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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: 0.7189
- Answer: {'precision': 0.6983783783783784, 'recall': 0.7985166872682324, 'f1': 0.7450980392156863, 'number': 809}
- Header: {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119}
- Question: {'precision': 0.7754199823165341, 'recall': 0.8234741784037559, 'f1': 0.7987249544626595, 'number': 1065}
- Overall Precision: 0.7114
- Overall Recall: 0.7842
- Overall F1: 0.7461
- Overall Accuracy: 0.8074
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.561 | 1.0 | 10 | 1.3641 | {'precision': 0.05998125585754452, 'recall': 0.07911001236093942, 'f1': 0.06823027718550106, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.39123867069486407, 'recall': 0.4863849765258216, 'f1': 0.43365424863959817, 'number': 1065} | 0.2434 | 0.2920 | 0.2655 | 0.4879 |
| 1.1891 | 2.0 | 20 | 0.9802 | {'precision': 0.43872778297474274, 'recall': 0.5797280593325093, 'f1': 0.4994675186368477, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.5793269230769231, 'recall': 0.6788732394366197, 'f1': 0.6251621271076524, 'number': 1065} | 0.5062 | 0.5981 | 0.5483 | 0.6922 |
| 0.8513 | 3.0 | 30 | 0.8015 | {'precision': 0.5782520325203252, 'recall': 0.7033374536464772, 'f1': 0.6346904629113218, 'number': 809} | {'precision': 0.10204081632653061, 'recall': 0.04201680672268908, 'f1': 0.05952380952380952, 'number': 119} | {'precision': 0.6831421006178288, 'recall': 0.7267605633802817, 'f1': 0.7042766151046406, 'number': 1065} | 0.6223 | 0.6764 | 0.6482 | 0.7473 |
| 0.702 | 4.0 | 40 | 0.7279 | {'precision': 0.6275303643724697, 'recall': 0.7663782447466008, 'f1': 0.6900389538119087, 'number': 809} | {'precision': 0.1411764705882353, 'recall': 0.10084033613445378, 'f1': 0.11764705882352941, 'number': 119} | {'precision': 0.6978354978354978, 'recall': 0.7568075117370892, 'f1': 0.726126126126126, 'number': 1065} | 0.6454 | 0.7215 | 0.6814 | 0.7727 |
| 0.6059 | 5.0 | 50 | 0.7065 | {'precision': 0.6370530877573131, 'recall': 0.7268232385661311, 'f1': 0.6789838337182448, 'number': 809} | {'precision': 0.19607843137254902, 'recall': 0.16806722689075632, 'f1': 0.18099547511312217, 'number': 119} | {'precision': 0.7024106400665004, 'recall': 0.7934272300469484, 'f1': 0.7451499118165786, 'number': 1065} | 0.6522 | 0.7291 | 0.6885 | 0.7809 |
| 0.5133 | 6.0 | 60 | 0.6761 | {'precision': 0.6592592592592592, 'recall': 0.7700865265760197, 'f1': 0.7103762827822121, 'number': 809} | {'precision': 0.19791666666666666, 'recall': 0.15966386554621848, 'f1': 0.17674418604651165, 'number': 119} | {'precision': 0.7100638977635783, 'recall': 0.8347417840375587, 'f1': 0.7673716012084592, 'number': 1065} | 0.6677 | 0.7682 | 0.7144 | 0.7927 |
| 0.4539 | 7.0 | 70 | 0.6811 | {'precision': 0.6793893129770993, 'recall': 0.7700865265760197, 'f1': 0.7219003476245655, 'number': 809} | {'precision': 0.23387096774193547, 'recall': 0.24369747899159663, 'f1': 0.23868312757201646, 'number': 119} | {'precision': 0.7506361323155216, 'recall': 0.8309859154929577, 'f1': 0.7887700534759358, 'number': 1065} | 0.6923 | 0.7712 | 0.7296 | 0.7970 |
