---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- ls-generated4
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-invoice-model
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ls-generated4
      type: ls-generated4
      config: invoice
      split: test
      args: invoice
    metrics:
    - name: Precision
      type: precision
      value: 0.9185733512786003
    - name: Recall
      type: recall
      value: 0.9375
    - name: F1
      type: f1
      value: 0.9279401767505099
    - name: Accuracy
      type: accuracy
      value: 0.9536870503597122
---

<!-- 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-invoice-model

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the ls-generated4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3694
- Precision: 0.9186
- Recall: 0.9375
- F1: 0.9279
- Accuracy: 0.9537

## 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: 5e-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: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.85  | 100  | 0.7836          | 0.5238    | 0.5982 | 0.5585 | 0.7680   |
| No log        | 1.69  | 200  | 0.4954          | 0.6888    | 0.7479 | 0.7172 | 0.8422   |
| No log        | 2.54  | 300  | 0.3483          | 0.7807    | 0.8462 | 0.8121 | 0.9040   |
| No log        | 3.39  | 400  | 0.3200          | 0.8113    | 0.8654 | 0.8375 | 0.9125   |
| 0.5923        | 4.24  | 500  | 0.2775          | 0.8593    | 0.8853 | 0.8721 | 0.9319   |
| 0.5923        | 5.08  | 600  | 0.2674          | 0.8700    | 0.9052 | 0.8872 | 0.9377   |
| 0.5923        | 5.93  | 700  | 0.2766          | 0.8739    | 0.9135 | 0.8932 | 0.9386   |
| 0.5923        | 6.78  | 800  | 0.2641          | 0.8879    | 0.9190 | 0.9031 | 0.9472   |
| 0.5923        | 7.63  | 900  | 0.2893          | 0.9094    | 0.9238 | 0.9165 | 0.9447   |
| 0.0802        | 8.47  | 1000 | 0.3369          | 0.9145    | 0.9258 | 0.9201 | 0.9465   |
| 0.0802        | 9.32  | 1100 | 0.3037          | 0.9043    | 0.9341 | 0.9189 | 0.9505   |
| 0.0802        | 10.17 | 1200 | 0.3510          | 0.9032    | 0.9231 | 0.9130 | 0.9472   |
| 0.0802        | 11.02 | 1300 | 0.3224          | 0.9138    | 0.9251 | 0.9195 | 0.9501   |
| 0.0802        | 11.86 | 1400 | 0.3873          | 0.9133    | 0.9265 | 0.9199 | 0.9456   |
| 0.0198        | 12.71 | 1500 | 0.3786          | 0.9120    | 0.9327 | 0.9222 | 0.9492   |
| 0.0198        | 13.56 | 1600 | 0.3807          | 0.9050    | 0.9293 | 0.9170 | 0.9469   |
| 0.0198        | 14.41 | 1700 | 0.3664          | 0.9088    | 0.9313 | 0.9199 | 0.9510   |
| 0.0198        | 15.25 | 1800 | 0.3582          | 0.9152    | 0.9341 | 0.9245 | 0.9521   |
| 0.0198        | 16.1  | 1900 | 0.3736          | 0.9198    | 0.9368 | 0.9282 | 0.9528   |
| 0.007         | 16.95 | 2000 | 0.3694          | 0.9186    | 0.9375 | 0.9279 | 0.9537   |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1