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---
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: 0.7180
- Answer: {'precision': 0.7269700332963374, 'recall': 0.8096415327564895, 'f1': 0.7660818713450291, 'number': 809}
- Header: {'precision': 0.2992125984251969, 'recall': 0.31932773109243695, 'f1': 0.30894308943089427, 'number': 119}
- Question: {'precision': 0.7881205673758865, 'recall': 0.8347417840375587, 'f1': 0.8107615139078888, 'number': 1065}
- Overall Precision: 0.7338
- Overall Recall: 0.7938
- Overall F1: 0.7626
- Overall Accuracy: 0.8036
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7814 | 1.0 | 10 | 1.5919 | {'precision': 0.015602836879432624, 'recall': 0.013597033374536464, 'f1': 0.01453104359313078, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.14666666666666667, 'recall': 0.09295774647887324, 'f1': 0.11379310344827587, 'number': 1065} | 0.0797 | 0.0552 | 0.0652 | 0.3502 |
| 1.446 | 2.0 | 20 | 1.2482 | {'precision': 0.15749235474006115, 'recall': 0.1273176761433869, 'f1': 0.14080656185919346, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.45069393718042366, 'recall': 0.5793427230046948, 'f1': 0.5069843878389482, 'number': 1065} | 0.3559 | 0.3613 | 0.3586 | 0.5919 |
| 1.0955 | 3.0 | 30 | 0.9653 | {'precision': 0.44740024183796856, 'recall': 0.4573547589616811, 'f1': 0.45232273838630804, 'number': 809} | {'precision': 0.030303030303030304, 'recall': 0.008403361344537815, 'f1': 0.013157894736842105, 'number': 119} | {'precision': 0.5621703089675961, 'recall': 0.7004694835680751, 'f1': 0.6237458193979933, 'number': 1065} | 0.5107 | 0.5605 | 0.5344 | 0.7004 |
| 0.8413 | 4.0 | 40 | 0.8063 | {'precision': 0.6070686070686071, 'recall': 0.7218788627935723, 'f1': 0.6595143986448335, 'number': 809} | {'precision': 0.1111111111111111, 'recall': 0.05042016806722689, 'f1': 0.06936416184971099, 'number': 119} | {'precision': 0.6669527896995708, 'recall': 0.7295774647887324, 'f1': 0.6968609865470853, 'number': 1065} | 0.6268 | 0.6859 | 0.6550 | 0.7495 |
| 0.6708 | 5.0 | 50 | 0.7123 | {'precision': 0.6703539823008849, 'recall': 0.7490729295426453, 'f1': 0.7075306479859895, 'number': 809} | {'precision': 0.11458333333333333, 'recall': 0.09243697478991597, 'f1': 0.10232558139534885, 'number': 119} | {'precision': 0.6787401574803149, 'recall': 0.8093896713615023, 'f1': 0.7383297644539615, 'number': 1065} | 0.6515 | 0.7421 | 0.6939 | 0.7841 |
| 0.566 | 6.0 | 60 | 0.7005 | {'precision': 0.6466466466466466, 'recall': 0.7985166872682324, 'f1': 0.7146017699115044, 'number': 809} | {'precision': 0.13725490196078433, 'recall': 0.11764705882352941, 'f1': 0.12669683257918554, 'number': 119} | {'precision': 0.7279151943462897, 'recall': 0.7737089201877935, 'f1': 0.7501137915339099, 'number': 1065} | 0.6646 | 0.7446 | 0.7023 | 0.7826 |
| 0.4969 | 7.0 | 70 | 0.6764 | {'precision': 0.6817691477885652, 'recall': 0.7812113720642769, 'f1': 0.7281105990783411, 'number': 809} | {'precision': 0.21666666666666667, 'recall': 0.2184873949579832, 'f1': 0.21757322175732216, 'number': 119} | {'precision': 0.7367521367521368, 'recall': 0.8093896713615023, 'f1': 0.7713646532438478, 'number': 1065} | 0.6856 | 0.7627 | 0.7221 | 0.7950 |