| 0.4175 | 8.0 | 80 | 0.6604 | {'precision': 0.6727664155005382, 'recall': 0.7725587144622992, 'f1': 0.7192174913693901, 'number': 809} | {'precision': 0.26785714285714285, 'recall': 0.25210084033613445, 'f1': 0.2597402597402597, 'number': 119} | {'precision': 0.7596899224806202, 'recall': 0.828169014084507, 'f1': 0.7924528301886793, 'number': 1065} | 0.6980 | 0.7712 | 0.7328 | 0.8022 |
| 0.3711 | 9.0 | 90 | 0.6827 | {'precision': 0.7034559643255296, 'recall': 0.7799752781211372, 'f1': 0.7397420867526378, 'number': 809} | {'precision': 0.2482758620689655, 'recall': 0.3025210084033613, 'f1': 0.2727272727272727, 'number': 119} | {'precision': 0.7497872340425532, 'recall': 0.8272300469483568, 'f1': 0.7866071428571428, 'number': 1065} | 0.6982 | 0.7767 | 0.7354 | 0.8049 |
| 0.3346 | 10.0 | 100 | 0.6881 | {'precision': 0.688367129135539, 'recall': 0.7972805933250927, 'f1': 0.738831615120275, 'number': 809} | {'precision': 0.2845528455284553, 'recall': 0.29411764705882354, 'f1': 0.2892561983471075, 'number': 119} | {'precision': 0.7693661971830986, 'recall': 0.8206572769953052, 'f1': 0.79418446160836, 'number': 1065} | 0.7077 | 0.7797 | 0.7419 | 0.8076 |
| 0.3003 | 11.0 | 110 | 0.7039 | {'precision': 0.6928104575163399, 'recall': 0.7861557478368356, 'f1': 0.7365373480023161, 'number': 809} | {'precision': 0.3008130081300813, 'recall': 0.31092436974789917, 'f1': 0.3057851239669422, 'number': 119} | {'precision': 0.7776801405975395, 'recall': 0.8309859154929577, 'f1': 0.8034498411257376, 'number': 1065} | 0.7150 | 0.7817 | 0.7469 | 0.8095 |
| 0.2878 | 12.0 | 120 | 0.7100 | {'precision': 0.6923913043478261, 'recall': 0.7873918417799752, 'f1': 0.736842105263158, 'number': 809} | {'precision': 0.2826086956521739, 'recall': 0.3277310924369748, 'f1': 0.3035019455252918, 'number': 119} | {'precision': 0.780053428317008, 'recall': 0.8225352112676056, 'f1': 0.8007312614259597, 'number': 1065} | 0.7116 | 0.7787 | 0.7437 | 0.8066 |
| 0.2724 | 13.0 | 130 | 0.7137 | {'precision': 0.6792249730893434, 'recall': 0.7799752781211372, 'f1': 0.7261219792865363, 'number': 809} | {'precision': 0.2846715328467153, 'recall': 0.3277310924369748, 'f1': 0.3046875, 'number': 119} | {'precision': 0.7838565022421524, 'recall': 0.8206572769953052, 'f1': 0.8018348623853212, 'number': 1065} | 0.7079 | 0.7747 | 0.7398 | 0.8028 |
| 0.2582 | 14.0 | 140 | 0.7174 | {'precision': 0.6964477933261571, 'recall': 0.799752781211372, 'f1': 0.7445339470655927, 'number': 809} | {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119} | {'precision': 0.7772848269742679, 'recall': 0.8225352112676056, 'f1': 0.7992700729927008, 'number': 1065} | 0.7114 | 0.7842 | 0.7461 | 0.8073 |
| 0.2569 | 15.0 | 150 | 0.7189 | {'precision': 0.6983783783783784, 'recall': 0.7985166872682324, 'f1': 0.7450980392156863, 'number': 809} | {'precision': 0.28368794326241137, 'recall': 0.33613445378151263, 'f1': 0.3076923076923077, 'number': 119} | {'precision': 0.7754199823165341, 'recall': 0.8234741784037559, 'f1': 0.7987249544626595, 'number': 1065} | 0.7114 | 0.7842 | 0.7461 | 0.8074 |
### Framework versions
- Transformers 4.29.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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