---
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: docuAI
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. -->
# docuAI
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: 0.6827
- Answer: {'precision': 0.7172949002217295, 'recall': 0.799752781211372, 'f1': 0.7562828755113968, 'number': 809}
- Header: {'precision': 0.33620689655172414, 'recall': 0.3277310924369748, 'f1': 0.33191489361702126, 'number': 119}
- Question: {'precision': 0.7637931034482759, 'recall': 0.831924882629108, 'f1': 0.7964044943820225, 'number': 1065}
- Overall Precision: 0.7218
- Overall Recall: 0.7888
- Overall F1: 0.7538
- Overall Accuracy: 0.8097
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7471 | 1.0 | 10 | 1.5418 | {'precision': 0.027965284474445518, 'recall': 0.03584672435105068, 'f1': 0.0314192849404117, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.26978723404255317, 'recall': 0.2976525821596244, 'f1': 0.2830357142857143, 'number': 1065} | 0.1564 | 0.1736 | 0.1646 | 0.4109 |
| 1.3886 | 2.0 | 20 | 1.2047 | {'precision': 0.25207100591715975, 'recall': 0.26328800988875156, 'f1': 0.2575574365175332, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4723969252271139, 'recall': 0.6347417840375587, 'f1': 0.5416666666666667, 'number': 1065} | 0.3906 | 0.4461 | 0.4165 | 0.5948 |
| 1.0458 | 3.0 | 30 | 0.9213 | {'precision': 0.4836471754212091, 'recall': 0.6032138442521632, 'f1': 0.5368536853685368, 'number': 809} | {'precision': 0.05263157894736842, 'recall': 0.008403361344537815, 'f1': 0.014492753623188406, 'number': 119} | {'precision': 0.6089795918367347, 'recall': 0.7004694835680751, 'f1': 0.651528384279476, 'number': 1065} | 0.5482 | 0.6197 | 0.5817 | 0.7024 |
| 0.8024 | 4.0 | 40 | 0.7873 | {'precision': 0.5814393939393939, 'recall': 0.7589616810877626, 'f1': 0.6584450402144773, 'number': 809} | {'precision': 0.1044776119402985, 'recall': 0.058823529411764705, 'f1': 0.07526881720430108, 'number': 119} | {'precision': 0.6488427773343974, 'recall': 0.7633802816901408, 'f1': 0.7014667817083693, 'number': 1065} | 0.6035 | 0.7195 | 0.6564 | 0.7567 |
| 0.6593 | 5.0 | 50 | 0.7148 | {'precision': 0.6419753086419753, 'recall': 0.7713226205191595, 'f1': 0.7007299270072992, 'number': 809} | {'precision': 0.2602739726027397, 'recall': 0.15966386554621848, 'f1': 0.19791666666666666, 'number': 119} | {'precision': 0.7365217391304347, 'recall': 0.7953051643192488, 'f1': 0.7647855530474039, 'number': 1065} | 0.6788 | 0.7476 | 0.7116 | 0.7846 |
| 0.5564 | 6.0 | 60 | 0.6806 | {'precision': 0.6945054945054945, 'recall': 0.7812113720642769, 'f1': 0.7353112274578244, 'number': 809} | {'precision': 0.2647058823529412, 'recall': 0.226890756302521, 'f1': 0.24434389140271492, 'number': 119} | {'precision': 0.7158067158067158, 'recall': 0.8206572769953052, 'f1': 0.7646544181977254, 'number': 1065} | 0.6865 | 0.7692 | 0.7255 | 0.7947 |
| 0.4838 | 7.0 | 70 | 0.6697 | {'precision': 0.6844396082698585, 'recall': 0.7775030902348579, 'f1': 0.7280092592592592, 'number': 809} | {'precision': 0.2696629213483146, 'recall': 0.20168067226890757, 'f1': 0.23076923076923078, 'number': 119} | {'precision': 0.738626964433416, 'recall': 0.8384976525821596, 'f1': 0.7854001759014952, 'number': 1065} | 0.6973 | 0.7757 | 0.7344 | 0.8004 |
