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---
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-cordv2
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-cordv2
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: 0.1800
- Precision: 0.9519
- Recall: 0.9568
- F1: 0.9544
- Accuracy: 0.9656
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.5625 | 250 | 0.7355 | 0.7503 | 0.7595 | 0.7549 | 0.8216 |
| 1.0406 | 3.125 | 500 | 0.4019 | 0.8576 | 0.8787 | 0.8680 | 0.9006 |
| 1.0406 | 4.6875 | 750 | 0.2671 | 0.9028 | 0.9260 | 0.9143 | 0.9384 |
| 0.2814 | 6.25 | 1000 | 0.2293 | 0.9380 | 0.9332 | 0.9356 | 0.9473 |
| 0.2814 | 7.8125 | 1250 | 0.1763 | 0.9426 | 0.9445 | 0.9435 | 0.9622 |
| 0.1349 | 9.375 | 1500 | 0.1926 | 0.9437 | 0.9476 | 0.9456 | 0.9613 |
| 0.1349 | 10.9375 | 1750 | 0.1848 | 0.9481 | 0.9579 | 0.9530 | 0.9647 |
| 0.082 | 12.5 | 2000 | 0.2028 | 0.9490 | 0.9558 | 0.9524 | 0.9626 |
| 0.082 | 14.0625 | 2250 | 0.1878 | 0.9510 | 0.9579 | 0.9544 | 0.9652 |
| 0.0584 | 15.625 | 2500 | 0.1800 | 0.9519 | 0.9568 | 0.9544 | 0.9656 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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