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---
library_name: transformers
base_model: layoutlmv3
tags:
- generated_from_trainer
datasets:
- mp-02/sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-base-sroie
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: mp-02/sroie
type: mp-02/sroie
metrics:
- name: Precision
type: precision
value: 0.9236398345529748
- name: Recall
type: recall
value: 0.9625331564986738
- name: F1
type: f1
value: 0.9426855008930022
- name: Accuracy
type: accuracy
value: 0.9821007282798235
---
<!-- 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-sroie
This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0639
- Precision: 0.9236
- Recall: 0.9625
- F1: 0.9427
- Accuracy: 0.9821
## 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-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.5 | 100 | 0.1464 | 0.9081 | 0.8488 | 0.8775 | 0.9645 |
| No log | 5.0 | 200 | 0.0821 | 0.9322 | 0.9294 | 0.9308 | 0.9791 |
| No log | 7.5 | 300 | 0.0746 | 0.9204 | 0.9469 | 0.9335 | 0.9796 |
| No log | 10.0 | 400 | 0.0685 | 0.9213 | 0.9506 | 0.9357 | 0.9802 |
| 0.1644 | 12.5 | 500 | 0.0657 | 0.9192 | 0.9586 | 0.9385 | 0.9809 |
| 0.1644 | 15.0 | 600 | 0.0678 | 0.9071 | 0.9649 | 0.9351 | 0.9796 |
| 0.1644 | 17.5 | 700 | 0.0636 | 0.9242 | 0.9625 | 0.9430 | 0.9822 |
| 0.1644 | 20.0 | 800 | 0.0643 | 0.9238 | 0.9609 | 0.9420 | 0.9819 |
| 0.1644 | 22.5 | 900 | 0.0620 | 0.9254 | 0.9629 | 0.9438 | 0.9824 |
| 0.0331 | 25.0 | 1000 | 0.0639 | 0.9236 | 0.9625 | 0.9427 | 0.9821 |
| 0.0331 | 27.5 | 1100 | 0.0632 | 0.9249 | 0.9639 | 0.9440 | 0.9825 |
| 0.0331 | 30.0 | 1200 | 0.0619 | 0.9268 | 0.9615 | 0.9439 | 0.9825 |
| 0.0331 | 32.5 | 1300 | 0.0640 | 0.9216 | 0.9665 | 0.9435 | 0.9823 |
| 0.0331 | 35.0 | 1400 | 0.0653 | 0.9201 | 0.9665 | 0.9428 | 0.9820 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
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