---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- funsd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-test
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: funsd
      type: funsd
    metrics:
    - name: Precision
      type: precision
      value: 0.8972868217054264
    - name: Recall
      type: recall
      value: 0.920019870839543
    - name: F1
      type: f1
      value: 0.9085111601667893
    - name: Accuracy
      type: accuracy
      value: 0.8480922382027815
---

<!-- 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-test

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: 0.8036
- Precision: 0.8973
- Recall: 0.9200
- F1: 0.9085
- Accuracy: 0.8481

## 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: 8
- eval_batch_size: 8
- 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        | 5.26  | 100  | 0.5115          | 0.8071    | 0.8624 | 0.8338 | 0.8407   |
| No log        | 10.53 | 200  | 0.4661          | 0.8730    | 0.9086 | 0.8905 | 0.8546   |
| No log        | 15.79 | 300  | 0.5613          | 0.8914    | 0.9091 | 0.9001 | 0.8552   |
| No log        | 21.05 | 400  | 0.6767          | 0.8937    | 0.8982 | 0.8959 | 0.8507   |
| 0.3022        | 26.32 | 500  | 0.7020          | 0.8935    | 0.9165 | 0.9049 | 0.8626   |
| 0.3022        | 31.58 | 600  | 0.7108          | 0.9040    | 0.9220 | 0.9129 | 0.8591   |
| 0.3022        | 36.84 | 700  | 0.7378          | 0.9049    | 0.9175 | 0.9112 | 0.8517   |
| 0.3022        | 42.11 | 800  | 0.7892          | 0.9026    | 0.9210 | 0.9117 | 0.8537   |
| 0.3022        | 47.37 | 900  | 0.8133          | 0.8995    | 0.9205 | 0.9099 | 0.8490   |
| 0.0223        | 52.63 | 1000 | 0.8036          | 0.8973    | 0.9200 | 0.9085 | 0.8481   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1