---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Fine-tuned_LayoutLMv3_on_SROIE
  results: []
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/miss1dua/LayoutLMv3-SROIE/runs/573wn3ba)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/miss1dua/LayoutLMv3-SROIE/runs/fdc141nv)
# Fine-tuned_LayoutLMv3_on_SROIE

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0393
- Precision: 0.9286
- Recall: 0.9460
- F1: 0.9372
- Accuracy: 0.9948

## 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: 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: 2500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.04          | 12.5  | 500  | 0.0308          | 0.9128    | 0.9424 | 0.9273 | 0.9941   |
| 0.0057        | 25.0  | 1000 | 0.0353          | 0.9222    | 0.9474 | 0.9346 | 0.9947   |
| 0.0022        | 37.5  | 1500 | 0.0385          | 0.9218    | 0.9424 | 0.9320 | 0.9943   |
| 0.0013        | 50.0  | 2000 | 0.0377          | 0.9392    | 0.9467 | 0.9429 | 0.9951   |
| 0.0008        | 62.5  | 2500 | 0.0393          | 0.9286    | 0.9460 | 0.9372 | 0.9948   |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1