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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: 0.6909
- Answer: {'precision': 0.7051835853131749, 'recall': 0.8071693448702101, 'f1': 0.7527377521613834, 'number': 809}
- Header: {'precision': 0.3418803418803419, 'recall': 0.33613445378151263, 'f1': 0.3389830508474576, 'number': 119}
- Question: {'precision': 0.7631352282515074, 'recall': 0.831924882629108, 'f1': 0.7960467205750225, 'number': 1065}
- Overall Precision: 0.7164
- Overall Recall: 0.7923
- Overall F1: 0.7524
- Overall Accuracy: 0.8064
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7913 | 1.0 | 10 | 1.5806 | {'precision': 0.02405857740585774, 'recall': 0.02843016069221261, 'f1': 0.026062322946175637, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.17197452229299362, 'recall': 0.15211267605633802, 'f1': 0.16143497757847533, 'number': 1065} | 0.0975 | 0.0928 | 0.0951 | 0.3662 |
| 1.4607 | 2.0 | 20 | 1.2580 | {'precision': 0.22879464285714285, 'recall': 0.25339925834363414, 'f1': 0.2404692082111437, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.41384499623777277, 'recall': 0.5164319248826291, 'f1': 0.4594820384294069, 'number': 1065} | 0.3393 | 0.3788 | 0.3580 | 0.5702 |
| 1.104 | 3.0 | 30 | 0.9936 | {'precision': 0.4552058111380145, 'recall': 0.4647713226205192, 'f1': 0.4599388379204893, 'number': 809} | {'precision': 0.14705882352941177, 'recall': 0.04201680672268908, 'f1': 0.06535947712418301, 'number': 119} | {'precision': 0.5559471365638766, 'recall': 0.5924882629107981, 'f1': 0.5736363636363637, 'number': 1065} | 0.5073 | 0.5078 | 0.5075 | 0.6862 |
| 0.8426 | 4.0 | 40 | 0.8075 | {'precision': 0.5957918050941307, 'recall': 0.6650185414091471, 'f1': 0.6285046728971962, 'number': 809} | {'precision': 0.3220338983050847, 'recall': 0.15966386554621848, 'f1': 0.21348314606741572, 'number': 119} | {'precision': 0.6645739910313901, 'recall': 0.6957746478873239, 'f1': 0.6798165137614679, 'number': 1065} | 0.6249 | 0.6513 | 0.6378 | 0.7554 |
| 0.6743 | 5.0 | 50 | 0.7167 | {'precision': 0.6370370370370371, 'recall': 0.7441285537700866, 'f1': 0.6864310148232612, 'number': 809} | {'precision': 0.35365853658536583, 'recall': 0.24369747899159663, 'f1': 0.2885572139303482, 'number': 119} | {'precision': 0.6849192100538599, 'recall': 0.7164319248826291, 'f1': 0.700321248279027, 'number': 1065} | 0.6511 | 0.6994 | 0.6744 | 0.7781 |
| 0.5571 | 6.0 | 60 | 0.6785 | {'precision': 0.6492146596858639, 'recall': 0.7663782447466008, 'f1': 0.7029478458049887, 'number': 809} | {'precision': 0.36585365853658536, 'recall': 0.25210084033613445, 'f1': 0.29850746268656714, 'number': 119} | {'precision': 0.6846275752773375, 'recall': 0.8112676056338028, 'f1': 0.742587021916631, 'number': 1065} | 0.6585 | 0.7597 | 0.7055 | 0.7929 |
| 0.4858 | 7.0 | 70 | 0.6678 | {'precision': 0.6611740473738414, 'recall': 0.7935723114956736, 'f1': 0.7213483146067416, 'number': 809} | {'precision': 0.39080459770114945, 'recall': 0.2857142857142857, 'f1': 0.33009708737864074, 'number': 119} | {'precision': 0.7212543554006968, 'recall': 0.7774647887323943, 'f1': 0.7483054676909172, 'number': 1065} | 0.6818 | 0.7546 | 0.7164 | 0.7961 |
