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---
base_model: layoutlmv3
tags:
- generated_from_trainer
datasets:
- mp-02/cord
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: mp-02/cord
type: mp-02/cord
metrics:
- name: Precision
type: precision
value: 0.9572519083969465
- name: Recall
type: recall
value: 0.9736024844720497
- name: F1
type: f1
value: 0.9653579676674365
- name: Accuracy
type: accuracy
value: 0.9673174872665535
---
<!-- 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-cord
This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/cord dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1831
- Precision: 0.9573
- Recall: 0.9736
- F1: 0.9654
- Accuracy: 0.9673
## 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: 10
- eval_batch_size: 10
- 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 | 3.125 | 250 | 0.7551 | 0.7974 | 0.8587 | 0.8269 | 0.8544 |
| 1.1001 | 6.25 | 500 | 0.3822 | 0.8846 | 0.9286 | 0.9061 | 0.9215 |
| 1.1001 | 9.375 | 750 | 0.2750 | 0.9334 | 0.9581 | 0.9456 | 0.9444 |
| 0.2309 | 12.5 | 1000 | 0.2072 | 0.9439 | 0.9674 | 0.9555 | 0.9605 |
| 0.2309 | 15.625 | 1250 | 0.1934 | 0.9500 | 0.9728 | 0.9613 | 0.9652 |
| 0.1003 | 18.75 | 1500 | 0.1898 | 0.9602 | 0.9736 | 0.9668 | 0.9665 |
| 0.1003 | 21.875 | 1750 | 0.2032 | 0.9542 | 0.9705 | 0.9623 | 0.9631 |
| 0.0637 | 25.0 | 2000 | 0.1831 | 0.9573 | 0.9736 | 0.9654 | 0.9673 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
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