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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: 0.9802
- Answer: {'precision': 0.7281879194630873, 'recall': 0.8046971569839307, 'f1': 0.7645331767469172, 'number': 809}
- Header: {'precision': 0.43884892086330934, 'recall': 0.5126050420168067, 'f1': 0.4728682170542636, 'number': 119}
- Question: {'precision': 0.8128390596745028, 'recall': 0.844131455399061, 'f1': 0.8281897742975588, 'number': 1065}
- Overall Precision: 0.7532
- Overall Recall: 0.8083
- Overall F1: 0.7798
- Overall Accuracy: 0.8137
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.3673 | 1.0 | 75 | 0.7834 | {'precision': 0.6307870370370371, 'recall': 0.6736711990111248, 'f1': 0.6515242080095638, 'number': 809} | {'precision': 0.05660377358490566, 'recall': 0.025210084033613446, 'f1': 0.03488372093023256, 'number': 119} | {'precision': 0.6363636363636364, 'recall': 0.7690140845070422, 'f1': 0.6964285714285714, 'number': 1065} | 0.6202 | 0.6859 | 0.6514 | 0.7643 |
| 0.7401 | 2.0 | 150 | 0.7013 | {'precision': 0.6420704845814978, 'recall': 0.7206427688504327, 'f1': 0.6790914385556204, 'number': 809} | {'precision': 0.2169811320754717, 'recall': 0.19327731092436976, 'f1': 0.20444444444444446, 'number': 119} | {'precision': 0.7446443873179092, 'recall': 0.815962441314554, 'f1': 0.7786738351254481, 'number': 1065} | 0.6763 | 0.7401 | 0.7068 | 0.7636 |
| 0.5127 | 3.0 | 225 | 0.6228 | {'precision': 0.7036637931034483, 'recall': 0.8071693448702101, 'f1': 0.7518710420264824, 'number': 809} | {'precision': 0.2987012987012987, 'recall': 0.3865546218487395, 'f1': 0.336996336996337, 'number': 119} | {'precision': 0.7757255936675461, 'recall': 0.828169014084507, 'f1': 0.8010899182561309, 'number': 1065} | 0.7125 | 0.7933 | 0.7507 | 0.8033 |
| 0.3405 | 4.0 | 300 | 0.6358 | {'precision': 0.7230419977298524, 'recall': 0.7873918417799752, 'f1': 0.7538461538461538, 'number': 809} | {'precision': 0.29577464788732394, 'recall': 0.35294117647058826, 'f1': 0.3218390804597701, 'number': 119} | {'precision': 0.7768888888888889, 'recall': 0.8206572769953052, 'f1': 0.7981735159817351, 'number': 1065} | 0.7230 | 0.7792 | 0.7501 | 0.8055 |
| 0.2492 | 5.0 | 375 | 0.6565 | {'precision': 0.7119914346895075, 'recall': 0.8220024721878862, 'f1': 0.7630522088353414, 'number': 809} | {'precision': 0.40869565217391307, 'recall': 0.3949579831932773, 'f1': 0.4017094017094017, 'number': 119} | {'precision': 0.8032056990204809, 'recall': 0.8469483568075117, 'f1': 0.8244972577696525, 'number': 1065} | 0.7431 | 0.8098 | 0.7750 | 0.8148 |
| 0.1761 | 6.0 | 450 | 0.7601 | {'precision': 0.7114754098360656, 'recall': 0.8046971569839307, 'f1': 0.7552204176334106, 'number': 809} | {'precision': 0.4482758620689655, 'recall': 0.4369747899159664, 'f1': 0.44255319148936173, 'number': 119} | {'precision': 0.8157156220767072, 'recall': 0.8187793427230047, 'f1': 0.8172446110590441, 'number': 1065} | 0.75 | 0.7903 | 0.7696 | 0.8165 |
| 0.1326 | 7.0 | 525 | 0.8064 | {'precision': 0.7289719626168224, 'recall': 0.7713226205191595, 'f1': 0.7495495495495494, 'number': 809} | {'precision': 0.41134751773049644, 'recall': 0.48739495798319327, 'f1': 0.4461538461538461, 'number': 119} | {'precision': 0.7851528384279476, 'recall': 0.844131455399061, 'f1': 0.813574660633484, 'number': 1065} | 0.7381 | 0.7933 | 0.7647 | 0.8066 |
