---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- funsd-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ft-ms-layoutlmv3-funsd-layoutlmv3
  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: 0.89171974522293
    - name: Recall
      type: recall
      value: 0.9041231992051664
    - name: F1
      type: f1
      value: 0.8978786383818451
    - name: Accuracy
      type: accuracy
      value: 0.8377510994888863
---

<!-- 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. -->

# ft-ms-layoutlmv3-funsd-layoutlmv3

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: 1.0021
- Precision: 0.8917
- Recall: 0.9041
- F1: 0.8979
- Accuracy: 0.8378

## 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: 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        | 10.0  | 100  | 0.5222          | 0.8477    | 0.8823 | 0.8647 | 0.8436   |
| No log        | 20.0  | 200  | 0.6686          | 0.8736    | 0.9026 | 0.8879 | 0.8357   |
| No log        | 30.0  | 300  | 0.7175          | 0.8759    | 0.9151 | 0.8950 | 0.8286   |
| No log        | 40.0  | 400  | 0.7636          | 0.8832    | 0.8977 | 0.8904 | 0.8426   |
| 0.2392        | 50.0  | 500  | 0.9518          | 0.8820    | 0.9026 | 0.8922 | 0.8178   |
| 0.2392        | 60.0  | 600  | 0.9803          | 0.8771    | 0.8897 | 0.8834 | 0.8121   |
| 0.2392        | 70.0  | 700  | 1.0956          | 0.8883    | 0.9086 | 0.8983 | 0.8173   |
| 0.2392        | 80.0  | 800  | 0.9517          | 0.8930    | 0.9076 | 0.9002 | 0.8444   |
| 0.2392        | 90.0  | 900  | 1.0337          | 0.8950    | 0.9061 | 0.9005 | 0.8379   |
| 0.0083        | 100.0 | 1000 | 1.0021          | 0.8917    | 0.9041 | 0.8979 | 0.8378   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0