---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Output_LayoutLMv3_v7
  results: []
datasets:
- Noureddinesa/LayoutLmv3_v1
---

<!-- 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. -->

# Output_LayoutLMv3_v7

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: 0.1075
- Precision: 0.7928
- Recall: 0.8
- F1: 0.7964
- Accuracy: 0.9723

## 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-06
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 2600

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 9.09   | 100  | 0.4138          | 0.0       | 0.0    | 0.0    | 0.8962   |
| No log        | 18.18  | 200  | 0.2709          | 0.1667    | 0.0273 | 0.0469 | 0.9014   |
| No log        | 27.27  | 300  | 0.2003          | 0.6234    | 0.4364 | 0.5134 | 0.9360   |
| No log        | 36.36  | 400  | 0.1711          | 0.6496    | 0.6909 | 0.6696 | 0.9481   |
| 0.3384        | 45.45  | 500  | 0.1624          | 0.6667    | 0.7273 | 0.6957 | 0.9498   |
| 0.3384        | 54.55  | 600  | 0.1502          | 0.6803    | 0.7545 | 0.7155 | 0.9550   |
| 0.3384        | 63.64  | 700  | 0.1428          | 0.7227    | 0.7818 | 0.7511 | 0.9602   |
| 0.3384        | 72.73  | 800  | 0.1452          | 0.7049    | 0.7818 | 0.7414 | 0.9550   |
| 0.3384        | 81.82  | 900  | 0.1260          | 0.7544    | 0.7818 | 0.7679 | 0.9671   |
| 0.0995        | 90.91  | 1000 | 0.1254          | 0.7544    | 0.7818 | 0.7679 | 0.9671   |
| 0.0995        | 100.0  | 1100 | 0.1211          | 0.7863    | 0.8364 | 0.8106 | 0.9706   |
| 0.0995        | 109.09 | 1200 | 0.1093          | 0.7739    | 0.8091 | 0.7911 | 0.9706   |
| 0.0995        | 118.18 | 1300 | 0.1081          | 0.7946    | 0.8091 | 0.8018 | 0.9723   |
| 0.0995        | 127.27 | 1400 | 0.1108          | 0.7778    | 0.8273 | 0.8018 | 0.9723   |
| 0.0608        | 136.36 | 1500 | 0.1115          | 0.7627    | 0.8182 | 0.7895 | 0.9706   |
| 0.0608        | 145.45 | 1600 | 0.1034          | 0.8053    | 0.8273 | 0.8161 | 0.9740   |
| 0.0608        | 154.55 | 1700 | 0.1050          | 0.7895    | 0.8182 | 0.8036 | 0.9723   |
| 0.0608        | 163.64 | 1800 | 0.1093          | 0.7739    | 0.8091 | 0.7911 | 0.9706   |
| 0.0608        | 172.73 | 1900 | 0.1043          | 0.7965    | 0.8182 | 0.8072 | 0.9723   |
| 0.0443        | 181.82 | 2000 | 0.1048          | 0.8036    | 0.8182 | 0.8108 | 0.9758   |
| 0.0443        | 190.91 | 2100 | 0.1067          | 0.8036    | 0.8182 | 0.8108 | 0.9758   |
| 0.0443        | 200.0  | 2200 | 0.1069          | 0.8036    | 0.8182 | 0.8108 | 0.9740   |
| 0.0443        | 209.09 | 2300 | 0.1083          | 0.7928    | 0.8    | 0.7964 | 0.9723   |
| 0.0443        | 218.18 | 2400 | 0.1079          | 0.7928    | 0.8    | 0.7964 | 0.9723   |
| 0.0381        | 227.27 | 2500 | 0.1076          | 0.7928    | 0.8    | 0.7964 | 0.9723   |
| 0.0381        | 236.36 | 2600 | 0.1075          | 0.7928    | 0.8    | 0.7964 | 0.9723   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.13.3