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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-UsingAlgoDataset_427Images
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-finetuned-UsingAlgoDataset_427Images
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: 0.0022
- Precision: 0.9892
- Recall: 0.9880
- F1: 0.9886
- Accuracy: 0.9997
## 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: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.62 | 50 | 0.0349 | 0.7521 | 0.6300 | 0.6857 | 0.9926 |
| No log | 1.25 | 100 | 0.0080 | 0.9538 | 0.9405 | 0.9471 | 0.9985 |
| No log | 1.88 | 150 | 0.0044 | 0.9750 | 0.9723 | 0.9736 | 0.9992 |
| No log | 2.5 | 200 | 0.0032 | 0.9834 | 0.9827 | 0.9831 | 0.9995 |
| No log | 3.12 | 250 | 0.0037 | 0.9710 | 0.9784 | 0.9747 | 0.9992 |
| No log | 3.75 | 300 | 0.0026 | 0.9861 | 0.9852 | 0.9857 | 0.9996 |
| No log | 4.38 | 350 | 0.0023 | 0.9880 | 0.9871 | 0.9875 | 0.9996 |
| No log | 5.0 | 400 | 0.0022 | 0.9883 | 0.9871 | 0.9877 | 0.9997 |
| No log | 5.62 | 450 | 0.0022 | 0.9892 | 0.9880 | 0.9886 | 0.9997 |
| 0.029 | 6.25 | 500 | 0.0022 | 0.9892 | 0.9880 | 0.9886 | 0.9997 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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