---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
model-index:
- name: layoutlmv3-custom_no_text
  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-custom_no_text

This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3487
- eval_noise: {'precision': 0.6914893617021277, 'recall': 0.7210776545166403, 'f1': 0.7059736229635376, 'number': 631}
- eval_signal: {'precision': 0.684931506849315, 'recall': 0.7142857142857143, 'f1': 0.6993006993006993, 'number': 630}
- eval_overall_precision: 0.6882
- eval_overall_recall: 0.7177
- eval_overall_f1: 0.7026
- eval_overall_accuracy: 0.9325
- eval_runtime: 0.9604
- eval_samples_per_second: 37.484
- eval_steps_per_second: 5.206
- epoch: 24.0
- step: 432

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0