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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Output_LayoutLMv3_v2
results: []
datasets:
- Noureddinesa/LayoutLmv3
---
<!-- 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_v2
This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1240
- Precision: 0.8174
- Recall: 0.8319
- F1: 0.8246
- Accuracy: 0.9762
## 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-07
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.27 | 100 | 0.5286 | 0.0 | 0.0 | 0.0 | 0.8867 |
| No log | 4.55 | 200 | 0.4075 | 0.0 | 0.0 | 0.0 | 0.8867 |
| No log | 6.82 | 300 | 0.3231 | 0.2258 | 0.0310 | 0.0545 | 0.8933 |
| No log | 9.09 | 400 | 0.2612 | 0.5546 | 0.2920 | 0.3826 | 0.9210 |
| 0.4595 | 11.36 | 500 | 0.2246 | 0.5897 | 0.4071 | 0.4817 | 0.9295 |
| 0.4595 | 13.64 | 600 | 0.2004 | 0.6869 | 0.6018 | 0.6415 | 0.9476 |
| 0.4595 | 15.91 | 700 | 0.1866 | 0.7019 | 0.6460 | 0.6728 | 0.9514 |
| 0.4595 | 18.18 | 800 | 0.1712 | 0.7419 | 0.7124 | 0.7269 | 0.96 |
| 0.4595 | 20.45 | 900 | 0.1599 | 0.7647 | 0.7478 | 0.7562 | 0.9638 |
| 0.1593 | 22.73 | 1000 | 0.1568 | 0.7729 | 0.7832 | 0.7780 | 0.9686 |
| 0.1593 | 25.0 | 1100 | 0.1476 | 0.7686 | 0.7788 | 0.7736 | 0.9686 |
| 0.1593 | 27.27 | 1200 | 0.1395 | 0.7930 | 0.7965 | 0.7947 | 0.9714 |
| 0.1593 | 29.55 | 1300 | 0.1372 | 0.8 | 0.8142 | 0.8070 | 0.9733 |
| 0.1593 | 31.82 | 1400 | 0.1356 | 0.8035 | 0.8142 | 0.8088 | 0.9743 |
| 0.0987 | 34.09 | 1500 | 0.1326 | 0.7939 | 0.8009 | 0.7974 | 0.9714 |
| 0.0987 | 36.36 | 1600 | 0.1292 | 0.7939 | 0.8009 | 0.7974 | 0.9714 |
| 0.0987 | 38.64 | 1700 | 0.1300 | 0.8017 | 0.8230 | 0.8122 | 0.9743 |
| 0.0987 | 40.91 | 1800 | 0.1260 | 0.8062 | 0.8097 | 0.8079 | 0.9724 |
| 0.0987 | 43.18 | 1900 | 0.1244 | 0.8017 | 0.8230 | 0.8122 | 0.9743 |
| 0.0689 | 45.45 | 2000 | 0.1228 | 0.8150 | 0.8186 | 0.8168 | 0.9752 |
| 0.0689 | 47.73 | 2100 | 0.1230 | 0.8087 | 0.8230 | 0.8158 | 0.9752 |
| 0.0689 | 50.0 | 2200 | 0.1225 | 0.8114 | 0.8186 | 0.8150 | 0.9743 |
| 0.0689 | 52.27 | 2300 | 0.1226 | 0.8114 | 0.8186 | 0.8150 | 0.9743 |
| 0.0689 | 54.55 | 2400 | 0.1237 | 0.8174 | 0.8319 | 0.8246 | 0.9762 |
| 0.0545 | 56.82 | 2500 | 0.1234 | 0.8122 | 0.8230 | 0.8176 | 0.9752 |
| 0.0545 | 59.09 | 2600 | 0.1240 | 0.8122 | 0.8230 | 0.8176 | 0.9752 |
| 0.0545 | 61.36 | 2700 | 0.1242 | 0.8122 | 0.8230 | 0.8176 | 0.9752 |
| 0.0545 | 63.64 | 2800 | 0.1241 | 0.8122 | 0.8230 | 0.8176 | 0.9752 |
| 0.0545 | 65.91 | 2900 | 0.1253 | 0.8190 | 0.8407 | 0.8297 | 0.9771 |
| 0.0491 | 68.18 | 3000 | 0.1235 | 0.8114 | 0.8186 | 0.8150 | 0.9743 |
| 0.0491 | 70.45 | 3100 | 0.1236 | 0.8166 | 0.8274 | 0.8220 | 0.9752 |
| 0.0491 | 72.73 | 3200 | 0.1231 | 0.8166 | 0.8274 | 0.8220 | 0.9752 |
| 0.0491 | 75.0 | 3300 | 0.1239 | 0.8190 | 0.8407 | 0.8297 | 0.9771 |
| 0.0491 | 77.27 | 3400 | 0.1241 | 0.8190 | 0.8407 | 0.8297 | 0.9771 |
| 0.0442 | 79.55 | 3500 | 0.1240 | 0.8174 | 0.8319 | 0.8246 | 0.9762 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |