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---
library_name: transformers
base_model: layoutlmv3
tags:
- generated_from_trainer
datasets:
- mp-02/cord-sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord-sroie
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: mp-02/cord-sroie
type: mp-02/cord-sroie
metrics:
- name: Precision
type: precision
value: 0.9539473684210527
- name: Recall
type: recall
value: 0.9618573797678275
- name: F1
type: f1
value: 0.9578860445912468
- name: Accuracy
type: accuracy
value: 0.9852276288106003
---
<!-- 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-sroie
This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/cord-sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0744
- Precision: 0.9539
- Recall: 0.9619
- F1: 0.9579
- Accuracy: 0.9852
## 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: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.7483 | 250 | 0.2724 | 0.7860 | 0.7768 | 0.7814 | 0.9414 |
| 0.7002 | 3.4965 | 500 | 0.1376 | 0.9001 | 0.9325 | 0.9160 | 0.9696 |
| 0.7002 | 5.2448 | 750 | 0.0983 | 0.9281 | 0.9417 | 0.9349 | 0.9781 |
| 0.176 | 6.9930 | 1000 | 0.0806 | 0.9411 | 0.9429 | 0.9420 | 0.9817 |
| 0.176 | 8.7413 | 1250 | 0.0779 | 0.9482 | 0.9462 | 0.9472 | 0.9824 |
| 0.0951 | 10.4895 | 1500 | 0.0740 | 0.9493 | 0.9581 | 0.9537 | 0.9844 |
| 0.0951 | 12.2378 | 1750 | 0.0744 | 0.9515 | 0.9614 | 0.9564 | 0.9848 |
| 0.0631 | 13.9860 | 2000 | 0.0740 | 0.9512 | 0.9607 | 0.9559 | 0.9846 |
| 0.0631 | 15.7343 | 2250 | 0.0756 | 0.9522 | 0.9588 | 0.9555 | 0.9846 |
| 0.0496 | 17.4825 | 2500 | 0.0744 | 0.9539 | 0.9619 | 0.9579 | 0.9852 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
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