---
library_name: transformers
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-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: 0.7822618488972314
    - name: Recall
      type: recall
      value: 0.8281172379533035
    - name: F1
      type: f1
      value: 0.8045366795366795
    - name: Accuracy
      type: accuracy
      value: 0.8135029121597528
---

<!-- 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: 1.1059
- Precision: 0.7823
- Recall: 0.8281
- F1: 0.8045
- Accuracy: 0.8135

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 10.0  | 100  | 0.7158          | 0.6964    | 0.7690 | 0.7309 | 0.7833   |
| No log        | 20.0  | 200  | 0.7508          | 0.7420    | 0.8058 | 0.7726 | 0.8076   |
| No log        | 30.0  | 300  | 0.8085          | 0.7678    | 0.8097 | 0.7882 | 0.8078   |
| No log        | 40.0  | 400  | 0.9083          | 0.7689    | 0.8147 | 0.7911 | 0.8061   |
| 0.3419        | 50.0  | 500  | 0.9294          | 0.7840    | 0.8296 | 0.8062 | 0.8128   |
| 0.3419        | 60.0  | 600  | 1.0036          | 0.7807    | 0.8366 | 0.8077 | 0.8178   |
| 0.3419        | 70.0  | 700  | 1.0983          | 0.7727    | 0.8241 | 0.7976 | 0.8044   |
| 0.3419        | 80.0  | 800  | 1.1092          | 0.7921    | 0.8251 | 0.8083 | 0.8064   |
| 0.3419        | 90.0  | 900  | 1.1010          | 0.7780    | 0.8306 | 0.8035 | 0.8133   |
| 0.0261        | 100.0 | 1000 | 1.1059          | 0.7823    | 0.8281 | 0.8045 | 0.8135   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1