---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-testCUSTOMds20_02
  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. -->

# layoutlmv3-testCUSTOMds20_02

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1298
- Precision: 0.8546
- Recall: 0.8362
- F1: 0.8453
- Accuracy: 0.9807

## 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        | 1.25  | 100  | 0.0624          | 0.8578    | 0.8578 | 0.8578 | 0.9837   |
| No log        | 2.5   | 200  | 0.0858          | 0.8603    | 0.8491 | 0.8547 | 0.9814   |
| No log        | 3.75  | 300  | 0.0826          | 0.9062    | 0.875  | 0.8904 | 0.9859   |
| No log        | 5.0   | 400  | 0.0940          | 0.9018    | 0.8707 | 0.8860 | 0.9851   |
| 0.0658        | 6.25  | 500  | 0.1237          | 0.8502    | 0.8319 | 0.8410 | 0.9807   |
| 0.0658        | 7.5   | 600  | 0.1125          | 0.9045    | 0.8578 | 0.8805 | 0.9844   |
| 0.0658        | 8.75  | 700  | 0.1252          | 0.8448    | 0.8448 | 0.8448 | 0.9799   |
| 0.0658        | 10.0  | 800  | 0.1156          | 0.8678    | 0.8491 | 0.8584 | 0.9829   |
| 0.0658        | 11.25 | 900  | 0.1238          | 0.8559    | 0.8448 | 0.8503 | 0.9822   |
| 0.0036        | 12.5  | 1000 | 0.1298          | 0.8546    | 0.8362 | 0.8453 | 0.9807   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2