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library_name: transformers
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6840
- Answer: {'precision': 0.7045203969128997, 'recall': 0.7898640296662547, 'f1': 0.7447552447552448, 'number': 809}
- Header: {'precision': 0.3465346534653465, 'recall': 0.29411764705882354, 'f1': 0.3181818181818182, 'number': 119}
- Question: {'precision': 0.7655709342560554, 'recall': 0.8309859154929577, 'f1': 0.7969383160738407, 'number': 1065}
- Overall Precision: 0.7204
- Overall Recall: 0.7822
- Overall F1: 0.7501
- 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: Use OptimizerNames.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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.81 | 1.0 | 10 | 1.6151 | {'precision': 0.004709576138147566, 'recall': 0.003708281829419036, 'f1': 0.004149377593360995, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.1045016077170418, 'recall': 0.06103286384976526, 'f1': 0.07705986959098991, 'number': 1065} | 0.0540 | 0.0341 | 0.0418 | 0.3338 |
| 1.4675 | 2.0 | 20 | 1.2857 | {'precision': 0.20516129032258065, 'recall': 0.1965389369592089, 'f1': 0.20075757575757577, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4057971014492754, 'recall': 0.5258215962441315, 'f1': 0.45807770961145194, 'number': 1065} | 0.3336 | 0.3608 | 0.3467 | 0.5615 |
| 1.1095 | 3.0 | 30 | 0.9749 | {'precision': 0.4744136460554371, 'recall': 0.5500618046971569, 'f1': 0.5094447624499141, 'number': 809} | {'precision': 0.02857142857142857, 'recall': 0.008403361344537815, 'f1': 0.012987012987012986, 'number': 119} | {'precision': 0.5271260997067448, 'recall': 0.6751173708920187, 'f1': 0.592013174145739, 'number': 1065} | 0.4985 | 0.5845 | 0.5381 | 0.6871 |
| 0.8396 | 4.0 | 40 | 0.7887 | {'precision': 0.6004098360655737, 'recall': 0.7243510506798516, 'f1': 0.6565826330532212, 'number': 809} | {'precision': 0.1016949152542373, 'recall': 0.05042016806722689, 'f1': 0.06741573033707866, 'number': 119} | {'precision': 0.6529942575881871, 'recall': 0.7474178403755869, 'f1': 0.6970227670753065, 'number': 1065} | 0.6158 | 0.6964 | 0.6536 | 0.7516 |
| 0.6706 | 5.0 | 50 | 0.7238 | {'precision': 0.6403887688984882, 'recall': 0.7330037082818294, 'f1': 0.6835734870317004, 'number': 809} | {'precision': 0.1875, 'recall': 0.12605042016806722, 'f1': 0.1507537688442211, 'number': 119} | {'precision': 0.6530303030303031, 'recall': 0.8093896713615023, 'f1': 0.7228511530398324, 'number': 1065} | 0.6320 | 0.7376 | 0.6807 | 0.7773 |
| 0.5725 | 6.0 | 60 | 0.6919 | {'precision': 0.6587473002159827, 'recall': 0.754017305315204, 'f1': 0.7031700288184438, 'number': 809} | {'precision': 0.24050632911392406, 'recall': 0.15966386554621848, 'f1': 0.1919191919191919, 'number': 119} | {'precision': 0.7182978723404255, 'recall': 0.7924882629107981, 'f1': 0.7535714285714286, 'number': 1065} | 0.6757 | 0.7391 | 0.7060 | 0.7819 |
| 0.4918 | 7.0 | 70 | 0.6613 | {'precision': 0.685807150595883, 'recall': 0.7824474660074165, 'f1': 0.7309468822170901, 'number': 809} | {'precision': 0.32941176470588235, 'recall': 0.23529411764705882, 'f1': 0.2745098039215686, 'number': 119} | {'precision': 0.730092204526404, 'recall': 0.8178403755868544, 'f1': 0.7714791851195748, 'number': 1065} | 0.6960 | 0.7687 | 0.7306 | 0.7954 |
