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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: 0.6771
- Answer: {'precision': 0.7053571428571429, 'recall': 0.7812113720642769, 'f1': 0.7413489736070381, 'number': 809}
- Header: {'precision': 0.29770992366412213, 'recall': 0.3277310924369748, 'f1': 0.312, 'number': 119}
- Question: {'precision': 0.763716814159292, 'recall': 0.8103286384976526, 'f1': 0.7863325740318907, 'number': 1065}
- Overall Precision: 0.7112
- Overall Recall: 0.7697
- Overall F1: 0.7393
- Overall Accuracy: 0.8036
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7668 | 1.0 | 10 | 1.5655 | {'precision': 0.01564945226917058, 'recall': 0.012360939431396786, 'f1': 0.013812154696132598, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.31925675675675674, 'recall': 0.17746478873239438, 'f1': 0.22812311406155703, 'number': 1065} | 0.1617 | 0.0998 | 0.1234 | 0.3599 |
| 1.4335 | 2.0 | 20 | 1.2504 | {'precision': 0.22972972972972974, 'recall': 0.2521631644004944, 'f1': 0.24042427813789036, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.391304347826087, 'recall': 0.5492957746478874, 'f1': 0.45703125, 'number': 1065} | 0.3311 | 0.3959 | 0.3606 | 0.5926 |
| 1.1215 | 3.0 | 30 | 0.9771 | {'precision': 0.4570273003033367, 'recall': 0.5587144622991347, 'f1': 0.5027808676307008, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5460829493087558, 'recall': 0.6676056338028169, 'f1': 0.6007604562737643, 'number': 1065} | 0.5022 | 0.5835 | 0.5398 | 0.6802 |
| 0.8632 | 4.0 | 40 | 0.7960 | {'precision': 0.5714285714285714, 'recall': 0.7317676143386898, 'f1': 0.6417344173441735, 'number': 809} | {'precision': 0.0379746835443038, 'recall': 0.025210084033613446, 'f1': 0.030303030303030304, 'number': 119} | {'precision': 0.633201581027668, 'recall': 0.752112676056338, 'f1': 0.6875536480686696, 'number': 1065} | 0.5866 | 0.7005 | 0.6385 | 0.7572 |
| 0.6913 | 5.0 | 50 | 0.7202 | {'precision': 0.6335797254487856, 'recall': 0.7416563658838071, 'f1': 0.683371298405467, 'number': 809} | {'precision': 0.10752688172043011, 'recall': 0.08403361344537816, 'f1': 0.09433962264150944, 'number': 119} | {'precision': 0.6907216494845361, 'recall': 0.7549295774647887, 'f1': 0.721399730820996, 'number': 1065} | 0.6416 | 0.7095 | 0.6738 | 0.7808 |
| 0.5758 | 6.0 | 60 | 0.6812 | {'precision': 0.6677248677248677, 'recall': 0.7799752781211372, 'f1': 0.7194982896237172, 'number': 809} | {'precision': 0.17857142857142858, 'recall': 0.16806722689075632, 'f1': 0.17316017316017318, 'number': 119} | {'precision': 0.6919315403422983, 'recall': 0.7971830985915493, 'f1': 0.7408376963350787, 'number': 1065} | 0.6567 | 0.7526 | 0.7014 | 0.7947 |
| 0.4993 | 7.0 | 70 | 0.6593 | {'precision': 0.6597938144329897, 'recall': 0.7911001236093943, 'f1': 0.7195053400786959, 'number': 809} | {'precision': 0.21875, 'recall': 0.17647058823529413, 'f1': 0.19534883720930232, 'number': 119} | {'precision': 0.7158703071672355, 'recall': 0.787793427230047, 'f1': 0.7501117568171659, 'number': 1065} | 0.6702 | 0.7526 | 0.7091 | 0.7951 |