| 0.104 | 8.0 | 600 | 0.8490 | {'precision': 0.7248618784530386, 'recall': 0.8108776266996292, 'f1': 0.765460910151692, 'number': 809} | {'precision': 0.4154929577464789, 'recall': 0.4957983193277311, 'f1': 0.4521072796934866, 'number': 119} | {'precision': 0.8113382899628253, 'recall': 0.819718309859155, 'f1': 0.8155067725361981, 'number': 1065} | 0.7480 | 0.7968 | 0.7716 | 0.8095 |
| 0.0751 | 9.0 | 675 | 0.8807 | {'precision': 0.7271714922048997, 'recall': 0.8071693448702101, 'f1': 0.7650849443468072, 'number': 809} | {'precision': 0.40625, 'recall': 0.4369747899159664, 'f1': 0.4210526315789474, 'number': 119} | {'precision': 0.8076580587711487, 'recall': 0.8516431924882629, 'f1': 0.8290676416819013, 'number': 1065} | 0.7501 | 0.8088 | 0.7784 | 0.8105 |
| 0.0556 | 10.0 | 750 | 0.9078 | {'precision': 0.7152466367713004, 'recall': 0.788627935723115, 'f1': 0.7501469723691946, 'number': 809} | {'precision': 0.4014084507042254, 'recall': 0.4789915966386555, 'f1': 0.4367816091954024, 'number': 119} | {'precision': 0.8066604995374653, 'recall': 0.8187793427230047, 'f1': 0.8126747437092265, 'number': 1065} | 0.7409 | 0.7863 | 0.7629 | 0.8071 |
| 0.0494 | 11.0 | 825 | 0.9615 | {'precision': 0.7342342342342343, 'recall': 0.8059332509270705, 'f1': 0.7684148497348262, 'number': 809} | {'precision': 0.4206896551724138, 'recall': 0.5126050420168067, 'f1': 0.46212121212121215, 'number': 119} | {'precision': 0.8015943312666076, 'recall': 0.8497652582159625, 'f1': 0.8249772105742936, 'number': 1065} | 0.7484 | 0.8118 | 0.7788 | 0.8022 |
| 0.0383 | 12.0 | 900 | 0.9451 | {'precision': 0.7216721672167217, 'recall': 0.8108776266996292, 'f1': 0.7636786961583235, 'number': 809} | {'precision': 0.3935483870967742, 'recall': 0.5126050420168067, 'f1': 0.44525547445255476, 'number': 119} | {'precision': 0.8148487626031164, 'recall': 0.8347417840375587, 'f1': 0.8246753246753246, 'number': 1065} | 0.7452 | 0.8058 | 0.7743 | 0.8148 |
| 0.0316 | 13.0 | 975 | 0.9593 | {'precision': 0.734533183352081, 'recall': 0.8071693448702101, 'f1': 0.769140164899882, 'number': 809} | {'precision': 0.4025974025974026, 'recall': 0.5210084033613446, 'f1': 0.45421245421245426, 'number': 119} | {'precision': 0.8153564899451554, 'recall': 0.8375586854460094, 'f1': 0.826308476146364, 'number': 1065} | 0.7520 | 0.8063 | 0.7782 | 0.8120 |
| 0.0286 | 14.0 | 1050 | 0.9804 | {'precision': 0.7295173961840629, 'recall': 0.8034610630407911, 'f1': 0.7647058823529411, 'number': 809} | {'precision': 0.4246575342465753, 'recall': 0.5210084033613446, 'f1': 0.46792452830188674, 'number': 119} | {'precision': 0.8177697189483227, 'recall': 0.8469483568075117, 'f1': 0.8321033210332104, 'number': 1065} | 0.7542 | 0.8098 | 0.7810 | 0.8130 |
| 0.0273 | 15.0 | 1125 | 0.9802 | {'precision': 0.7281879194630873, 'recall': 0.8046971569839307, 'f1': 0.7645331767469172, 'number': 809} | {'precision': 0.43884892086330934, 'recall': 0.5126050420168067, 'f1': 0.4728682170542636, 'number': 119} | {'precision': 0.8128390596745028, 'recall': 0.844131455399061, 'f1': 0.8281897742975588, 'number': 1065} | 0.7532 | 0.8083 | 0.7798 | 0.8137 |
### Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.2.0
- Tokenizers 0.22.0
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