---
library_name: transformers
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-finetuned-invoice
  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

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: 0.8510
- Precision: 0.9058
- Recall: 0.9175
- F1: 0.9116
- Accuracy: 0.8556

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 2000

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.3333  | 100  | 0.6411          | 0.7779    | 0.8316 | 0.8038 | 0.7951   |
| No log        | 2.6667  | 200  | 0.5235          | 0.8209    | 0.8629 | 0.8414 | 0.8238   |
| No log        | 4.0     | 300  | 0.5130          | 0.8738    | 0.9081 | 0.8906 | 0.8504   |
| No log        | 5.3333  | 400  | 0.5742          | 0.8848    | 0.9081 | 0.8963 | 0.8431   |
| 0.5233        | 6.6667  | 500  | 0.6276          | 0.8610    | 0.8927 | 0.8766 | 0.8374   |
| 0.5233        | 8.0     | 600  | 0.6887          | 0.8818    | 0.9041 | 0.8928 | 0.8357   |
| 0.5233        | 9.3333  | 700  | 0.6323          | 0.8930    | 0.9165 | 0.9046 | 0.8628   |
| 0.5233        | 10.6667 | 800  | 0.6644          | 0.8878    | 0.9195 | 0.9034 | 0.8538   |
| 0.5233        | 12.0    | 900  | 0.7365          | 0.9138    | 0.9210 | 0.9174 | 0.8580   |
| 0.1181        | 13.3333 | 1000 | 0.7774          | 0.8939    | 0.9210 | 0.9073 | 0.8549   |
| 0.1181        | 14.6667 | 1100 | 0.8265          | 0.9090    | 0.9175 | 0.9132 | 0.8557   |
| 0.1181        | 16.0    | 1200 | 0.8112          | 0.9023    | 0.9265 | 0.9142 | 0.8546   |
| 0.1181        | 17.3333 | 1300 | 0.8212          | 0.9075    | 0.9160 | 0.9117 | 0.8596   |
| 0.1181        | 18.6667 | 1400 | 0.8931          | 0.8999    | 0.9151 | 0.9074 | 0.8509   |
| 0.0443        | 20.0    | 1500 | 0.8510          | 0.9058    | 0.9175 | 0.9116 | 0.8556   |
| 0.0443        | 21.3333 | 1600 | 0.8318          | 0.9016    | 0.9235 | 0.9124 | 0.8612   |
| 0.0443        | 22.6667 | 1700 | 0.8783          | 0.9065    | 0.9146 | 0.9105 | 0.8519   |
| 0.0443        | 24.0    | 1800 | 0.8964          | 0.9023    | 0.9126 | 0.9074 | 0.8527   |
| 0.0443        | 25.3333 | 1900 | 0.8890          | 0.9054    | 0.9175 | 0.9114 | 0.8580   |
| 0.0205        | 26.6667 | 2000 | 0.8992          | 0.9004    | 0.9165 | 0.9084 | 0.8552   |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.20.1