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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice-2
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. -->
# layoutlmv3-finetuned-invoice-2
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1396
- Precision: 0.7576
- Recall: 0.8929
- F1: 0.8197
- Accuracy: 0.9742
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 4.35 | 100 | 0.4241 | 0.0 | 0.0 | 0.0 | 0.9135 |
| No log | 8.7 | 200 | 0.2990 | 0.2353 | 0.1429 | 0.1778 | 0.9239 |
| No log | 13.04 | 300 | 0.3107 | 0.5263 | 0.3571 | 0.4255 | 0.9458 |
| No log | 17.39 | 400 | 0.1345 | 0.6970 | 0.8214 | 0.7541 | 0.9742 |
| 0.2872 | 21.74 | 500 | 0.1396 | 0.7576 | 0.8929 | 0.8197 | 0.9742 |
| 0.2872 | 26.09 | 600 | 0.1673 | 0.8519 | 0.8214 | 0.8364 | 0.9690 |
| 0.2872 | 30.43 | 700 | 0.1784 | 0.8519 | 0.8214 | 0.8364 | 0.9690 |
| 0.2872 | 34.78 | 800 | 0.1401 | 0.7742 | 0.8571 | 0.8136 | 0.9729 |
| 0.2872 | 39.13 | 900 | 0.1480 | 0.7273 | 0.8571 | 0.7869 | 0.9716 |
| 0.0443 | 43.48 | 1000 | 0.1739 | 0.6970 | 0.8214 | 0.7541 | 0.9703 |
| 0.0443 | 47.83 | 1100 | 0.1786 | 0.7097 | 0.7857 | 0.7458 | 0.9690 |
| 0.0443 | 52.17 | 1200 | 0.1832 | 0.6970 | 0.8214 | 0.7541 | 0.9690 |
| 0.0443 | 56.52 | 1300 | 0.1861 | 0.6389 | 0.8214 | 0.7187 | 0.9690 |
| 0.0443 | 60.87 | 1400 | 0.2155 | 0.6667 | 0.7143 | 0.6897 | 0.9639 |
| 0.0198 | 65.22 | 1500 | 0.2087 | 0.6667 | 0.7143 | 0.6897 | 0.9652 |
| 0.0198 | 69.57 | 1600 | 0.1680 | 0.6970 | 0.8214 | 0.7541 | 0.9703 |
| 0.0198 | 73.91 | 1700 | 0.1664 | 0.6970 | 0.8214 | 0.7541 | 0.9703 |
| 0.0198 | 78.26 | 1800 | 0.1795 | 0.6970 | 0.8214 | 0.7541 | 0.9703 |
| 0.0198 | 82.61 | 1900 | 0.1807 | 0.6970 | 0.8214 | 0.7541 | 0.9703 |
| 0.0151 | 86.96 | 2000 | 0.1825 | 0.6970 | 0.8214 | 0.7541 | 0.9703 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3
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