| 0.4397 | 8.0 | 80 | 0.6633 | {'precision': 0.6749192680301399, 'recall': 0.7750309023485785, 'f1': 0.721518987341772, 'number': 809} | {'precision': 0.23943661971830985, 'recall': 0.2857142857142857, 'f1': 0.26053639846743293, 'number': 119} | {'precision': 0.7274290627687017, 'recall': 0.7943661971830986, 'f1': 0.7594254937163376, 'number': 1065} | 0.6746 | 0.7561 | 0.7130 | 0.7987 |
| 0.396 | 9.0 | 90 | 0.6605 | {'precision': 0.6875699888017918, 'recall': 0.7589616810877626, 'f1': 0.7215041128084607, 'number': 809} | {'precision': 0.25203252032520324, 'recall': 0.2605042016806723, 'f1': 0.25619834710743805, 'number': 119} | {'precision': 0.7344013490725126, 'recall': 0.8178403755868544, 'f1': 0.7738782763216348, 'number': 1065} | 0.6885 | 0.7607 | 0.7228 | 0.8026 |
| 0.3578 | 10.0 | 100 | 0.6624 | {'precision': 0.6842105263157895, 'recall': 0.7873918417799752, 'f1': 0.7321839080459769, 'number': 809} | {'precision': 0.2773109243697479, 'recall': 0.2773109243697479, 'f1': 0.2773109243697479, 'number': 119} | {'precision': 0.7476231633535004, 'recall': 0.812206572769953, 'f1': 0.7785778577857785, 'number': 1065} | 0.6955 | 0.7702 | 0.7310 | 0.8022 |
| 0.3249 | 11.0 | 110 | 0.6678 | {'precision': 0.6865671641791045, 'recall': 0.796044499381953, 'f1': 0.7372638809387521, 'number': 809} | {'precision': 0.273972602739726, 'recall': 0.33613445378151263, 'f1': 0.3018867924528302, 'number': 119} | {'precision': 0.7584973166368515, 'recall': 0.7962441314553991, 'f1': 0.7769125057260651, 'number': 1065} | 0.6957 | 0.7687 | 0.7304 | 0.8010 |
| 0.3021 | 12.0 | 120 | 0.6681 | {'precision': 0.7120535714285714, 'recall': 0.788627935723115, 'f1': 0.7483870967741936, 'number': 809} | {'precision': 0.2923076923076923, 'recall': 0.31932773109243695, 'f1': 0.3052208835341365, 'number': 119} | {'precision': 0.7547826086956522, 'recall': 0.8150234741784037, 'f1': 0.7837471783295711, 'number': 1065} | 0.7096 | 0.7747 | 0.7407 | 0.8045 |
| 0.2895 | 13.0 | 130 | 0.6755 | {'precision': 0.7122060470324748, 'recall': 0.7861557478368356, 'f1': 0.7473560517038778, 'number': 809} | {'precision': 0.2887323943661972, 'recall': 0.3445378151260504, 'f1': 0.31417624521072796, 'number': 119} | {'precision': 0.7595048629531388, 'recall': 0.8065727699530516, 'f1': 0.7823315118397085, 'number': 1065} | 0.7091 | 0.7707 | 0.7386 | 0.8026 |
| 0.2734 | 14.0 | 140 | 0.6768 | {'precision': 0.7093541202672605, 'recall': 0.7873918417799752, 'f1': 0.7463386057410661, 'number': 809} | {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119} | {'precision': 0.7570175438596491, 'recall': 0.8103286384976526, 'f1': 0.7827664399092971, 'number': 1065} | 0.7067 | 0.7727 | 0.7383 | 0.8026 |
| 0.2804 | 15.0 | 150 | 0.6771 | {'precision': 0.7053571428571429, 'recall': 0.7812113720642769, 'f1': 0.7413489736070381, 'number': 809} | {'precision': 0.29770992366412213, 'recall': 0.3277310924369748, 'f1': 0.312, 'number': 119} | {'precision': 0.763716814159292, 'recall': 0.8103286384976526, 'f1': 0.7863325740318907, 'number': 1065} | 0.7112 | 0.7697 | 0.7393 | 0.8036 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cpu
- Datasets 2.14.5
- Tokenizers 0.14.1
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