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---
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: 0.7011
- Answer: {'precision': 0.7142857142857143, 'recall': 0.8096415327564895, 'f1': 0.7589803012746235, 'number': 809}
- Header: {'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119}
- Question: {'precision': 0.7859712230215827, 'recall': 0.8206572769953052, 'f1': 0.8029398254478639, 'number': 1065}
- Overall Precision: 0.7250
- Overall Recall: 0.7873
- Overall F1: 0.7549
- Overall Accuracy: 0.8102
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7566 | 1.0 | 10 | 1.5349 | {'precision': 0.03646308113035551, 'recall': 0.049443757725587144, 'f1': 0.04197271773347323, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.16700819672131148, 'recall': 0.15305164319248826, 'f1': 0.15972562469377757, 'number': 1065} | 0.0979 | 0.1019 | 0.0999 | 0.4336 |
| 1.4057 | 2.0 | 20 | 1.1865 | {'precision': 0.17656500802568217, 'recall': 0.13597033374536466, 'f1': 0.15363128491620115, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.471847739888977, 'recall': 0.5586854460093896, 'f1': 0.5116079105760963, 'number': 1065} | 0.3742 | 0.3537 | 0.3637 | 0.6016 |
| 1.0729 | 3.0 | 30 | 0.9241 | {'precision': 0.49693251533742333, 'recall': 0.5006180469715699, 'f1': 0.4987684729064039, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.6378708551483421, 'recall': 0.6863849765258216, 'f1': 0.6612392582541836, 'number': 1065} | 0.5691 | 0.5700 | 0.5696 | 0.7181 |
| 0.8134 | 4.0 | 40 | 0.7831 | {'precision': 0.6211640211640211, 'recall': 0.7255871446229913, 'f1': 0.669327251995439, 'number': 809} | {'precision': 0.09375, 'recall': 0.05042016806722689, 'f1': 0.0655737704918033, 'number': 119} | {'precision': 0.6889081455805892, 'recall': 0.7464788732394366, 'f1': 0.7165389815232085, 'number': 1065} | 0.6417 | 0.6964 | 0.6679 | 0.7640 |
| 0.6582 | 5.0 | 50 | 0.7298 | {'precision': 0.6422018348623854, 'recall': 0.7787391841779975, 'f1': 0.7039106145251396, 'number': 809} | {'precision': 0.2361111111111111, 'recall': 0.14285714285714285, 'f1': 0.17801047120418848, 'number': 119} | {'precision': 0.7311233885819521, 'recall': 0.7455399061032864, 'f1': 0.7382612738261274, 'number': 1065} | 0.6737 | 0.7230 | 0.6975 | 0.7761 |
| 0.553 | 6.0 | 60 | 0.6763 | {'precision': 0.6673532440782698, 'recall': 0.8009888751545118, 'f1': 0.7280898876404494, 'number': 809} | {'precision': 0.25806451612903225, 'recall': 0.20168067226890757, 'f1': 0.22641509433962265, 'number': 119} | {'precision': 0.735445205479452, 'recall': 0.8065727699530516, 'f1': 0.7693685624720108, 'number': 1065} | 0.6859 | 0.7682 | 0.7247 | 0.7962 |
| 0.4805 | 7.0 | 70 | 0.6797 | {'precision': 0.6904255319148936, 'recall': 0.8022249690976514, 'f1': 0.7421383647798742, 'number': 809} | {'precision': 0.25925925925925924, 'recall': 0.23529411764705882, 'f1': 0.24669603524229072, 'number': 119} | {'precision': 0.7363945578231292, 'recall': 0.8131455399061033, 'f1': 0.7728692547969657, 'number': 1065} | 0.6938 | 0.7742 | 0.7318 | 0.7970 |
