File size: 16,593 Bytes
2656407 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | ---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- blumatix_dataset
model-index:
- name: layoutlm-blumatix
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-blumatix
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the blumatix_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3906
- At Table Summary: {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 8}
- Aymentinformation: {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13}
- Eader: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
- Ineitemtable: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10}
- Nvoicedetails: {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20}
- Ogo: {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10}
- Ontact: {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16}
- Ooter: {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10}
- Overall Precision: 0.82
- Overall Recall: 0.8454
- Overall F1: 0.8325
- Overall Accuracy: 0.8704
## 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 | At Table Summary | Aymentinformation | Eader | Ineitemtable | Nvoicedetails | Ogo | Ontact | Ooter | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.88 | 1.0 | 7 | 1.5813 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.42857142857142855, 'recall': 0.23076923076923078, 'f1': 0.3, 'number': 13} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.13333333333333333, 'recall': 0.2, 'f1': 0.16, 'number': 20} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.23076923076923078, 'recall': 0.375, 'f1': 0.2857142857142857, 'number': 16} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | 0.2063 | 0.1340 | 0.1625 | 0.4259 |
| 1.4414 | 2.0 | 14 | 1.1408 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.4, 'recall': 0.46153846153846156, 'f1': 0.42857142857142855, 'number': 13} | {'precision': 1.0, 'recall': 0.3, 'f1': 0.4615384615384615, 'number': 10} | {'precision': 1.0, 'recall': 0.4, 'f1': 0.5714285714285715, 'number': 10} | {'precision': 0.52, 'recall': 0.65, 'f1': 0.5777777777777778, 'number': 20} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 10} | {'precision': 0.4, 'recall': 0.625, 'f1': 0.48780487804878053, 'number': 16} | {'precision': 0.625, 'recall': 0.5, 'f1': 0.5555555555555556, 'number': 10} | 0.5125 | 0.4227 | 0.4633 | 0.5833 |
| 1.144 | 3.0 | 21 | 0.8586 | {'precision': 1.0, 'recall': 0.625, 'f1': 0.7692307692307693, 'number': 8} | {'precision': 0.5714285714285714, 'recall': 0.6153846153846154, 'f1': 0.5925925925925927, 'number': 13} | {'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10} | {'precision': 1.0, 'recall': 0.7, 'f1': 0.8235294117647058, 'number': 10} | {'precision': 0.7368421052631579, 'recall': 0.7, 'f1': 0.717948717948718, 'number': 20} | {'precision': 0.75, 'recall': 0.3, 'f1': 0.4285714285714285, 'number': 10} | {'precision': 0.5454545454545454, 'recall': 0.75, 'f1': 0.631578947368421, 'number': 16} | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10} | 0.7222 | 0.6701 | 0.6952 | 0.7685 |
| 0.8948 | 4.0 | 28 | 0.6937 | {'precision': 0.8333333333333334, 'recall': 0.625, 'f1': 0.7142857142857143, 'number': 8} | {'precision': 0.6923076923076923, 'recall': 0.6923076923076923, 'f1': 0.6923076923076923, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 0.6, 'f1': 0.7499999999999999, 'number': 10} | {'precision': 0.7894736842105263, 'recall': 0.75, 'f1': 0.7692307692307692, 'number': 20} | {'precision': 0.5, 'recall': 0.3, 'f1': 0.37499999999999994, 'number': 10} | {'precision': 0.55, 'recall': 0.6875, 'f1': 0.6111111111111112, 'number': 16} | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | 0.7253 | 0.6804 | 0.7021 | 0.7870 |
| 0.7146 | 5.0 | 35 | 0.5632 | {'precision': 0.7777777777777778, 'recall': 0.875, 'f1': 0.823529411764706, 'number': 8} | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10} | {'precision': 0.8947368421052632, 'recall': 0.85, 'f1': 0.8717948717948718, 'number': 20} | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10} | {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16} | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10} | 0.8229 | 0.8144 | 0.8187 | 0.8611 |
