File size: 5,337 Bytes
feefffc | 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 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | ---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Layoutlmv3InvoiceCzechV3
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. -->
# Layoutlmv3InvoiceCzechV3
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0874
- Precision: 0.6694
- Recall: 0.6920
- F1: 0.6805
- Accuracy: 0.9804
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 23 | 2.5446 | 0.0 | 0.0 | 0.0 | 0.9472 |
| No log | 2.0 | 46 | 0.8320 | 0.0 | 0.0 | 0.0 | 0.9473 |
| No log | 3.0 | 69 | 0.4185 | 0.0 | 0.0 | 0.0 | 0.9473 |
| No log | 4.0 | 92 | 0.3832 | 0.0 | 0.0 | 0.0 | 0.9473 |
| No log | 5.0 | 115 | 0.2963 | 0.0 | 0.0 | 0.0 | 0.9473 |
| No log | 6.0 | 138 | 0.2591 | 0.0357 | 0.0017 | 0.0032 | 0.9473 |
| No log | 7.0 | 161 | 0.2357 | 0.2468 | 0.1320 | 0.1720 | 0.9510 |
| No log | 8.0 | 184 | 0.2226 | 0.4192 | 0.2589 | 0.3201 | 0.9574 |
| No log | 9.0 | 207 | 0.2062 | 0.5011 | 0.3875 | 0.4370 | 0.9633 |
| No log | 10.0 | 230 | 0.1946 | 0.5164 | 0.4264 | 0.4671 | 0.9651 |
| No log | 11.0 | 253 | 0.1839 | 0.5515 | 0.4349 | 0.4863 | 0.9663 |
| No log | 12.0 | 276 | 0.1724 | 0.5376 | 0.4839 | 0.5093 | 0.9677 |
| No log | 13.0 | 299 | 0.1675 | 0.5824 | 0.5381 | 0.5594 | 0.9699 |
| No log | 14.0 | 322 | 0.1569 | 0.6127 | 0.5567 | 0.5833 | 0.9709 |
| No log | 15.0 | 345 | 0.1298 | 0.6084 | 0.5888 | 0.5985 | 0.9719 |
| No log | 16.0 | 368 | 0.1226 | 0.5652 | 0.5939 | 0.5792 | 0.9729 |
| No log | 17.0 | 391 | 0.1157 | 0.5621 | 0.5973 | 0.5792 | 0.9739 |
| No log | 18.0 | 414 | 0.1148 | 0.5863 | 0.6210 | 0.6031 | 0.9757 |
| No log | 19.0 | 437 | 0.1134 | 0.5974 | 0.6176 | 0.6073 | 0.9760 |
| No log | 20.0 | 460 | 0.1093 | 0.5866 | 0.6244 | 0.6049 | 0.9757 |
| No log | 21.0 | 483 | 0.1030 | 0.5953 | 0.6396 | 0.6166 | 0.9772 |
| 0.4082 | 22.0 | 506 | 0.1027 | 0.6025 | 0.6413 | 0.6213 | 0.9771 |
| 0.4082 | 23.0 | 529 | 0.1017 | 0.6093 | 0.6464 | 0.6273 | 0.9776 |
| 0.4082 | 24.0 | 552 | 0.1049 | 0.6104 | 0.6362 | 0.6230 | 0.9773 |
| 0.4082 | 25.0 | 575 | 0.0970 | 0.5913 | 0.6413 | 0.6153 | 0.9767 |
| 0.4082 | 26.0 | 598 | 0.0922 | 0.6069 | 0.6582 | 0.6315 | 0.9777 |
| 0.4082 | 27.0 | 621 | 0.0937 | 0.6154 | 0.6633 | 0.6384 | 0.9782 |
| 0.4082 | 28.0 | 644 | 0.0934 | 0.6266 | 0.6616 | 0.6436 | 0.9787 |
| 0.4082 | 29.0 | 667 | 0.0921 | 0.6177 | 0.6616 | 0.6389 | 0.9785 |
| 0.4082 | 30.0 | 690 | 0.0904 | 0.6109 | 0.6616 | 0.6353 | 0.9783 |
| 0.4082 | 31.0 | 713 | 0.0922 | 0.6194 | 0.6582 | 0.6382 | 0.9786 |
| 0.4082 | 32.0 | 736 | 0.0896 | 0.6304 | 0.6667 | 0.6480 | 0.9791 |
| 0.4082 | 33.0 | 759 | 0.0903 | 0.6314 | 0.6667 | 0.6486 | 0.9793 |
| 0.4082 | 34.0 | 782 | 0.0879 | 0.6377 | 0.6819 | 0.6590 | 0.9794 |
| 0.4082 | 35.0 | 805 | 0.0863 | 0.6439 | 0.6853 | 0.6639 | 0.9798 |
| 0.4082 | 36.0 | 828 | 0.0860 | 0.6421 | 0.6768 | 0.6590 | 0.9794 |
| 0.4082 | 37.0 | 851 | 0.0874 | 0.6721 | 0.6937 | 0.6828 | 0.9805 |
| 0.4082 | 38.0 | 874 | 0.0861 | 0.6559 | 0.6870 | 0.6711 | 0.9799 |
| 0.4082 | 39.0 | 897 | 0.0867 | 0.6694 | 0.6920 | 0.6805 | 0.9803 |
| 0.4082 | 40.0 | 920 | 0.0860 | 0.6667 | 0.6937 | 0.6799 | 0.9803 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
|