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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: 0.7572254335260116
- name: Recall
type: recall
value: 0.8136645962732919
- name: F1
type: f1
value: 0.784431137724551
- name: Accuracy
type: accuracy
value: 0.7975382003395586
---
<!-- 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: 1.0695
- Precision: 0.7572
- Recall: 0.8137
- F1: 0.7844
- Accuracy: 0.7975
## 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-06
- 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: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.7217 | 6.25 | 500 | 1.8788 | 0.5168 | 0.6095 | 0.5593 | 0.5883 |
| 1.7562 | 12.5 | 1000 | 1.4443 | 0.5337 | 0.6576 | 0.5892 | 0.6795 |
| 1.4387 | 18.75 | 1500 | 1.2162 | 0.6981 | 0.7811 | 0.7373 | 0.7746 |
| 1.2728 | 25.0 | 2000 | 1.1030 | 0.7473 | 0.8106 | 0.7777 | 0.7941 |
| 1.1902 | 31.25 | 2500 | 1.0695 | 0.7572 | 0.8137 | 0.7844 | 0.7975 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|