---
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: test
  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: 0.8869778869778869
    - name: Recall
      type: recall
      value: 0.9025
    - name: F1
      type: f1
      value: 0.8946716232961586
    - name: Accuracy
      type: accuracy
      value: 0.9977016777752241
---

<!-- 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 format_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0089
- Precision: 0.8870
- Recall: 0.9025
- F1: 0.8947
- Accuracy: 0.9977

## 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: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.62  | 100  | 0.0405          | 0.0       | 0.0    | 0.0    | 0.9877   |
| No log        | 1.25  | 200  | 0.0170          | 0.7538    | 0.735  | 0.7443 | 0.9949   |
| No log        | 1.88  | 300  | 0.0131          | 0.7261    | 0.875  | 0.7937 | 0.9956   |
| No log        | 2.5   | 400  | 0.0123          | 0.7692    | 0.85   | 0.8076 | 0.9959   |
| 0.0271        | 3.12  | 500  | 0.0105          | 0.8098    | 0.905  | 0.8548 | 0.9968   |
| 0.0271        | 3.75  | 600  | 0.0106          | 0.8460    | 0.8925 | 0.8686 | 0.9972   |
| 0.0271        | 4.38  | 700  | 0.0086          | 0.8504    | 0.895  | 0.8721 | 0.9973   |
| 0.0271        | 5.0   | 800  | 0.0109          | 0.8871    | 0.845  | 0.8656 | 0.9972   |
| 0.0271        | 5.62  | 900  | 0.0085          | 0.8883    | 0.895  | 0.8917 | 0.9977   |
| 0.0042        | 6.25  | 1000 | 0.0089          | 0.8870    | 0.9025 | 0.8947 | 0.9977   |


### Framework versions

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