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---
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license: mit
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base_model: microsoft/layoutlm-base-uncased
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tags:
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- generated_from_trainer
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datasets:
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- layoutlmv3
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model-index:
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- name: Layoutlm_Inkaso_2
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Layoutlm_Inkaso_2
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This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the layoutlmv3 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1191
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- Creditor address: {'precision': 0.9807692307692307, 'recall': 0.9622641509433962, 'f1': 0.9714285714285713, 'number': 53}
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- Creditor name: {'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 35}
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- Creditor proxy: {'precision': 0.75, 'recall': 0.8823529411764706, 'f1': 0.8108108108108107, 'number': 34}
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- Debtor address: {'precision': 0.9807692307692307, 'recall': 0.9807692307692307, 'f1': 0.9807692307692307, 'number': 52}
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- Debtor name: {'precision': 0.926829268292683, 'recall': 0.95, 'f1': 0.9382716049382716, 'number': 40}
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- Doc id: {'precision': 0.6875, 'recall': 0.6875, 'f1': 0.6875, 'number': 16}
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- Title: {'precision': 0.9772727272727273, 'recall': 0.7678571428571429, 'f1': 0.86, 'number': 56}
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- Overall Precision: 0.9217
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- Overall Recall: 0.9056
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- Overall F1: 0.9136
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- Overall Accuracy: 0.9755
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 3e-05
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- train_batch_size: 16
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 50
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Creditor address | Creditor name | Creditor proxy | Debtor address | Debtor name | Doc id | Title | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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|:-------------:|:-------:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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| 1.2524 | 6.6667 | 20 | 0.6528 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 53} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 35} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 52} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 40} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 56} | 0.0 | 0.0 | 0.0 | 0.8405 |
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| 0.4371 | 13.3333 | 40 | 0.2820 | {'precision': 0.7457627118644068, 'recall': 0.8301886792452831, 'f1': 0.7857142857142858, 'number': 53} | {'precision': 0.868421052631579, 'recall': 0.9428571428571428, 'f1': 0.904109589041096, 'number': 35} | {'precision': 0.9166666666666666, 'recall': 0.3235294117647059, 'f1': 0.4782608695652174, 'number': 34} | {'precision': 0.6222222222222222, 'recall': 0.5384615384615384, 'f1': 0.577319587628866, 'number': 52} | {'precision': 0.9375, 'recall': 0.375, 'f1': 0.5357142857142857, 'number': 40} | {'precision': 0.8, 'recall': 0.5, 'f1': 0.6153846153846154, 'number': 16} | {'precision': 0.8235294117647058, 'recall': 0.75, 'f1': 0.7850467289719627, 'number': 56} | 0.7835 | 0.6329 | 0.7002 | 0.9320 |
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| 0.1154 | 20.0 | 60 | 0.1217 | {'precision': 1.0, 'recall': 0.9433962264150944, 'f1': 0.970873786407767, 'number': 53} | {'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 35} | {'precision': 0.7666666666666667, 'recall': 0.6764705882352942, 'f1': 0.71875, 'number': 34} | {'precision': 0.8947368421052632, 'recall': 0.9807692307692307, 'f1': 0.9357798165137614, 'number': 52} | {'precision': 0.9142857142857143, 'recall': 0.8, 'f1': 0.8533333333333333, 'number': 40} | {'precision': 0.6875, 'recall': 0.6875, 'f1': 0.6875, 'number': 16} | {'precision': 0.9565217391304348, 'recall': 0.7857142857142857, 'f1': 0.8627450980392156, 'number': 56} | 0.9111 | 0.8601 | 0.8849 | 0.9682 |
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| 0.0263 | 26.6667 | 80 | 0.1306 | {'precision': 0.9803921568627451, 'recall': 0.9433962264150944, 'f1': 0.9615384615384616, 'number': 53} | {'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 35} | {'precision': 0.7307692307692307, 'recall': 0.5588235294117647, 'f1': 0.6333333333333334, 'number': 34} | {'precision': 0.9807692307692307, 'recall': 0.9807692307692307, 'f1': 0.9807692307692307, 'number': 52} | {'precision': 0.926829268292683, 'recall': 0.95, 'f1': 0.9382716049382716, 'number': 40} | {'precision': 0.6875, 'recall': 0.6875, 'f1': 0.6875, 'number': 16} | {'precision': 1.0, 'recall': 0.7857142857142857, 'f1': 0.88, 'number': 56} | 0.9323 | 0.8671 | 0.8986 | 0.9704 |
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| 0.0113 | 33.3333 | 100 | 0.1161 | {'precision': 0.9803921568627451, 'recall': 0.9433962264150944, 'f1': 0.9615384615384616, 'number': 53} | {'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 35} | {'precision': 0.75, 'recall': 0.8823529411764706, 'f1': 0.8108108108108107, 'number': 34} | {'precision': 1.0, 'recall': 0.9807692307692307, 'f1': 0.9902912621359222, 'number': 52} | {'precision': 0.9285714285714286, 'recall': 0.975, 'f1': 0.951219512195122, 'number': 40} | {'precision': 0.6875, 'recall': 0.6875, 'f1': 0.6875, 'number': 16} | {'precision': 1.0, 'recall': 0.75, 'f1': 0.8571428571428571, 'number': 56} | 0.9281 | 0.9021 | 0.9149 | 0.9755 |
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| 0.0079 | 40.0 | 120 | 0.1306 | {'precision': 0.9803921568627451, 'recall': 0.9433962264150944, 'f1': 0.9615384615384616, 'number': 53} | {'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 35} | {'precision': 0.7272727272727273, 'recall': 0.7058823529411765, 'f1': 0.7164179104477613, 'number': 34} | {'precision': 1.0, 'recall': 0.9807692307692307, 'f1': 0.9902912621359222, 'number': 52} | {'precision': 0.926829268292683, 'recall': 0.95, 'f1': 0.9382716049382716, 'number': 40} | {'precision': 0.6875, 'recall': 0.6875, 'f1': 0.6875, 'number': 16} | {'precision': 1.0, 'recall': 0.7678571428571429, 'f1': 0.8686868686868687, 'number': 56} | 0.9299 | 0.8811 | 0.9048 | 0.9727 |
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| 0.0064 | 46.6667 | 140 | 0.1191 | {'precision': 0.9807692307692307, 'recall': 0.9622641509433962, 'f1': 0.9714285714285713, 'number': 53} | {'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 35} | {'precision': 0.75, 'recall': 0.8823529411764706, 'f1': 0.8108108108108107, 'number': 34} | {'precision': 0.9807692307692307, 'recall': 0.9807692307692307, 'f1': 0.9807692307692307, 'number': 52} | {'precision': 0.926829268292683, 'recall': 0.95, 'f1': 0.9382716049382716, 'number': 40} | {'precision': 0.6875, 'recall': 0.6875, 'f1': 0.6875, 'number': 16} | {'precision': 0.9772727272727273, 'recall': 0.7678571428571429, 'f1': 0.86, 'number': 56} | 0.9217 | 0.9056 | 0.9136 | 0.9755 |
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### Framework versions
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- Transformers 4.40.1
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- Pytorch 2.3.0+cu118
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- Datasets 2.19.0
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- Tokenizers 0.19.1
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