---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: fine-tuned-rvl-cdip
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/david_hajdu/huggingface/runs/0bqlwuvd)
# fine-tuned-rvl-cdip

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7731
- F1: 0.8177

## 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: 2e-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
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 96   | 2.0377          | 0.4479 |
| No log        | 2.0   | 192  | 1.3075          | 0.6641 |
| No log        | 3.0   | 288  | 0.9850          | 0.7240 |
| No log        | 4.0   | 384  | 0.8775          | 0.7630 |
| No log        | 5.0   | 480  | 0.7824          | 0.7865 |
| 1.2987        | 6.0   | 576  | 0.7516          | 0.8021 |
| 1.2987        | 7.0   | 672  | 0.7688          | 0.7865 |
| 1.2987        | 8.0   | 768  | 0.7462          | 0.8125 |
| 1.2987        | 9.0   | 864  | 0.7731          | 0.8177 |
| 1.2987        | 10.0  | 960  | 0.7755          | 0.8125 |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1