| 0.4259 | 8.0 | 80 | 0.6726 | {'precision': 0.689401888772298, 'recall': 0.8121137206427689, 'f1': 0.7457434733257663, 'number': 809} | {'precision': 0.24786324786324787, 'recall': 0.24369747899159663, 'f1': 0.24576271186440676, 'number': 119} | {'precision': 0.7463581833761782, 'recall': 0.8178403755868544, 'f1': 0.7804659498207885, 'number': 1065} | 0.6960 | 0.7812 | 0.7362 | 0.8020 |
| 0.3787 | 9.0 | 90 | 0.6784 | {'precision': 0.7043956043956044, 'recall': 0.792336217552534, 'f1': 0.7457824316463061, 'number': 809} | {'precision': 0.26229508196721313, 'recall': 0.2689075630252101, 'f1': 0.26556016597510373, 'number': 119} | {'precision': 0.779707495429616, 'recall': 0.8009389671361502, 'f1': 0.7901806391848076, 'number': 1065} | 0.7178 | 0.7657 | 0.7410 | 0.8026 |
| 0.3411 | 10.0 | 100 | 0.6821 | {'precision': 0.7015086206896551, 'recall': 0.8046971569839307, 'f1': 0.7495682210708117, 'number': 809} | {'precision': 0.2708333333333333, 'recall': 0.3277310924369748, 'f1': 0.2965779467680608, 'number': 119} | {'precision': 0.775200713648528, 'recall': 0.815962441314554, 'f1': 0.7950594693504116, 'number': 1065} | 0.7109 | 0.7822 | 0.7449 | 0.8047 |
| 0.313 | 11.0 | 110 | 0.7129 | {'precision': 0.7111111111111111, 'recall': 0.7911001236093943, 'f1': 0.7489760093622002, 'number': 809} | {'precision': 0.2835820895522388, 'recall': 0.31932773109243695, 'f1': 0.30039525691699603, 'number': 119} | {'precision': 0.7816711590296496, 'recall': 0.8169014084507042, 'f1': 0.7988980716253444, 'number': 1065} | 0.7210 | 0.7767 | 0.7478 | 0.7994 |
| 0.297 | 12.0 | 120 | 0.6955 | {'precision': 0.708779443254818, 'recall': 0.8182941903584673, 'f1': 0.759609868043603, 'number': 809} | {'precision': 0.291044776119403, 'recall': 0.3277310924369748, 'f1': 0.308300395256917, 'number': 119} | {'precision': 0.783978397839784, 'recall': 0.8178403755868544, 'f1': 0.8005514705882352, 'number': 1065} | 0.7214 | 0.7888 | 0.7536 | 0.8103 |
| 0.2907 | 13.0 | 130 | 0.7098 | {'precision': 0.7092511013215859, 'recall': 0.796044499381953, 'f1': 0.7501456027955737, 'number': 809} | {'precision': 0.3142857142857143, 'recall': 0.3697478991596639, 'f1': 0.33976833976833976, 'number': 119} | {'precision': 0.7896678966789668, 'recall': 0.8037558685446009, 'f1': 0.796649604467194, 'number': 1065} | 0.7242 | 0.7747 | 0.7486 | 0.8052 |
| 0.2701 | 14.0 | 140 | 0.7006 | {'precision': 0.7133479212253829, 'recall': 0.8059332509270705, 'f1': 0.7568195008705745, 'number': 809} | {'precision': 0.3037037037037037, 'recall': 0.3445378151260504, 'f1': 0.3228346456692913, 'number': 119} | {'precision': 0.7894736842105263, 'recall': 0.8169014084507042, 'f1': 0.8029533917858791, 'number': 1065} | 0.7266 | 0.7842 | 0.7543 | 0.8091 |
| 0.2649 | 15.0 | 150 | 0.7011 | {'precision': 0.7142857142857143, 'recall': 0.8096415327564895, 'f1': 0.7589803012746235, 'number': 809} | {'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119} | {'precision': 0.7859712230215827, 'recall': 0.8206572769953052, 'f1': 0.8029398254478639, 'number': 1065} | 0.7250 | 0.7873 | 0.7549 | 0.8102 |
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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