---
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: 0.8553875236294896
    - name: Recall
      type: recall
      value: 0.905
    - name: F1
      type: f1
      value: 0.8794946550048591
    - name: Accuracy
      type: accuracy
      value: 0.833371612310519
---

<!-- 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: 0.5784
- Precision: 0.8554
- Recall: 0.905
- F1: 0.8795
- Accuracy: 0.8334

## 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: 400

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.66  | 25   | 1.3511          | 0.3301    | 0.3585 | 0.3437 | 0.5721   |
| No log        | 1.32  | 50   | 0.9059          | 0.6965    | 0.7515 | 0.7229 | 0.7615   |
| No log        | 1.97  | 75   | 0.7164          | 0.7613    | 0.831  | 0.7946 | 0.7796   |
| No log        | 2.63  | 100  | 0.6393          | 0.7947    | 0.8575 | 0.8249 | 0.7993   |
| No log        | 3.29  | 125  | 0.5756          | 0.8138    | 0.87   | 0.8410 | 0.8104   |
| No log        | 3.95  | 150  | 0.5508          | 0.8197    | 0.884  | 0.8506 | 0.8323   |
| No log        | 4.61  | 175  | 0.5458          | 0.8325    | 0.8895 | 0.8600 | 0.8328   |
| No log        | 5.26  | 200  | 0.5740          | 0.8234    | 0.8765 | 0.8491 | 0.8266   |
| No log        | 5.92  | 225  | 0.5719          | 0.8532    | 0.8895 | 0.8710 | 0.8361   |
| No log        | 6.58  | 250  | 0.5436          | 0.8439    | 0.9055 | 0.8736 | 0.8264   |
| No log        | 7.24  | 275  | 0.5714          | 0.8520    | 0.9065 | 0.8784 | 0.8290   |
| No log        | 7.89  | 300  | 0.5853          | 0.8560    | 0.9035 | 0.8791 | 0.8281   |
| No log        | 8.55  | 325  | 0.5702          | 0.8578    | 0.905  | 0.8808 | 0.8390   |
| No log        | 9.21  | 350  | 0.5667          | 0.8552    | 0.901  | 0.8775 | 0.8419   |
| No log        | 9.87  | 375  | 0.5793          | 0.8552    | 0.9035 | 0.8787 | 0.8338   |
| No log        | 10.53 | 400  | 0.5784          | 0.8554    | 0.905  | 0.8795 | 0.8334   |


### Framework versions

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