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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
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: 0.7064
- Answer: {'precision': 0.70801317233809, 'recall': 0.7972805933250927, 'f1': 0.75, 'number': 809}
- Header: {'precision': 0.36, 'recall': 0.37815126050420167, 'f1': 0.3688524590163934, 'number': 119}
- Question: {'precision': 0.7894736842105263, 'recall': 0.8309859154929577, 'f1': 0.8096980786825252, 'number': 1065}
- Overall Precision: 0.7302
- Overall Recall: 0.7903
- Overall F1: 0.7590
- Overall Accuracy: 0.8069
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7527 | 1.0 | 10 | 1.5609 | {'precision': 0.027744270205066344, 'recall': 0.02843016069221261, 'f1': 0.02808302808302808, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.24188129899216126, 'recall': 0.2028169014084507, 'f1': 0.22063329928498468, 'number': 1065} | 0.1388 | 0.1199 | 0.1287 | 0.3775 |
| 1.4189 | 2.0 | 20 | 1.1905 | {'precision': 0.23932729624838292, 'recall': 0.22867737948084055, 'f1': 0.23388116308470291, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.43356164383561646, 'recall': 0.5943661971830986, 'f1': 0.5013861386138613, 'number': 1065} | 0.3663 | 0.4104 | 0.3871 | 0.6099 |
| 1.0743 | 3.0 | 30 | 0.9279 | {'precision': 0.5135722041259501, 'recall': 0.584672435105068, 'f1': 0.5468208092485548, 'number': 809} | {'precision': 0.09090909090909091, 'recall': 0.008403361344537815, 'f1': 0.015384615384615384, 'number': 119} | {'precision': 0.5574144486692015, 'recall': 0.6882629107981221, 'f1': 0.6159663865546218, 'number': 1065} | 0.5372 | 0.6056 | 0.5693 | 0.7197 |
| 0.8323 | 4.0 | 40 | 0.7781 | {'precision': 0.6182965299684543, 'recall': 0.7268232385661311, 'f1': 0.6681818181818182, 'number': 809} | {'precision': 0.14814814814814814, 'recall': 0.06722689075630252, 'f1': 0.09248554913294797, 'number': 119} | {'precision': 0.6731255265374895, 'recall': 0.7502347417840376, 'f1': 0.7095914742451155, 'number': 1065} | 0.6364 | 0.6999 | 0.6667 | 0.7627 |
| 0.6658 | 5.0 | 50 | 0.7131 | {'precision': 0.6371308016877637, 'recall': 0.7466007416563659, 'f1': 0.6875355719977233, 'number': 809} | {'precision': 0.21686746987951808, 'recall': 0.15126050420168066, 'f1': 0.1782178217821782, 'number': 119} | {'precision': 0.6829066886870355, 'recall': 0.7765258215962442, 'f1': 0.726713532513181, 'number': 1065} | 0.6463 | 0.7270 | 0.6843 | 0.7812 |
| 0.5559 | 6.0 | 60 | 0.7018 | {'precision': 0.6437941473259334, 'recall': 0.788627935723115, 'f1': 0.7088888888888889, 'number': 809} | {'precision': 0.2653061224489796, 'recall': 0.2184873949579832, 'f1': 0.23963133640552997, 'number': 119} | {'precision': 0.7235555555555555, 'recall': 0.7643192488262911, 'f1': 0.7433789954337899, 'number': 1065} | 0.6676 | 0.7416 | 0.7026 | 0.7797 |
| 0.4847 | 7.0 | 70 | 0.6667 | {'precision': 0.6787234042553192, 'recall': 0.788627935723115, 'f1': 0.729559748427673, 'number': 809} | {'precision': 0.23853211009174313, 'recall': 0.2184873949579832, 'f1': 0.2280701754385965, 'number': 119} | {'precision': 0.7450643776824034, 'recall': 0.8150234741784037, 'f1': 0.77847533632287, 'number': 1065} | 0.6920 | 0.7687 | 0.7283 | 0.7982 |