| 0.4364 | 8.0 | 80 | 0.6622 | {'precision': 0.6994475138121546, 'recall': 0.7824474660074165, 'f1': 0.7386231038506416, 'number': 809} | {'precision': 0.25961538461538464, 'recall': 0.226890756302521, 'f1': 0.242152466367713, 'number': 119} | {'precision': 0.7337826453243471, 'recall': 0.8178403755868544, 'f1': 0.7735346358792186, 'number': 1065} | 0.6972 | 0.7682 | 0.7310 | 0.7961 |
| 0.3848 | 9.0 | 90 | 0.6633 | {'precision': 0.7046460176991151, 'recall': 0.7873918417799752, 'f1': 0.7437244600116752, 'number': 809} | {'precision': 0.3106796116504854, 'recall': 0.2689075630252101, 'f1': 0.28828828828828823, 'number': 119} | {'precision': 0.7519446845289542, 'recall': 0.8169014084507042, 'f1': 0.7830783078307832, 'number': 1065} | 0.7112 | 0.7722 | 0.7404 | 0.7973 |
| 0.3806 | 10.0 | 100 | 0.6619 | {'precision': 0.6894679695982627, 'recall': 0.7849196538936959, 'f1': 0.7341040462427746, 'number': 809} | {'precision': 0.29896907216494845, 'recall': 0.24369747899159663, 'f1': 0.2685185185185185, 'number': 119} | {'precision': 0.7575236457437661, 'recall': 0.8272300469483568, 'f1': 0.7908438061041293, 'number': 1065} | 0.7084 | 0.7752 | 0.7403 | 0.8005 |
| 0.3245 | 11.0 | 110 | 0.6781 | {'precision': 0.7051569506726457, 'recall': 0.7775030902348579, 'f1': 0.7395649617871839, 'number': 809} | {'precision': 0.32710280373831774, 'recall': 0.29411764705882354, 'f1': 0.3097345132743363, 'number': 119} | {'precision': 0.7420701168614358, 'recall': 0.8347417840375587, 'f1': 0.7856827220503757, 'number': 1065} | 0.7069 | 0.7792 | 0.7413 | 0.7994 |
| 0.3037 | 12.0 | 120 | 0.6741 | {'precision': 0.7049723756906078, 'recall': 0.788627935723115, 'f1': 0.7444574095682615, 'number': 809} | {'precision': 0.32, 'recall': 0.2689075630252101, 'f1': 0.2922374429223744, 'number': 119} | {'precision': 0.7791519434628975, 'recall': 0.828169014084507, 'f1': 0.8029130632680928, 'number': 1065} | 0.7263 | 0.7787 | 0.7516 | 0.8001 |
| 0.2917 | 13.0 | 130 | 0.6849 | {'precision': 0.7, 'recall': 0.7787391841779975, 'f1': 0.7372732592159158, 'number': 809} | {'precision': 0.32673267326732675, 'recall': 0.2773109243697479, 'f1': 0.30000000000000004, 'number': 119} | {'precision': 0.7703056768558952, 'recall': 0.828169014084507, 'f1': 0.7981900452488688, 'number': 1065} | 0.7199 | 0.7752 | 0.7466 | 0.8011 |
| 0.2692 | 14.0 | 140 | 0.6823 | {'precision': 0.7019867549668874, 'recall': 0.7861557478368356, 'f1': 0.7416909620991254, 'number': 809} | {'precision': 0.35051546391752575, 'recall': 0.2857142857142857, 'f1': 0.3148148148148148, 'number': 119} | {'precision': 0.7642980935875217, 'recall': 0.828169014084507, 'f1': 0.7949526813880126, 'number': 1065} | 0.7195 | 0.7787 | 0.7480 | 0.8019 |
| 0.2721 | 15.0 | 150 | 0.6840 | {'precision': 0.7045203969128997, 'recall': 0.7898640296662547, 'f1': 0.7447552447552448, 'number': 809} | {'precision': 0.3465346534653465, 'recall': 0.29411764705882354, 'f1': 0.3181818181818182, 'number': 119} | {'precision': 0.7655709342560554, 'recall': 0.8309859154929577, 'f1': 0.7969383160738407, 'number': 1065} | 0.7204 | 0.7822 | 0.7501 | 0.8036 |
### Framework versions
- Transformers 4.53.0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
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