| 0.4342 | 8.0 | 80 | 0.6709 | {'precision': 0.7062780269058296, 'recall': 0.7787391841779975, 'f1': 0.7407407407407407, 'number': 809} | {'precision': 0.30097087378640774, 'recall': 0.2605042016806723, 'f1': 0.27927927927927926, 'number': 119} | {'precision': 0.7415359207266722, 'recall': 0.8431924882629108, 'f1': 0.7891036906854131, 'number': 1065} | 0.7067 | 0.7822 | 0.7426 | 0.8020 |
| 0.386 | 9.0 | 90 | 0.6592 | {'precision': 0.7107258938244854, 'recall': 0.8108776266996292, 'f1': 0.7575057736720554, 'number': 809} | {'precision': 0.2764227642276423, 'recall': 0.2857142857142857, 'f1': 0.2809917355371901, 'number': 119} | {'precision': 0.75, 'recall': 0.8253521126760563, 'f1': 0.7858739383102369, 'number': 1065} | 0.7074 | 0.7873 | 0.7452 | 0.8106 |
| 0.3572 | 10.0 | 100 | 0.6611 | {'precision': 0.7080213903743315, 'recall': 0.8182941903584673, 'f1': 0.7591743119266056, 'number': 809} | {'precision': 0.29906542056074764, 'recall': 0.2689075630252101, 'f1': 0.28318584070796454, 'number': 119} | {'precision': 0.7532133676092545, 'recall': 0.8253521126760563, 'f1': 0.7876344086021505, 'number': 1065} | 0.7121 | 0.7893 | 0.7487 | 0.8102 |
| 0.3264 | 11.0 | 110 | 0.6828 | {'precision': 0.7325056433408578, 'recall': 0.8022249690976514, 'f1': 0.7657817109144542, 'number': 809} | {'precision': 0.3, 'recall': 0.3025210084033613, 'f1': 0.301255230125523, 'number': 119} | {'precision': 0.7525773195876289, 'recall': 0.8225352112676056, 'f1': 0.7860026917900403, 'number': 1065} | 0.7194 | 0.7832 | 0.7499 | 0.8055 |
| 0.3132 | 12.0 | 120 | 0.6722 | {'precision': 0.7123893805309734, 'recall': 0.796044499381953, 'f1': 0.7518972562755399, 'number': 809} | {'precision': 0.3391304347826087, 'recall': 0.3277310924369748, 'f1': 0.3333333333333333, 'number': 119} | {'precision': 0.7548605240912933, 'recall': 0.8384976525821596, 'f1': 0.7944839857651246, 'number': 1065} | 0.7157 | 0.7908 | 0.7514 | 0.8082 |
| 0.293 | 13.0 | 130 | 0.6817 | {'precision': 0.7109634551495017, 'recall': 0.7935723114956736, 'f1': 0.75, 'number': 809} | {'precision': 0.3277310924369748, 'recall': 0.3277310924369748, 'f1': 0.3277310924369748, 'number': 119} | {'precision': 0.7680776014109347, 'recall': 0.8178403755868544, 'f1': 0.7921782628467485, 'number': 1065} | 0.7199 | 0.7787 | 0.7481 | 0.8078 |
| 0.282 | 14.0 | 140 | 0.6845 | {'precision': 0.712707182320442, 'recall': 0.7972805933250927, 'f1': 0.7526254375729288, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3277310924369748, 'f1': 0.3305084745762712, 'number': 119} | {'precision': 0.768624014022787, 'recall': 0.8234741784037559, 'f1': 0.7951042611060744, 'number': 1065} | 0.7217 | 0.7832 | 0.7512 | 0.8094 |
| 0.2728 | 15.0 | 150 | 0.6827 | {'precision': 0.7172949002217295, 'recall': 0.799752781211372, 'f1': 0.7562828755113968, 'number': 809} | {'precision': 0.33620689655172414, 'recall': 0.3277310924369748, 'f1': 0.33191489361702126, 'number': 119} | {'precision': 0.7637931034482759, 'recall': 0.831924882629108, 'f1': 0.7964044943820225, 'number': 1065} | 0.7218 | 0.7888 | 0.7538 | 0.8097 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3