---
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: 0.6850
- Precision: 0.8936
- Recall: 0.9136
- F1: 0.9035
- Accuracy: 0.8445

## 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  | 0.6682          | 0.7574    | 0.8311 | 0.7925 | 0.7658   |
| No log        | 2.67  | 200  | 0.4970          | 0.8289    | 0.8833 | 0.8552 | 0.8339   |
| No log        | 4.0   | 300  | 0.5550          | 0.8398    | 0.8882 | 0.8634 | 0.8128   |
| No log        | 5.33  | 400  | 0.5001          | 0.8800    | 0.9106 | 0.8950 | 0.8500   |
| 0.5341        | 6.67  | 500  | 0.5645          | 0.8947    | 0.9036 | 0.8992 | 0.8456   |
| 0.5341        | 8.0   | 600  | 0.5797          | 0.8847    | 0.9190 | 0.9016 | 0.8537   |
| 0.5341        | 9.33  | 700  | 0.6635          | 0.8816    | 0.9101 | 0.8956 | 0.8421   |
| 0.5341        | 10.67 | 800  | 0.6857          | 0.8939    | 0.9126 | 0.9031 | 0.8452   |
| 0.5341        | 12.0  | 900  | 0.6777          | 0.8941    | 0.9146 | 0.9042 | 0.8438   |
| 0.1335        | 13.33 | 1000 | 0.6850          | 0.8936    | 0.9136 | 0.9035 | 0.8445   |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0