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

# test

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: 0.5892
- Precision: 0.9073
- Recall: 0.9240
- F1: 0.9156
- Accuracy: 0.8690

## 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  | 0.5784          | 0.7726    | 0.8455 | 0.8074 | 0.8067   |
| No log        | 2.67  | 200  | 0.4754          | 0.8436    | 0.8818 | 0.8623 | 0.8483   |
| No log        | 4.0   | 300  | 0.4297          | 0.8572    | 0.9036 | 0.8798 | 0.8574   |
| No log        | 5.33  | 400  | 0.5095          | 0.8676    | 0.9016 | 0.8843 | 0.8370   |
| 0.526         | 6.67  | 500  | 0.5301          | 0.8785    | 0.9051 | 0.8916 | 0.8533   |
| 0.526         | 8.0   | 600  | 0.5517          | 0.8849    | 0.8937 | 0.8893 | 0.8431   |
| 0.526         | 9.33  | 700  | 0.6011          | 0.8853    | 0.9086 | 0.8968 | 0.8556   |
| 0.526         | 10.67 | 800  | 0.5435          | 0.8989    | 0.9230 | 0.9108 | 0.8682   |
| 0.526         | 12.0  | 900  | 0.5980          | 0.9078    | 0.9240 | 0.9158 | 0.8676   |
| 0.1221        | 13.33 | 1000 | 0.5892          | 0.9073    | 0.9240 | 0.9156 | 0.8690   |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2