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---
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: layoutlmv3-finetuned-invoice
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
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: 0.0558
- Precision: 0.9435
- Recall: 0.9612
- F1: 0.9523
- Accuracy: 0.9858
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.3195 | 100 | 0.0961 | 0.9121 | 0.9134 | 0.9128 | 0.9752 |
| No log | 0.6390 | 200 | 0.0772 | 0.9120 | 0.9396 | 0.9256 | 0.9780 |
| No log | 0.9585 | 300 | 0.0707 | 0.9272 | 0.9509 | 0.9389 | 0.9822 |
| No log | 1.2780 | 400 | 0.0638 | 0.9202 | 0.9602 | 0.9398 | 0.9819 |
| 0.1131 | 1.5974 | 500 | 0.0631 | 0.9270 | 0.9582 | 0.9423 | 0.9829 |
| 0.1131 | 1.9169 | 600 | 0.0561 | 0.9331 | 0.9615 | 0.9471 | 0.9843 |
| 0.1131 | 2.2364 | 700 | 0.0651 | 0.9141 | 0.9720 | 0.9421 | 0.9824 |
| 0.1131 | 2.5559 | 800 | 0.0537 | 0.9515 | 0.9556 | 0.9535 | 0.9862 |
| 0.1131 | 2.8754 | 900 | 0.0556 | 0.9467 | 0.9582 | 0.9524 | 0.9860 |
| 0.0379 | 3.1949 | 1000 | 0.0558 | 0.9435 | 0.9612 | 0.9523 | 0.9858 |
### Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
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