---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice-2
  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-finetuned-invoice-2

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: 0.1396
- Precision: 0.7576
- Recall: 0.8929
- F1: 0.8197
- Accuracy: 0.9742

## 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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 4.35  | 100  | 0.4241          | 0.0       | 0.0    | 0.0    | 0.9135   |
| No log        | 8.7   | 200  | 0.2990          | 0.2353    | 0.1429 | 0.1778 | 0.9239   |
| No log        | 13.04 | 300  | 0.3107          | 0.5263    | 0.3571 | 0.4255 | 0.9458   |
| No log        | 17.39 | 400  | 0.1345          | 0.6970    | 0.8214 | 0.7541 | 0.9742   |
| 0.2872        | 21.74 | 500  | 0.1396          | 0.7576    | 0.8929 | 0.8197 | 0.9742   |
| 0.2872        | 26.09 | 600  | 0.1673          | 0.8519    | 0.8214 | 0.8364 | 0.9690   |
| 0.2872        | 30.43 | 700  | 0.1784          | 0.8519    | 0.8214 | 0.8364 | 0.9690   |
| 0.2872        | 34.78 | 800  | 0.1401          | 0.7742    | 0.8571 | 0.8136 | 0.9729   |
| 0.2872        | 39.13 | 900  | 0.1480          | 0.7273    | 0.8571 | 0.7869 | 0.9716   |
| 0.0443        | 43.48 | 1000 | 0.1739          | 0.6970    | 0.8214 | 0.7541 | 0.9703   |
| 0.0443        | 47.83 | 1100 | 0.1786          | 0.7097    | 0.7857 | 0.7458 | 0.9690   |
| 0.0443        | 52.17 | 1200 | 0.1832          | 0.6970    | 0.8214 | 0.7541 | 0.9690   |
| 0.0443        | 56.52 | 1300 | 0.1861          | 0.6389    | 0.8214 | 0.7187 | 0.9690   |
| 0.0443        | 60.87 | 1400 | 0.2155          | 0.6667    | 0.7143 | 0.6897 | 0.9639   |
| 0.0198        | 65.22 | 1500 | 0.2087          | 0.6667    | 0.7143 | 0.6897 | 0.9652   |
| 0.0198        | 69.57 | 1600 | 0.1680          | 0.6970    | 0.8214 | 0.7541 | 0.9703   |
| 0.0198        | 73.91 | 1700 | 0.1664          | 0.6970    | 0.8214 | 0.7541 | 0.9703   |
| 0.0198        | 78.26 | 1800 | 0.1795          | 0.6970    | 0.8214 | 0.7541 | 0.9703   |
| 0.0198        | 82.61 | 1900 | 0.1807          | 0.6970    | 0.8214 | 0.7541 | 0.9703   |
| 0.0151        | 86.96 | 2000 | 0.1825          | 0.6970    | 0.8214 | 0.7541 | 0.9703   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3