---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
- Document Layout
- LayoutLMv3
datasets:
- funsd-layoutlmv3
metrics:
- f1
- accuracy
- recall
- precision
model-index:
- name: layoutlmv3-base-fine_tuned-FUNSD_dataset
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: funsd-layoutlmv3
      type: funsd-layoutlmv3
      config: funsd
      split: test
      args: funsd
    metrics:
    - name: Precision
      type: precision
      value: 0.8978890525282278
    - name: Recall
      type: recall
      value: 0.9085941381023348
    - name: F1
      type: f1
      value: 0.9032098765432099
    - name: Accuracy
      type: accuracy
      value: 0.8461904195887318
language:
- en
---

# layoutlmv3-base-fine_tuned-FUNSD_dataset

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co./microsoft/layoutlmv3-base) on the funsd-layoutlmv3 dataset.

It achieves the following results on the evaluation set:
- Loss: 0.2956
- Precision: 0.8979
- Recall: 0.9086
- F1: 0.9032
- Accuracy: 0.8462

## Model description

For more information on how it was created, check out the following link: 

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://huggingface.co./datasets/nielsr/funsd-layoutlmv3

## 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: 2000

### Training results

| Train Loss | Epoch | Step | Valid. Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2149        | 1.33  | 100  | 0.2402          | 0.7469    | 0.8212 | 0.7823 | 0.7758   |
| 0.1466        | 2.67  | 200  | 0.1869          | 0.8161    | 0.8838 | 0.8486 | 0.8273   |
| 0.1122        | 4.0   | 300  | 0.1902          | 0.8538    | 0.8997 | 0.8761 | 0.8316   |
| 0.0757        | 5.33  | 400  | 0.1857          | 0.8354    | 0.8927 | 0.8631 | 0.8349   |
| 0.0427        | 6.67  | 500  | 0.2091          | 0.8792    | 0.8897 | 0.8844 | 0.8446   |
| 0.0495        | 8.0   | 600  | 0.2235          | 0.8825    | 0.9031 | 0.8927 | 0.8370   |
| 0.0369        | 9.33  | 700  | 0.2532          | 0.8826    | 0.9146 | 0.8983 | 0.8349   |
| 0.0329        | 10.67 | 800  | 0.2576          | 0.8829    | 0.8992 | 0.8910 | 0.8474   |
| 0.0229        | 12.0  | 900  | 0.2579          | 0.8827    | 0.8937 | 0.8882 | 0.8443   |
| 0.0219        | 13.33 | 1000 | 0.2710          | 0.8710    | 0.8987 | 0.8846 | 0.8347   |
| 0.0191        | 14.67 | 1100 | 0.2582          | 0.8889    | 0.9061 | 0.8974 | 0.8454   |
| 0.0179        | 16.0  | 1200 | 0.2646          | 0.8870    | 0.9006 | 0.8938 | 0.8356   |
| 0.0135        | 17.33 | 1300 | 0.2798          | 0.8949    | 0.9180 | 0.9063 | 0.8512   |
| 0.007         | 18.67 | 1400 | 0.2944          | 0.8988    | 0.9091 | 0.9039 | 0.8455   |
| 0.0064        | 20.0  | 1500 | 0.2822          | 0.8938    | 0.9071 | 0.9004 | 0.8452   |
| 0.0089        | 21.33 | 1600 | 0.3003          | 0.8941    | 0.9101 | 0.9020 | 0.8484   |
| 0.0099        | 22.67 | 1700 | 0.3008          | 0.8942    | 0.9071 | 0.9006 | 0.8439   |
| 0.0069        | 24.0  | 1800 | 0.2965          | 0.8942    | 0.9071 | 0.9006 | 0.8386   |
| 0.0048        | 25.33 | 1900 | 0.2973          | 0.9027    | 0.9076 | 0.9051 | 0.8501   |
| 0.0069        | 26.67 | 2000 | 0.2956          | 0.8979    | 0.9086 | 0.9032 | 0.8462   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3