---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: layoutlmv3
      type: layoutlmv3
      config: InvoiceExtraction
      split: test
      args: InvoiceExtraction
    metrics:
    - name: Precision
      type: precision
      value: 0.9735537190082645
    - name: Recall
      type: recall
      value: 0.9751655629139073
    - name: F1
      type: f1
      value: 0.9743589743589743
    - name: Accuracy
      type: accuracy
      value: 0.9924257137308216
---

<!-- 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 the layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0899
- Precision: 0.9736
- Recall: 0.9752
- F1: 0.9744
- Accuracy: 0.9924

## 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: 1000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.5291 | 100  | 0.4956          | 0.7821    | 0.7724 | 0.7772 | 0.9413   |
| No log        | 1.0582 | 200  | 0.1802          | 0.9285    | 0.9247 | 0.9266 | 0.9761   |
| No log        | 1.5873 | 300  | 0.1465          | 0.9334    | 0.9512 | 0.9422 | 0.9841   |
| No log        | 2.1164 | 400  | 0.1309          | 0.9447    | 0.9611 | 0.9528 | 0.9876   |
| 0.3392        | 2.6455 | 500  | 0.1095          | 0.9516    | 0.9594 | 0.9555 | 0.9891   |
| 0.3392        | 3.1746 | 600  | 0.1022          | 0.9573    | 0.9652 | 0.9613 | 0.9915   |
| 0.3392        | 3.7037 | 700  | 0.1081          | 0.9573    | 0.9661 | 0.9617 | 0.9918   |
| 0.3392        | 4.2328 | 800  | 0.0922          | 0.9726    | 0.9694 | 0.9710 | 0.9920   |
| 0.3392        | 4.7619 | 900  | 0.0930          | 0.9702    | 0.9702 | 0.9702 | 0.9916   |
| 0.0282        | 5.2910 | 1000 | 0.0899          | 0.9736    | 0.9752 | 0.9744 | 0.9924   |


### Framework versions

- Transformers 4.47.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3