---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- generated
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: generated
      type: generated
      config: sroie
      split: test
      args: sroie
    metrics:
    - name: Precision
      type: precision
      value: 1.0
    - name: Recall
      type: recall
      value: 1.0
    - name: F1
      type: f1
      value: 1.0
    - name: Accuracy
      type: accuracy
      value: 1.0
---

<!-- 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 the generated dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0014
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0

## 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: 5000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.0   | 100  | 0.0766          | 0.97      | 0.9838 | 0.9768 | 0.9968   |
| No log        | 4.0   | 200  | 0.0214          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 6.0   | 300  | 0.0157          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 8.0   | 400  | 0.0142          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| 0.1264        | 10.0  | 500  | 0.0129          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| 0.1264        | 12.0  | 600  | 0.0118          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| 0.1264        | 14.0  | 700  | 0.0038          | 0.9980    | 0.9959 | 0.9970 | 0.9996   |
| 0.1264        | 16.0  | 800  | 0.0020          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.1264        | 18.0  | 900  | 0.0016          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0064        | 20.0  | 1000 | 0.0014          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0064        | 22.0  | 1100 | 0.0012          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0064        | 24.0  | 1200 | 0.0010          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0064        | 26.0  | 1300 | 0.0009          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0064        | 28.0  | 1400 | 0.0008          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0018        | 30.0  | 1500 | 0.0008          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0018        | 32.0  | 1600 | 0.0007          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0018        | 34.0  | 1700 | 0.0006          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0018        | 36.0  | 1800 | 0.0006          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0018        | 38.0  | 1900 | 0.0005          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0011        | 40.0  | 2000 | 0.0005          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0011        | 42.0  | 2100 | 0.0005          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0011        | 44.0  | 2200 | 0.0004          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0011        | 46.0  | 2300 | 0.0004          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0011        | 48.0  | 2400 | 0.0004          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0008        | 50.0  | 2500 | 0.0004          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0008        | 52.0  | 2600 | 0.0003          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0008        | 54.0  | 2700 | 0.0003          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0008        | 56.0  | 2800 | 0.0003          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0008        | 58.0  | 2900 | 0.0003          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0006        | 60.0  | 3000 | 0.0003          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0006        | 62.0  | 3100 | 0.0003          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0006        | 64.0  | 3200 | 0.0003          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0006        | 66.0  | 3300 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0006        | 68.0  | 3400 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0005        | 70.0  | 3500 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0005        | 72.0  | 3600 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0005        | 74.0  | 3700 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0005        | 76.0  | 3800 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0005        | 78.0  | 3900 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0004        | 80.0  | 4000 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0004        | 82.0  | 4100 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0004        | 84.0  | 4200 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0004        | 86.0  | 4300 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0004        | 88.0  | 4400 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0003        | 90.0  | 4500 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0003        | 92.0  | 4600 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0003        | 94.0  | 4700 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0003        | 96.0  | 4800 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0003        | 98.0  | 4900 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.0003        | 100.0 | 5000 | 0.0002          | 1.0       | 1.0    | 1.0    | 1.0      |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3