---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- format_dataset
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: reciept-model-2500
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: format_dataset
      type: format_dataset
      config: assesment dataset
      split: test
      args: assesment dataset
    metrics:
    - name: Precision
      type: precision
      value: 0.9673366834170855
    - name: Recall
      type: recall
      value: 0.9625
    - name: F1
      type: f1
      value: 0.9649122807017544
    - name: Accuracy
      type: accuracy
      value: 0.9993105033325672
---

<!-- 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. -->

# reciept-model-2500

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the format_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0043
- Precision: 0.9673
- Recall: 0.9625
- F1: 0.9649
- Accuracy: 0.9993

## 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: 2500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.62  | 100  | 0.0150          | 0.8575    | 0.8725 | 0.8649 | 0.9972   |
| No log        | 1.25  | 200  | 0.0075          | 0.8756    | 0.9325 | 0.9031 | 0.9979   |
| No log        | 1.88  | 300  | 0.0154          | 0.8744    | 0.8875 | 0.8809 | 0.9973   |
| No log        | 2.5   | 400  | 0.0118          | 0.8881    | 0.9525 | 0.9192 | 0.9982   |
| 0.0029        | 3.12  | 500  | 0.0091          | 0.9158    | 0.925  | 0.9204 | 0.9983   |
| 0.0029        | 3.75  | 600  | 0.0167          | 0.8720    | 0.9025 | 0.8870 | 0.9975   |
| 0.0029        | 4.38  | 700  | 0.0092          | 0.9183    | 0.9275 | 0.9229 | 0.9983   |
| 0.0029        | 5.0   | 800  | 0.0113          | 0.8843    | 0.9175 | 0.9006 | 0.9979   |
| 0.0029        | 5.62  | 900  | 0.0106          | 0.9349    | 0.8975 | 0.9158 | 0.9982   |
| 0.0017        | 6.25  | 1000 | 0.0043          | 0.9673    | 0.9625 | 0.9649 | 0.9993   |
| 0.0017        | 6.88  | 1100 | 0.0044          | 0.9602    | 0.965  | 0.9626 | 0.9993   |
| 0.0017        | 7.5   | 1200 | 0.0118          | 0.9246    | 0.92   | 0.9223 | 0.9982   |
| 0.0017        | 8.12  | 1300 | 0.0067          | 0.9406    | 0.95   | 0.9453 | 0.9988   |
| 0.0017        | 8.75  | 1400 | 0.0083          | 0.9409    | 0.955  | 0.9479 | 0.9989   |
| 0.001         | 9.38  | 1500 | 0.0060          | 0.9495    | 0.94   | 0.9447 | 0.9988   |
| 0.001         | 10.0  | 1600 | 0.0078          | 0.9369    | 0.9275 | 0.9322 | 0.9985   |
| 0.001         | 10.62 | 1700 | 0.0093          | 0.9248    | 0.9525 | 0.9384 | 0.9986   |
| 0.001         | 11.25 | 1800 | 0.0097          | 0.9062    | 0.9425 | 0.9240 | 0.9983   |
| 0.001         | 11.88 | 1900 | 0.0100          | 0.9098    | 0.9325 | 0.9210 | 0.9982   |
| 0.0006        | 12.5  | 2000 | 0.0111          | 0.9113    | 0.925  | 0.9181 | 0.9981   |
| 0.0006        | 13.12 | 2100 | 0.0107          | 0.9275    | 0.9275 | 0.9275 | 0.9983   |
| 0.0006        | 13.75 | 2200 | 0.0105          | 0.9279    | 0.9325 | 0.9302 | 0.9984   |
| 0.0006        | 14.38 | 2300 | 0.0109          | 0.9325    | 0.9325 | 0.9325 | 0.9985   |
| 0.0006        | 15.0  | 2400 | 0.0109          | 0.9325    | 0.9325 | 0.9325 | 0.9985   |
| 0.0003        | 15.62 | 2500 | 0.0109          | 0.9325    | 0.9325 | 0.9325 | 0.9985   |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1