---
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: 0.8860759493670886
    - name: Recall
      type: recall
      value: 0.9210526315789473
    - name: F1
      type: f1
      value: 0.9032258064516129
    - name: Accuracy
      type: accuracy
      value: 0.94375
---

<!-- 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: 0.3028
- Precision: 0.8861
- Recall: 0.9211
- F1: 0.9032
- Accuracy: 0.9437

## 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        | 4.3478  | 100  | 0.6867          | 0.6842    | 0.6842 | 0.6842 | 0.8063   |
| No log        | 8.6957  | 200  | 0.2381          | 0.8625    | 0.9079 | 0.8846 | 0.9313   |
| No log        | 13.0435 | 300  | 0.2598          | 0.8846    | 0.9079 | 0.8961 | 0.9313   |
| No log        | 17.3913 | 400  | 0.2165          | 0.8625    | 0.9079 | 0.8846 | 0.9375   |
| 0.3281        | 21.7391 | 500  | 0.2037          | 0.8625    | 0.9079 | 0.8846 | 0.9375   |
| 0.3281        | 26.0870 | 600  | 0.2571          | 0.8861    | 0.9211 | 0.9032 | 0.9437   |
| 0.3281        | 30.4348 | 700  | 0.2735          | 0.8861    | 0.9211 | 0.9032 | 0.9437   |
| 0.3281        | 34.7826 | 800  | 0.2993          | 0.8861    | 0.9211 | 0.9032 | 0.9437   |
| 0.3281        | 39.1304 | 900  | 0.3044          | 0.8861    | 0.9211 | 0.9032 | 0.9437   |
| 0.012         | 43.4783 | 1000 | 0.3028          | 0.8861    | 0.9211 | 0.9032 | 0.9437   |


### Framework versions

- Transformers 4.47.0.dev0
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3