| 0.6475 | 6.0 | 42 | 0.5030 | {'precision': 0.6666666666666666, 'recall': 0.75, 'f1': 0.7058823529411765, 'number': 8} | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 0.7, 'f1': 0.8235294117647058, 'number': 10} | {'precision': 0.8421052631578947, 'recall': 0.8, 'f1': 0.8205128205128205, 'number': 20} | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10} | {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16} | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10} | 0.7979 | 0.7732 | 0.7853 | 0.8426 |
| 0.5697 | 7.0 | 49 | 0.4463 | {'precision': 0.7777777777777778, 'recall': 0.875, 'f1': 0.823529411764706, 'number': 8} | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.8888888888888888, 'recall': 0.8, 'f1': 0.8421052631578948, 'number': 10} | {'precision': 0.8947368421052632, 'recall': 0.85, 'f1': 0.8717948717948718, 'number': 20} | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10} | {'precision': 0.7647058823529411, 'recall': 0.8125, 'f1': 0.787878787878788, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.8211 | 0.8041 | 0.8125 | 0.8611 |
| 0.4919 | 8.0 | 56 | 0.4412 | {'precision': 0.6666666666666666, 'recall': 0.75, 'f1': 0.7058823529411765, 'number': 8} | {'precision': 0.6923076923076923, 'recall': 0.6923076923076923, 'f1': 0.6923076923076923, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10} | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20} | {'precision': 0.7, 'recall': 0.7, 'f1': 0.7, 'number': 10} | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.8061 | 0.8144 | 0.8103 | 0.8426 |
| 0.4344 | 9.0 | 63 | 0.4189 | {'precision': 0.7, 'recall': 0.875, 'f1': 0.7777777777777777, 'number': 8} | {'precision': 0.8181818181818182, 'recall': 0.6923076923076923, 'f1': 0.7500000000000001, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 0.9, 'f1': 0.9473684210526316, 'number': 10} | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20} | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.8163 | 0.8247 | 0.8205 | 0.8704 |
| 0.4855 | 10.0 | 70 | 0.4099 | {'precision': 0.7272727272727273, 'recall': 1.0, 'f1': 0.8421052631578948, 'number': 8} | {'precision': 0.7272727272727273, 'recall': 0.6153846153846154, 'f1': 0.6666666666666667, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20} | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.7222222222222222, 'recall': 0.8125, 'f1': 0.7647058823529411, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.8182 | 0.8351 | 0.8265 | 0.8704 |
| 0.482 | 11.0 | 77 | 0.3974 | {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 8} | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20} | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.82 | 0.8454 | 0.8325 | 0.8704 |
| 0.3704 | 12.0 | 84 | 0.3928 | {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 8} | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20} | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.7222222222222222, 'recall': 0.8125, 'f1': 0.7647058823529411, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.8283 | 0.8454 | 0.8367 | 0.8796 |
| 0.3888 | 13.0 | 91 | 0.3838 | {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 8} | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20} | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.7222222222222222, 'recall': 0.8125, 'f1': 0.7647058823529411, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.8283 | 0.8454 | 0.8367 | 0.8796 |
| 0.3754 | 14.0 | 98 | 0.3889 | {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 8} | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20} | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.82 | 0.8454 | 0.8325 | 0.8704 |
| 0.3666 | 15.0 | 105 | 0.3906 | {'precision': 0.8, 'recall': 1.0, 'f1': 0.888888888888889, 'number': 8} | {'precision': 0.75, 'recall': 0.6923076923076923, 'f1': 0.7199999999999999, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 10} | {'precision': 0.9473684210526315, 'recall': 0.9, 'f1': 0.9230769230769231, 'number': 20} | {'precision': 0.6363636363636364, 'recall': 0.7, 'f1': 0.6666666666666666, 'number': 10} | {'precision': 0.6842105263157895, 'recall': 0.8125, 'f1': 0.742857142857143, 'number': 16} | {'precision': 0.7777777777777778, 'recall': 0.7, 'f1': 0.7368421052631577, 'number': 10} | 0.82 | 0.8454 | 0.8325 | 0.8704 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|