| 0.4247 | 8.0 | 80 | 0.6833 | {'precision': 0.6836518046709129, 'recall': 0.796044499381953, 'f1': 0.7355796687607081, 'number': 809} | {'precision': 0.2578125, 'recall': 0.2773109243697479, 'f1': 0.26720647773279355, 'number': 119} | {'precision': 0.7610008628127696, 'recall': 0.828169014084507, 'f1': 0.7931654676258992, 'number': 1065} | 0.6994 | 0.7822 | 0.7385 | 0.7961 |
| 0.3796 | 9.0 | 90 | 0.6774 | {'precision': 0.7042716319824753, 'recall': 0.7948084054388134, 'f1': 0.7468060394889663, 'number': 809} | {'precision': 0.28688524590163933, 'recall': 0.29411764705882354, 'f1': 0.2904564315352697, 'number': 119} | {'precision': 0.7781785392245266, 'recall': 0.8103286384976526, 'f1': 0.7939282428702852, 'number': 1065} | 0.7188 | 0.7732 | 0.7450 | 0.8022 |
| 0.361 | 10.0 | 100 | 0.6885 | {'precision': 0.7047413793103449, 'recall': 0.8084054388133498, 'f1': 0.7530224525043179, 'number': 809} | {'precision': 0.30833333333333335, 'recall': 0.31092436974789917, 'f1': 0.3096234309623431, 'number': 119} | {'precision': 0.7742504409171076, 'recall': 0.8244131455399061, 'f1': 0.7985447930877672, 'number': 1065} | 0.7191 | 0.7873 | 0.7516 | 0.8045 |
| 0.3089 | 11.0 | 110 | 0.6921 | {'precision': 0.7141292442497261, 'recall': 0.8059332509270705, 'f1': 0.7572590011614402, 'number': 809} | {'precision': 0.3358208955223881, 'recall': 0.37815126050420167, 'f1': 0.3557312252964427, 'number': 119} | {'precision': 0.7929792979297929, 'recall': 0.8272300469483568, 'f1': 0.8097426470588235, 'number': 1065} | 0.7312 | 0.7918 | 0.7603 | 0.8038 |
| 0.295 | 12.0 | 120 | 0.6928 | {'precision': 0.7082872928176795, 'recall': 0.792336217552534, 'f1': 0.7479579929988331, 'number': 809} | {'precision': 0.33070866141732286, 'recall': 0.35294117647058826, 'f1': 0.34146341463414637, 'number': 119} | {'precision': 0.7917414721723519, 'recall': 0.828169014084507, 'f1': 0.8095456631482332, 'number': 1065} | 0.7293 | 0.7852 | 0.7562 | 0.8064 |
| 0.278 | 13.0 | 130 | 0.7052 | {'precision': 0.6988082340195017, 'recall': 0.7972805933250927, 'f1': 0.7448036951501155, 'number': 809} | {'precision': 0.34615384615384615, 'recall': 0.37815126050420167, 'f1': 0.36144578313253006, 'number': 119} | {'precision': 0.7985546522131888, 'recall': 0.8300469483568075, 'f1': 0.8139963167587477, 'number': 1065} | 0.7287 | 0.7898 | 0.7580 | 0.8048 |
| 0.2603 | 14.0 | 140 | 0.7044 | {'precision': 0.7056892778993435, 'recall': 0.7972805933250927, 'f1': 0.7486941381311665, 'number': 809} | {'precision': 0.3492063492063492, 'recall': 0.3697478991596639, 'f1': 0.35918367346938773, 'number': 119} | {'precision': 0.7852706299911268, 'recall': 0.8309859154929577, 'f1': 0.8074817518248176, 'number': 1065} | 0.7263 | 0.7898 | 0.7567 | 0.8074 |
| 0.258 | 15.0 | 150 | 0.7064 | {'precision': 0.70801317233809, 'recall': 0.7972805933250927, 'f1': 0.75, 'number': 809} | {'precision': 0.36, 'recall': 0.37815126050420167, 'f1': 0.3688524590163934, 'number': 119} | {'precision': 0.7894736842105263, 'recall': 0.8309859154929577, 'f1': 0.8096980786825252, 'number': 1065} | 0.7302 | 0.7903 | 0.7590 | 0.8069 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cpu
- Datasets 3.1.0
- Tokenizers 0.20.3
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