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library_name: transformers
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-FUNSD-only
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-only
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: 0.5883
- Eader: {'precision': 0.3877551020408163, 'recall': 0.2289156626506024, 'f1': 0.2878787878787879, 'number': 83}
- Nswer: {'precision': 0.4581673306772908, 'recall': 0.5609756097560976, 'f1': 0.5043859649122806, 'number': 205}
- Uestion: {'precision': 0.36981132075471695, 'recall': 0.42424242424242425, 'f1': 0.3951612903225806, 'number': 231}
- Overall Precision: 0.4106
- Overall Recall: 0.4470
- Overall F1: 0.4280
- Overall Accuracy: 0.7852
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 9
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Eader | Nswer | Uestion | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.3004 | 1.0 | 8 | 1.0817 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83} | {'precision': 0.07280832095096583, 'recall': 0.23902439024390243, 'f1': 0.11161731207289294, 'number': 205} | {'precision': 0.06845238095238096, 'recall': 0.19913419913419914, 'f1': 0.1018826135105205, 'number': 231} | 0.0706 | 0.1830 | 0.1019 | 0.6047 |
| 1.0289 | 2.0 | 16 | 0.8889 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83} | {'precision': 0.1986754966887417, 'recall': 0.43902439024390244, 'f1': 0.2735562310030395, 'number': 205} | {'precision': 0.17155756207674944, 'recall': 0.329004329004329, 'f1': 0.22551928783382788, 'number': 231} | 0.1853 | 0.3198 | 0.2346 | 0.6935 |
| 0.8399 | 3.0 | 24 | 0.7179 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 83} | {'precision': 0.2890855457227139, 'recall': 0.47804878048780486, 'f1': 0.36029411764705876, 'number': 205} | {'precision': 0.2375366568914956, 'recall': 0.35064935064935066, 'f1': 0.28321678321678323, 'number': 231} | 0.2602 | 0.3449 | 0.2966 | 0.7429 |
| 0.7069 | 4.0 | 32 | 0.6412 | {'precision': 0.13636363636363635, 'recall': 0.03614457831325301, 'f1': 0.05714285714285714, 'number': 83} | {'precision': 0.37318840579710144, 'recall': 0.5024390243902439, 'f1': 0.4282744282744283, 'number': 205} | {'precision': 0.3356164383561644, 'recall': 0.42424242424242425, 'f1': 0.37476099426386233, 'number': 231} | 0.3458 | 0.3931 | 0.3679 | 0.7591 |
| 0.5901 | 5.0 | 40 | 0.6059 | {'precision': 0.2564102564102564, 'recall': 0.12048192771084337, 'f1': 0.1639344262295082, 'number': 83} | {'precision': 0.3925925925925926, 'recall': 0.5170731707317073, 'f1': 0.4463157894736842, 'number': 205} | {'precision': 0.3726235741444867, 'recall': 0.42424242424242425, 'f1': 0.3967611336032389, 'number': 231} | 0.3741 | 0.4123 | 0.3923 | 0.7735 |
| 0.5121 | 6.0 | 48 | 0.5797 | {'precision': 0.3269230769230769, 'recall': 0.20481927710843373, 'f1': 0.2518518518518518, 'number': 83} | {'precision': 0.4351145038167939, 'recall': 0.5560975609756098, 'f1': 0.48822269807280516, 'number': 205} | {'precision': 0.3527272727272727, 'recall': 0.4199134199134199, 'f1': 0.383399209486166, 'number': 231} | 0.3871 | 0.4393 | 0.4116 | 0.7865 |
| 0.4503 | 7.0 | 56 | 0.5941 | {'precision': 0.36, 'recall': 0.21686746987951808, 'f1': 0.2706766917293233, 'number': 83} | {'precision': 0.4474708171206226, 'recall': 0.5609756097560976, 'f1': 0.4978354978354979, 'number': 205} | {'precision': 0.3619402985074627, 'recall': 0.4199134199134199, 'f1': 0.38877755511022044, 'number': 231} | 0.4 | 0.4432 | 0.4205 | 0.7799 |
| 0.4114 | 8.0 | 64 | 0.5924 | {'precision': 0.38, 'recall': 0.2289156626506024, 'f1': 0.28571428571428575, 'number': 83} | {'precision': 0.4453125, 'recall': 0.5560975609756098, 'f1': 0.4945770065075922, 'number': 205} | {'precision': 0.3656716417910448, 'recall': 0.42424242424242425, 'f1': 0.39278557114228463, 'number': 231} | 0.4024 | 0.4451 | 0.4227 | 0.7827 |
| 0.3935 | 9.0 | 72 | 0.5883 | {'precision': 0.3877551020408163, 'recall': 0.2289156626506024, 'f1': 0.2878787878787879, 'number': 83} | {'precision': 0.4581673306772908, 'recall': 0.5609756097560976, 'f1': 0.5043859649122806, 'number': 205} | {'precision': 0.36981132075471695, 'recall': 0.42424242424242425, 'f1': 0.3951612903225806, 'number': 231} | 0.4106 | 0.4470 | 0.4280 | 0.7852 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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