| 0.4295 | 8.0 | 80 | 0.6735 | {'precision': 0.7071823204419889, 'recall': 0.7911001236093943, 'f1': 0.7467911318553092, 'number': 809} | {'precision': 0.21311475409836064, 'recall': 0.2184873949579832, 'f1': 0.21576763485477177, 'number': 119} | {'precision': 0.7489397794741306, 'recall': 0.8291079812206573, 'f1': 0.78698752228164, 'number': 1065} | 0.7022 | 0.7772 | 0.7378 | 0.7992 |
| 0.3774 | 9.0 | 90 | 0.6814 | {'precision': 0.7023809523809523, 'recall': 0.8022249690976514, 'f1': 0.7489901904212348, 'number': 809} | {'precision': 0.2773109243697479, 'recall': 0.2773109243697479, 'f1': 0.2773109243697479, 'number': 119} | {'precision': 0.7639372822299652, 'recall': 0.8234741784037559, 'f1': 0.7925892453682785, 'number': 1065} | 0.7115 | 0.7822 | 0.7452 | 0.8047 |
| 0.3678 | 10.0 | 100 | 0.6885 | {'precision': 0.7193370165745856, 'recall': 0.8046971569839307, 'f1': 0.7596266044340723, 'number': 809} | {'precision': 0.264, 'recall': 0.2773109243697479, 'f1': 0.27049180327868855, 'number': 119} | {'precision': 0.7690972222222222, 'recall': 0.831924882629108, 'f1': 0.799278304014434, 'number': 1065} | 0.7195 | 0.7878 | 0.7521 | 0.8050 |
| 0.3137 | 11.0 | 110 | 0.6976 | {'precision': 0.7161572052401747, 'recall': 0.8108776266996292, 'f1': 0.7605797101449275, 'number': 809} | {'precision': 0.27007299270072993, 'recall': 0.31092436974789917, 'f1': 0.2890625, 'number': 119} | {'precision': 0.7719756309834639, 'recall': 0.8328638497652582, 'f1': 0.8012646793134598, 'number': 1065} | 0.7175 | 0.7928 | 0.7533 | 0.8018 |
| 0.2983 | 12.0 | 120 | 0.6996 | {'precision': 0.7108433734939759, 'recall': 0.8022249690976514, 'f1': 0.7537746806039489, 'number': 809} | {'precision': 0.304, 'recall': 0.31932773109243695, 'f1': 0.31147540983606553, 'number': 119} | {'precision': 0.7810283687943262, 'recall': 0.8272300469483568, 'f1': 0.8034655722754217, 'number': 1065} | 0.7239 | 0.7868 | 0.7540 | 0.8029 |
| 0.2786 | 13.0 | 130 | 0.7114 | {'precision': 0.7172949002217295, 'recall': 0.799752781211372, 'f1': 0.7562828755113968, 'number': 809} | {'precision': 0.29838709677419356, 'recall': 0.31092436974789917, 'f1': 0.3045267489711935, 'number': 119} | {'precision': 0.7814977973568282, 'recall': 0.8328638497652582, 'f1': 0.8063636363636365, 'number': 1065} | 0.7270 | 0.7883 | 0.7564 | 0.8019 |
| 0.2632 | 14.0 | 140 | 0.7151 | {'precision': 0.7256438969764838, 'recall': 0.8009888751545118, 'f1': 0.7614571092831962, 'number': 809} | {'precision': 0.3064516129032258, 'recall': 0.31932773109243695, 'f1': 0.31275720164609055, 'number': 119} | {'precision': 0.7858407079646018, 'recall': 0.8338028169014085, 'f1': 0.8091116173120729, 'number': 1065} | 0.7331 | 0.7898 | 0.7604 | 0.8033 |
| 0.2668 | 15.0 | 150 | 0.7180 | {'precision': 0.7269700332963374, 'recall': 0.8096415327564895, 'f1': 0.7660818713450291, 'number': 809} | {'precision': 0.2992125984251969, 'recall': 0.31932773109243695, 'f1': 0.30894308943089427, 'number': 119} | {'precision': 0.7881205673758865, 'recall': 0.8347417840375587, 'f1': 0.8107615139078888, 'number': 1065} | 0.7338 | 0.7938 | 0.7626 | 0.8036 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Tokenizers 0.19.1
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