---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- funsd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-funsd
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: funsd
      type: funsd
      config: funsd
      split: test
      args: funsd
    metrics:
    - name: Precision
      type: precision
      value: 0.7998102466793169
    - name: Recall
      type: recall
      value: 0.8375558867362146
    - name: F1
      type: f1
      value: 0.8182479980587235
    - name: Accuracy
      type: accuracy
      value: 0.826102460477832
---

<!-- 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 [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0068
- Precision: 0.7998
- Recall: 0.8376
- F1: 0.8182
- Accuracy: 0.8261

## 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: 2500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 3.33  | 250  | 0.5828          | 0.7015    | 0.8033 | 0.7490 | 0.8022   |
| 0.6702        | 6.67  | 500  | 0.5765          | 0.7499    | 0.8073 | 0.7775 | 0.8253   |
| 0.6702        | 10.0  | 750  | 0.7082          | 0.7755    | 0.8236 | 0.7988 | 0.8160   |
| 0.1797        | 13.33 | 1000 | 0.7819          | 0.7807    | 0.8366 | 0.8077 | 0.8256   |
| 0.1797        | 16.67 | 1250 | 0.8199          | 0.7997    | 0.8311 | 0.8151 | 0.8227   |
| 0.0745        | 20.0  | 1500 | 0.9025          | 0.7943    | 0.8286 | 0.8111 | 0.8231   |
| 0.0745        | 23.33 | 1750 | 0.9159          | 0.7941    | 0.8470 | 0.8197 | 0.8248   |
| 0.041         | 26.67 | 2000 | 1.0012          | 0.7989    | 0.8385 | 0.8182 | 0.8210   |
| 0.041         | 30.0  | 2250 | 0.9852          | 0.8024    | 0.8450 | 0.8231 | 0.8301   |
| 0.0246        | 33.33 | 2500 | 1.0068          | 0.7998    | 0.8376 | 0.8182 | 0.8261   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3