---
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: 0.9583333333333334
    - name: Recall
      type: recall
      value: 0.9663865546218487
    - name: F1
      type: f1
      value: 0.9623430962343097
    - name: Accuracy
      type: accuracy
      value: 0.9921787709497206
---

<!-- 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: 0.1046
- Precision: 0.9583
- Recall: 0.9664
- F1: 0.9623
- Accuracy: 0.9922

## 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        | 1.3514  | 100  | 0.5502          | 0.7261    | 0.7017 | 0.7137 | 0.9006   |
| No log        | 2.7027  | 200  | 0.1859          | 0.9034    | 0.9034 | 0.9034 | 0.9721   |
| No log        | 4.0541  | 300  | 0.1437          | 0.9333    | 0.9412 | 0.9372 | 0.9844   |
| No log        | 5.4054  | 400  | 0.1351          | 0.9256    | 0.9412 | 0.9333 | 0.9844   |
| 0.332         | 6.7568  | 500  | 0.1183          | 0.9380    | 0.9538 | 0.9458 | 0.9888   |
| 0.332         | 8.1081  | 600  | 0.1137          | 0.9502    | 0.9622 | 0.9562 | 0.9911   |
| 0.332         | 9.4595  | 700  | 0.1188          | 0.9502    | 0.9622 | 0.9562 | 0.9899   |
| 0.332         | 10.8108 | 800  | 0.1107          | 0.9583    | 0.9664 | 0.9623 | 0.9922   |
| 0.332         | 12.1622 | 900  | 0.1081          | 0.9583    | 0.9664 | 0.9623 | 0.9922   |
| 0.0141        | 13.5135 | 1000 | 0.1046          | 0.9583    | 0.9664 | 0.9623 | 0.9922   |


### Framework versions

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