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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: 0.7080
- Answer: {'precision': 0.7122381477398015, 'recall': 0.7985166872682324, 'f1': 0.752913752913753, 'number': 809}
- Header: {'precision': 0.3359375, 'recall': 0.36134453781512604, 'f1': 0.3481781376518218, 'number': 119}
- Question: {'precision': 0.7817531305903399, 'recall': 0.8206572769953052, 'f1': 0.8007329363261567, 'number': 1065}
- Overall Precision: 0.7260
- Overall Recall: 0.7842
- Overall F1: 0.7540
- Overall Accuracy: 0.8073
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.4164 | 1.0 | 10 | 1.1867 | {'precision': 0.21566110397946084, 'recall': 0.207663782447466, 'f1': 0.21158690176322417, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.48124557678697805, 'recall': 0.6384976525821596, 'f1': 0.5488297013720743, 'number': 1065} | 0.3869 | 0.4255 | 0.4053 | 0.6139 |
| 1.0235 | 2.0 | 20 | 0.8815 | {'precision': 0.578494623655914, 'recall': 0.6650185414091471, 'f1': 0.6187464059804485, 'number': 809} | {'precision': 0.05555555555555555, 'recall': 0.008403361344537815, 'f1': 0.014598540145985401, 'number': 119} | {'precision': 0.6398687448728466, 'recall': 0.7323943661971831, 'f1': 0.6830122591943958, 'number': 1065} | 0.6087 | 0.6618 | 0.6341 | 0.7403 |
| 0.7822 | 3.0 | 30 | 0.7564 | {'precision': 0.6335403726708074, 'recall': 0.7564894932014833, 'f1': 0.6895774647887324, 'number': 809} | {'precision': 0.13559322033898305, 'recall': 0.06722689075630252, 'f1': 0.0898876404494382, 'number': 119} | {'precision': 0.6905158069883528, 'recall': 0.7793427230046949, 'f1': 0.7322452580502868, 'number': 1065} | 0.6511 | 0.7275 | 0.6872 | 0.7697 |
| 0.6495 | 4.0 | 40 | 0.6955 | {'precision': 0.6533333333333333, 'recall': 0.7873918417799752, 'f1': 0.7141255605381165, 'number': 809} | {'precision': 0.19480519480519481, 'recall': 0.12605042016806722, 'f1': 0.15306122448979592, 'number': 119} | {'precision': 0.7162276975361087, 'recall': 0.7915492957746478, 'f1': 0.752007136485281, 'number': 1065} | 0.6707 | 0.7501 | 0.7082 | 0.7915 |
| 0.5641 | 5.0 | 50 | 0.6796 | {'precision': 0.6843267108167771, 'recall': 0.7663782447466008, 'f1': 0.7230320699708457, 'number': 809} | {'precision': 0.275, 'recall': 0.18487394957983194, 'f1': 0.22110552763819097, 'number': 119} | {'precision': 0.7565217391304347, 'recall': 0.8169014084507042, 'f1': 0.7855530474040633, 'number': 1065} | 0.7079 | 0.7587 | 0.7324 | 0.7899 |
| 0.4862 | 6.0 | 60 | 0.6563 | {'precision': 0.6844978165938864, 'recall': 0.7750309023485785, 'f1': 0.7269565217391305, 'number': 809} | {'precision': 0.28, 'recall': 0.23529411764705882, 'f1': 0.2557077625570776, 'number': 119} | {'precision': 0.7420168067226891, 'recall': 0.8291079812206573, 'f1': 0.7831485587583149, 'number': 1065} | 0.6972 | 0.7717 | 0.7326 | 0.8007 |
| 0.4389 | 7.0 | 70 | 0.6444 | {'precision': 0.6868365180467091, 'recall': 0.799752781211372, 'f1': 0.7390062821245003, 'number': 809} | {'precision': 0.28703703703703703, 'recall': 0.2605042016806723, 'f1': 0.27312775330396477, 'number': 119} | {'precision': 0.7411167512690355, 'recall': 0.8225352112676056, 'f1': 0.7797062750333779, 'number': 1065} | 0.6962 | 0.7797 | 0.7356 | 0.8040 |
