precision': 0.7317518248175182, 'recall': 0.7530516431924883, 'f1': 0.7422489588153632, 'number': 1065} | 0.6760 | 0.7296 | 0.7017 | 0.7811 |
| 0.5543 | 6.0 | 60 | 0.6688 | {'precision': 0.6625514403292181, 'recall': 0.796044499381953, 'f1': 0.7231892195395846, 'number': 809} | {'precision': 0.26732673267326734, 'recall': 0.226890756302521, 'f1': 0.24545454545454548, 'number': 119} | {'precision': 0.7412587412587412, 'recall': 0.7962441314553991, 'f1': 0.7677682209144409, 'number': 1065} | 0.6852 | 0.7622 | 0.7216 | 0.8004 |
| 0.4829 | 7.0 | 70 | 0.6491 | {'precision': 0.6635610766045549, 'recall': 0.792336217552534, 'f1': 0.7222535211267606, 'number': 809} | {'precision': 0.25471698113207547, 'recall': 0.226890756302521, 'f1': 0.24, 'number': 119} | {'precision': 0.7401372212692967, 'recall': 0.8103286384976526, 'f1': 0.7736441057821605, 'number': 1065} | 0.6841 | 0.7682 | 0.7237 | 0.8056 |
| 0.4371 | 8.0 | 80 | 0.6419 | {'precision': 0.6742502585315409, 'recall': 0.8059332509270705, 'f1': 0.7342342342342343, 'number': 809} | {'precision': 0.25210084033613445, 'recall': 0.25210084033613445, 'f1': 0.25210084033613445, 'number': 119} | {'precision': 0.7491228070175439, 'recall': 0.8018779342723005, 'f1': 0.7746031746031745, 'number': 1065} | 0.6900 | 0.7707 | 0.7281 | 0.8062 |
| 0.3855 | 9.0 | 90 | 0.6560 | {'precision': 0.6869747899159664, 'recall': 0.8084054388133498, 'f1': 0.7427597955706984, 'number': 809} | {'precision': 0.2711864406779661, 'recall': 0.2689075630252101, 'f1': 0.270042194092827, 'number': 119} | {'precision': 0.7871559633027523, 'recall': 0.8056338028169014, 'f1': 0.7962877030162413, 'number': 1065} | 0.7148 | 0.7747 | 0.7436 | 0.8047 |
| 0.3511 | 10.0 | 100 | 0.6675 | {'precision': 0.6853932584269663, 'recall': 0.8294190358467244, 'f1': 0.750559284116331, 'number': 809} | {'precision': 0.2966101694915254, 'recall': 0.29411764705882354, 'f1': 0.2953586497890296, 'number': 119} | {'precision': 0.7851239669421488, 'recall': 0.8028169014084507, 'f1': 0.7938718662952646, 'number': 1065} | 0.7141 | 0.7832 | 0.7471 | 0.8062 |
| 0.3236 | 11.0 | 110 | 0.6729 | {'precision': 0.7195121951219512, 'recall': 0.8022249690976514, 'f1': 0.7586206896551723, 'number': 809} | {'precision': 0.30303030303030304, 'recall': 0.33613445378151263, 'f1': 0.3187250996015936, 'number': 119} | {'precision': 0.774798927613941, 'recall': 0.8140845070422535, 'f1': 0.7939560439560438, 'number': 1065} | 0.7227 | 0.7807 | 0.7506 | 0.8045 |
| 0.307 | 12.0 | 120 | 0.6755 | {'precision': 0.6757575757575758, 'recall': 0.826946847960445, 'f1': 0.7437465258476932, 'number': 809} | {'precision': 0.29365079365079366, 'recall': 0.31092436974789917, 'f1': 0.30204081632653057, 'number': 119} | {'precision': 0.7683363148479427, 'recall': 0.8065727699530516, 'f1': 0.7869903802107192, 'number': 1065} | 0.7005 | 0.7852 | 0.7405 | 0.8052 |
| 0.2905 | 13.0 | 130 | 0.6712 | {'precision': 0.6970021413276232, 'recall': 0.8046971569839307, 'f1': 0.7469879518072289, 'number': 809} | {'precision': 0.3007518796992481, 'recall': 0.33613445378151263, 'f1': 0.31746031746031744, 'number': 119} | {'precision': 0.7817028985507246, 'recall': 0.8103286384976526, 'f1': 0.7957584140156754, 'number': 1065} | 0.7158 | 0.7797 | 0.7464 | 0.8067 |
| 0.2734 | 14.0 | 140 | 0.6758 | {'precision': 0.6912681912681913, 'recall': 0.8220024721878862, 'f1': 0.7509881422924901, 'number': 809} | {'precision': 0.3089430894308943, 'recall': 0.31932773109243695, 'f1': 0.3140495867768595, 'number': 119} | {'precision': 0.7850045167118338, 'recall': 0.815962441314554, 'f1': 0.8001841620626151, 'number': 1065} | 0.7172 | 0.7888 | 0.7513 | 0.8097 |
| 0.2672 | 15.0 | 150 | 0.6741 | {'precision': 0.6960167714884696, 'recall': 0.8207663782447466, 'f1': 0.7532614861032332, 'number': 809} | {'precision': 0.30952380952380953, 'recall': 0.3277310924369748, 'f1': 0.31836734693877555, 'number': 119} | {'precision': 0.7824529991047449, 'recall': 0.8206572769953052, 'f1': 0.8010999083409717, 'number': 1065} | 0.7178 | 0.7913 | 0.7527 | 0.8085 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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