---
tags:
- generated_from_trainer
datasets:
- mp-02/funsd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-funsd
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: mp-02/funsd
      type: mp-02/funsd
    metrics:
    - name: Precision
      type: precision
      value: 0.875725338491296
    - name: Recall
      type: recall
      value: 0.9055
    - name: F1
      type: f1
      value: 0.8903638151425762
    - name: Accuracy
      type: accuracy
      value: 0.843706936150666
---

<!-- 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-funsd

This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6187
- Precision: 0.8757
- Recall: 0.9055
- F1: 0.8904
- Accuracy: 0.8437

## 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: 4
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.32  | 50   | 0.9063          | 0.7006    | 0.757  | 0.7277 | 0.7607   |
| No log        | 2.63  | 100  | 0.6387          | 0.7930    | 0.858  | 0.8242 | 0.7967   |
| No log        | 3.95  | 150  | 0.5691          | 0.8171    | 0.8825 | 0.8486 | 0.8254   |
| No log        | 5.26  | 200  | 0.5723          | 0.8315    | 0.881  | 0.8555 | 0.8223   |
| No log        | 6.58  | 250  | 0.5897          | 0.8475    | 0.9    | 0.8729 | 0.8292   |
| No log        | 7.89  | 300  | 0.6122          | 0.8482    | 0.9025 | 0.8745 | 0.8283   |
| No log        | 9.21  | 350  | 0.6045          | 0.8505    | 0.899  | 0.8741 | 0.8392   |
| No log        | 10.53 | 400  | 0.5662          | 0.8708    | 0.9    | 0.8852 | 0.8446   |
| No log        | 11.84 | 450  | 0.5973          | 0.8739    | 0.9045 | 0.8889 | 0.8437   |
| 0.4305        | 13.16 | 500  | 0.6187          | 0.8757    | 0.9055 | 0.8904 | 0.8437   |


### Framework versions

- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 2.13.2
- Tokenizers 0.10.1