---
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: 0.9059871350816427
    - name: Recall
      type: recall
      value: 0.9155
    - name: F1
      type: f1
      value: 0.9107187266849044
    - name: Accuracy
      type: accuracy
      value: 0.8407211759301791
---

<!-- 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: 0.8860
- Precision: 0.9060
- Recall: 0.9155
- F1: 0.9107
- Accuracy: 0.8407

## 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: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.63  | 100  | 0.6111          | 0.7963    | 0.864  | 0.8288 | 0.7987   |
| No log        | 5.26  | 200  | 0.5861          | 0.8507    | 0.883  | 0.8665 | 0.8266   |
| No log        | 7.89  | 300  | 0.5856          | 0.8654    | 0.9005 | 0.8826 | 0.8426   |
| No log        | 10.53 | 400  | 0.6502          | 0.8801    | 0.8995 | 0.8897 | 0.8427   |
| 0.4088        | 13.16 | 500  | 0.7679          | 0.8880    | 0.904  | 0.8959 | 0.8373   |
| 0.4088        | 15.79 | 600  | 0.8371          | 0.8820    | 0.904  | 0.8928 | 0.8333   |
| 0.4088        | 18.42 | 700  | 0.8320          | 0.8931    | 0.9145 | 0.9037 | 0.8336   |
| 0.4088        | 21.05 | 800  | 0.8494          | 0.8969    | 0.9135 | 0.9051 | 0.8341   |
| 0.4088        | 23.68 | 900  | 0.8700          | 0.9005    | 0.914  | 0.9072 | 0.8385   |
| 0.061         | 26.32 | 1000 | 0.8860          | 0.9060    | 0.9155 | 0.9107 | 0.8407   |


### Framework versions

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