---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layout3
  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. -->

# layout3

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: 0.6334
- Precision: 0.8935
- Recall: 0.9131
- F1: 0.9032
- Accuracy: 0.8586

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.33  | 100  | 0.6874          | 0.7820    | 0.8073 | 0.7944 | 0.7841   |
| No log        | 2.67  | 200  | 0.4485          | 0.8321    | 0.8838 | 0.8571 | 0.8474   |
| No log        | 4.0   | 300  | 0.4403          | 0.8579    | 0.9086 | 0.8825 | 0.8414   |
| No log        | 5.33  | 400  | 0.4593          | 0.8452    | 0.9056 | 0.8743 | 0.8341   |
| 0.5531        | 6.67  | 500  | 0.4881          | 0.8732    | 0.9170 | 0.8946 | 0.8575   |
| 0.5531        | 8.0   | 600  | 0.5332          | 0.8761    | 0.9101 | 0.8928 | 0.8547   |
| 0.5531        | 9.33  | 700  | 0.5910          | 0.8894    | 0.9106 | 0.8999 | 0.8517   |
| 0.5531        | 10.67 | 800  | 0.5914          | 0.8909    | 0.9131 | 0.9019 | 0.8557   |
| 0.5531        | 12.0  | 900  | 0.6127          | 0.9001    | 0.9180 | 0.9090 | 0.8614   |
| 0.1245        | 13.33 | 1000 | 0.6334          | 0.8935    | 0.9131 | 0.9032 | 0.8586   |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.0+cu118
- Datasets 2.17.1
- Tokenizers 0.13.2