---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-funsd
  results: []
---

<!-- 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-funsd

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8428
- Precision: 0.8993
- Recall: 0.9046
- F1: 0.9019
- Accuracy: 0.8354

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.63  | 100  | 0.6294          | 0.7864    | 0.8286 | 0.8070 | 0.7966   |
| No log        | 5.26  | 200  | 0.5034          | 0.8389    | 0.8793 | 0.8586 | 0.8343   |
| No log        | 7.89  | 300  | 0.5673          | 0.8597    | 0.9011 | 0.8799 | 0.8416   |
| No log        | 10.53 | 400  | 0.5730          | 0.8783    | 0.9106 | 0.8941 | 0.8395   |
| 0.4463        | 13.16 | 500  | 0.6630          | 0.8923    | 0.9016 | 0.8970 | 0.8412   |
| 0.4463        | 15.79 | 600  | 0.7048          | 0.8850    | 0.8947 | 0.8898 | 0.8329   |
| 0.4463        | 18.42 | 700  | 0.7772          | 0.8925    | 0.9071 | 0.8997 | 0.8317   |
| 0.4463        | 21.05 | 800  | 0.8408          | 0.8959    | 0.9016 | 0.8987 | 0.8313   |
| 0.4463        | 23.68 | 900  | 0.8580          | 0.8918    | 0.9051 | 0.8984 | 0.8313   |
| 0.0611        | 26.32 | 1000 | 0.8428          | 0.8993    | 0.9046 | 0.9019 | 0.8354   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0