---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- data_loader
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: models
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: data_loader
      type: data_loader
      config: default
      split: test
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.8940149625935162
    - name: Recall
      type: recall
      value: 0.9168797953964194
    - name: F1
      type: f1
      value: 0.9053030303030304
    - name: Accuracy
      type: accuracy
      value: 0.9743718592964824
---

<!-- 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. -->

# models

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the data_loader dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1595
- Precision: 0.8940
- Recall: 0.9169
- F1: 0.9053
- Accuracy: 0.9744

## 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        | 2.5   | 100  | 0.1926          | 0.7730    | 0.8274 | 0.7993 | 0.9452   |
| No log        | 5.0   | 200  | 0.1342          | 0.8285    | 0.8708 | 0.8491 | 0.9583   |
| No log        | 7.5   | 300  | 0.1217          | 0.8758    | 0.9015 | 0.8885 | 0.9693   |
| No log        | 10.0  | 400  | 0.1157          | 0.9082    | 0.9233 | 0.9157 | 0.9769   |
| 0.15          | 12.5  | 500  | 0.1310          | 0.9011    | 0.9092 | 0.9052 | 0.9744   |
| 0.15          | 15.0  | 600  | 0.1583          | 0.8682    | 0.9015 | 0.8846 | 0.9693   |
| 0.15          | 17.5  | 700  | 0.1628          | 0.8867    | 0.9105 | 0.8984 | 0.9724   |
| 0.15          | 20.0  | 800  | 0.1594          | 0.8945    | 0.9220 | 0.9081 | 0.9749   |
| 0.15          | 22.5  | 900  | 0.1579          | 0.8940    | 0.9169 | 0.9053 | 0.9744   |
| 0.0047        | 25.0  | 1000 | 0.1595          | 0.8940    | 0.9169 | 0.9053 | 0.9744   |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2