| 0.4397 | 8.0 | 80 | 0.6626 | {'precision': 0.6826608505997819, 'recall': 0.7737948084054388, 'f1': 0.7253765932792584, 'number': 809} | {'precision': 0.32673267326732675, 'recall': 0.2773109243697479, 'f1': 0.30000000000000004, 'number': 119} | {'precision': 0.742437337942956, 'recall': 0.8065727699530516, 'f1': 0.7731773177317731, 'number': 1065} | 0.6979 | 0.7617 | 0.7284 | 0.8015 |
| 0.393 | 9.0 | 90 | 0.6611 | {'precision': 0.6856223175965666, 'recall': 0.7898640296662547, 'f1': 0.7340608845491098, 'number': 809} | {'precision': 0.30833333333333335, 'recall': 0.31092436974789917, 'f1': 0.3096234309623431, 'number': 119} | {'precision': 0.7425658453695837, 'recall': 0.8206572769953052, 'f1': 0.7796610169491525, 'number': 1065} | 0.6954 | 0.7777 | 0.7342 | 0.8020 |
| 0.351 | 10.0 | 100 | 0.6665 | {'precision': 0.6994535519125683, 'recall': 0.7911001236093943, 'f1': 0.7424593967517401, 'number': 809} | {'precision': 0.33043478260869563, 'recall': 0.31932773109243695, 'f1': 0.32478632478632474, 'number': 119} | {'precision': 0.7415254237288136, 'recall': 0.8215962441314554, 'f1': 0.7795100222717148, 'number': 1065} | 0.7027 | 0.7792 | 0.7390 | 0.8054 |
| 0.3187 | 11.0 | 110 | 0.6752 | {'precision': 0.6963123644251626, 'recall': 0.7935723114956736, 'f1': 0.7417677642980935, 'number': 809} | {'precision': 0.3275862068965517, 'recall': 0.31932773109243695, 'f1': 0.3234042553191489, 'number': 119} | {'precision': 0.7708516242317822, 'recall': 0.8244131455399061, 'f1': 0.7967332123411976, 'number': 1065} | 0.7157 | 0.7817 | 0.7472 | 0.8076 |
| 0.3034 | 12.0 | 120 | 0.6826 | {'precision': 0.6970998925886144, 'recall': 0.8022249690976514, 'f1': 0.7459770114942528, 'number': 809} | {'precision': 0.3486238532110092, 'recall': 0.31932773109243695, 'f1': 0.3333333333333333, 'number': 119} | {'precision': 0.7675814751286449, 'recall': 0.8403755868544601, 'f1': 0.8023307933662035, 'number': 1065} | 0.7171 | 0.7938 | 0.7535 | 0.8080 |
| 0.2825 | 13.0 | 130 | 0.6909 | {'precision': 0.6901408450704225, 'recall': 0.7873918417799752, 'f1': 0.7355658198614318, 'number': 809} | {'precision': 0.3228346456692913, 'recall': 0.3445378151260504, 'f1': 0.3333333333333333, 'number': 119} | {'precision': 0.7626086956521739, 'recall': 0.8234741784037559, 'f1': 0.7918735891647856, 'number': 1065} | 0.7068 | 0.7802 | 0.7417 | 0.8055 |
| 0.2745 | 14.0 | 140 | 0.6884 | {'precision': 0.7039827771797632, 'recall': 0.8084054388133498, 'f1': 0.7525891829689298, 'number': 809} | {'precision': 0.33620689655172414, 'recall': 0.3277310924369748, 'f1': 0.33191489361702126, 'number': 119} | {'precision': 0.7651122625215889, 'recall': 0.831924882629108, 'f1': 0.7971210076473234, 'number': 1065} | 0.7167 | 0.7923 | 0.7526 | 0.8070 |
| 0.2711 | 15.0 | 150 | 0.6909 | {'precision': 0.7051835853131749, 'recall': 0.8071693448702101, 'f1': 0.7527377521613834, 'number': 809} | {'precision': 0.3418803418803419, 'recall': 0.33613445378151263, 'f1': 0.3389830508474576, 'number': 119} | {'precision': 0.7631352282515074, 'recall': 0.831924882629108, 'f1': 0.7960467205750225, 'number': 1065} | 0.7164 | 0.7923 | 0.7524 | 0.8064 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.13.2
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