| 0.3912 | 8.0 | 80 | 0.6505 | {'precision': 0.7074527252502781, 'recall': 0.7861557478368356, 'f1': 0.7447306791569087, 'number': 809} | {'precision': 0.3392857142857143, 'recall': 0.31932773109243695, 'f1': 0.32900432900432897, 'number': 119} | {'precision': 0.7689594356261023, 'recall': 0.8187793427230047, 'f1': 0.793087767166894, 'number': 1065} | 0.7207 | 0.7757 | 0.7472 | 0.8073 |
| 0.3511 | 9.0 | 90 | 0.6696 | {'precision': 0.7147577092511013, 'recall': 0.8022249690976514, 'f1': 0.7559697146185206, 'number': 809} | {'precision': 0.296, 'recall': 0.31092436974789917, 'f1': 0.30327868852459017, 'number': 119} | {'precision': 0.7589833479404031, 'recall': 0.8131455399061033, 'f1': 0.7851314596554851, 'number': 1065} | 0.7139 | 0.7787 | 0.7449 | 0.8042 |
| 0.3166 | 10.0 | 100 | 0.6746 | {'precision': 0.7190265486725663, 'recall': 0.8034610630407911, 'f1': 0.7589025102159953, 'number': 809} | {'precision': 0.35398230088495575, 'recall': 0.33613445378151263, 'f1': 0.3448275862068966, 'number': 119} | {'precision': 0.7753108348134992, 'recall': 0.819718309859155, 'f1': 0.7968963943404839, 'number': 1065} | 0.7294 | 0.7842 | 0.7558 | 0.8081 |
| 0.2925 | 11.0 | 110 | 0.6839 | {'precision': 0.7160356347438753, 'recall': 0.7948084054388134, 'f1': 0.753368482718219, 'number': 809} | {'precision': 0.3208955223880597, 'recall': 0.36134453781512604, 'f1': 0.33992094861660077, 'number': 119} | {'precision': 0.7803780378037803, 'recall': 0.8140845070422535, 'f1': 0.796875, 'number': 1065} | 0.7247 | 0.7792 | 0.7510 | 0.8087 |
| 0.2837 | 12.0 | 120 | 0.6853 | {'precision': 0.7161862527716186, 'recall': 0.7985166872682324, 'f1': 0.7551139684395091, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.3445378151260504, 'f1': 0.33884297520661155, 'number': 119} | {'precision': 0.7751322751322751, 'recall': 0.8253521126760563, 'f1': 0.7994542974079127, 'number': 1065} | 0.7253 | 0.7858 | 0.7543 | 0.8064 |
| 0.265 | 13.0 | 130 | 0.7016 | {'precision': 0.7069154774972558, 'recall': 0.796044499381953, 'f1': 0.7488372093023256, 'number': 809} | {'precision': 0.31654676258992803, 'recall': 0.3697478991596639, 'f1': 0.3410852713178294, 'number': 119} | {'precision': 0.7867513611615246, 'recall': 0.8140845070422535, 'f1': 0.8001845869866173, 'number': 1065} | 0.7226 | 0.7802 | 0.7503 | 0.8076 |
| 0.2475 | 14.0 | 140 | 0.7055 | {'precision': 0.7084708470847084, 'recall': 0.796044499381953, 'f1': 0.749708963911525, 'number': 809} | {'precision': 0.32575757575757575, 'recall': 0.36134453781512604, 'f1': 0.3426294820717131, 'number': 119} | {'precision': 0.771806167400881, 'recall': 0.8225352112676056, 'f1': 0.7963636363636363, 'number': 1065} | 0.7183 | 0.7842 | 0.7498 | 0.8054 |
| 0.2423 | 15.0 | 150 | 0.7080 | {'precision': 0.7122381477398015, 'recall': 0.7985166872682324, 'f1': 0.752913752913753, 'number': 809} | {'precision': 0.3359375, 'recall': 0.36134453781512604, 'f1': 0.3481781376518218, 'number': 119} | {'precision': 0.7817531305903399, 'recall': 0.8206572769953052, 'f1': 0.8007329363261567, 'number': 1065} | 0.7260 | 0.7842 | 0.7540 | 0.8073 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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