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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-base-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: 0.9105022831050228
- name: Recall
type: recall
value: 0.9447998104714522
- name: F1
type: f1
value: 0.9273340309266364
- name: Accuracy
type: accuracy
value: 0.9738126147097005
---
<!-- 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-base-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: 0.0936
- Precision: 0.9105
- Recall: 0.9448
- F1: 0.9273
- Accuracy: 0.9738
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.7937 | 100 | 0.4396 | 0.6271 | 0.6015 | 0.6140 | 0.9064 |
| No log | 1.5873 | 200 | 0.2500 | 0.8669 | 0.8394 | 0.8529 | 0.9508 |
| No log | 2.3810 | 300 | 0.1517 | 0.8682 | 0.9050 | 0.8862 | 0.9634 |
| No log | 3.1746 | 400 | 0.1346 | 0.8694 | 0.9339 | 0.9005 | 0.9645 |
| 0.6691 | 3.9683 | 500 | 0.0943 | 0.9369 | 0.9325 | 0.9347 | 0.9778 |
| 0.6691 | 4.7619 | 600 | 0.0922 | 0.9049 | 0.9491 | 0.9265 | 0.9742 |
| 0.6691 | 5.5556 | 700 | 0.1106 | 0.8913 | 0.9540 | 0.9216 | 0.9717 |
| 0.6691 | 6.3492 | 800 | 0.0875 | 0.9091 | 0.9552 | 0.9316 | 0.9755 |
| 0.6691 | 7.1429 | 900 | 0.0958 | 0.8977 | 0.9623 | 0.9289 | 0.9743 |
| 0.1055 | 7.9365 | 1000 | 0.0936 | 0.9105 | 0.9448 | 0.9273 | 0.9738 |
| 0.1055 | 8.7302 | 1100 | 0.1035 | 0.9289 | 0.9415 | 0.9352 | 0.9766 |
| 0.1055 | 9.5238 | 1200 | 0.1115 | 0.9081 | 0.9507 | 0.9289 | 0.9739 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|