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---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Output_LayoutLMv3_v99
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_v99
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: 0.1581
- Precision: 0.7822
- Recall: 0.7182
- F1: 0.7488
- Accuracy: 0.9619
## 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-07
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 2.38 | 100 | 1.4434 | 0.0283 | 0.0636 | 0.0392 | 0.6938 |
| No log | 4.76 | 200 | 0.7802 | 0.0 | 0.0 | 0.0 | 0.8945 |
| No log | 7.14 | 300 | 0.5023 | 0.0 | 0.0 | 0.0 | 0.8962 |
| No log | 9.52 | 400 | 0.4425 | 0.0 | 0.0 | 0.0 | 0.8962 |
| 0.8848 | 11.9 | 500 | 0.3951 | 0.0 | 0.0 | 0.0 | 0.8962 |
| 0.8848 | 14.29 | 600 | 0.3557 | 0.0 | 0.0 | 0.0 | 0.8962 |
| 0.8848 | 16.67 | 700 | 0.3236 | 0.0 | 0.0 | 0.0 | 0.8962 |
| 0.8848 | 19.05 | 800 | 0.2988 | 0.2143 | 0.0273 | 0.0484 | 0.8997 |
| 0.8848 | 21.43 | 900 | 0.2787 | 0.4167 | 0.0909 | 0.1493 | 0.9066 |
| 0.3328 | 23.81 | 1000 | 0.2623 | 0.4839 | 0.1364 | 0.2128 | 0.9100 |
| 0.3328 | 26.19 | 1100 | 0.2474 | 0.5238 | 0.2 | 0.2895 | 0.9187 |
| 0.3328 | 28.57 | 1200 | 0.2358 | 0.6038 | 0.2909 | 0.3926 | 0.9308 |
| 0.3328 | 30.95 | 1300 | 0.2267 | 0.6 | 0.3 | 0.4 | 0.9325 |
| 0.3328 | 33.33 | 1400 | 0.2172 | 0.6032 | 0.3455 | 0.4393 | 0.9343 |
| 0.2435 | 35.71 | 1500 | 0.2113 | 0.5821 | 0.3545 | 0.4407 | 0.9343 |
| 0.2435 | 38.1 | 1600 | 0.2042 | 0.5634 | 0.3636 | 0.4420 | 0.9343 |
| 0.2435 | 40.48 | 1700 | 0.1981 | 0.6203 | 0.4455 | 0.5185 | 0.9429 |
| 0.2435 | 42.86 | 1800 | 0.1923 | 0.6628 | 0.5182 | 0.5816 | 0.9446 |
| 0.2435 | 45.24 | 1900 | 0.1895 | 0.6818 | 0.5455 | 0.6061 | 0.9481 |
| 0.1971 | 47.62 | 2000 | 0.1846 | 0.7128 | 0.6091 | 0.6569 | 0.9533 |
| 0.1971 | 50.0 | 2100 | 0.1811 | 0.7526 | 0.6636 | 0.7053 | 0.9585 |
| 0.1971 | 52.38 | 2200 | 0.1797 | 0.7396 | 0.6455 | 0.6893 | 0.9567 |
| 0.1971 | 54.76 | 2300 | 0.1755 | 0.7653 | 0.6818 | 0.7212 | 0.9602 |
| 0.1971 | 57.14 | 2400 | 0.1745 | 0.7526 | 0.6636 | 0.7053 | 0.9585 |
| 0.1722 | 59.52 | 2500 | 0.1707 | 0.7526 | 0.6636 | 0.7053 | 0.9585 |
| 0.1722 | 61.9 | 2600 | 0.1672 | 0.7526 | 0.6636 | 0.7053 | 0.9585 |
| 0.1722 | 64.29 | 2700 | 0.1662 | 0.7677 | 0.6909 | 0.7273 | 0.9602 |
| 0.1722 | 66.67 | 2800 | 0.1659 | 0.7677 | 0.6909 | 0.7273 | 0.9602 |
| 0.1722 | 69.05 | 2900 | 0.1650 | 0.78 | 0.7091 | 0.7429 | 0.9619 |
| 0.1558 | 71.43 | 3000 | 0.1633 | 0.78 | 0.7091 | 0.7429 | 0.9619 |
| 0.1558 | 73.81 | 3100 | 0.1613 | 0.78 | 0.7091 | 0.7429 | 0.9619 |
| 0.1558 | 76.19 | 3200 | 0.1605 | 0.78 | 0.7091 | 0.7429 | 0.9619 |
| 0.1558 | 78.57 | 3300 | 0.1600 | 0.78 | 0.7091 | 0.7429 | 0.9619 |
| 0.1558 | 80.95 | 3400 | 0.1594 | 0.78 | 0.7091 | 0.7429 | 0.9619 |
| 0.1461 | 83.33 | 3500 | 0.1588 | 0.7822 | 0.7182 | 0.7488 | 0.9619 |
| 0.1461 | 85.71 | 3600 | 0.1588 | 0.7822 | 0.7182 | 0.7488 | 0.9619 |
| 0.1461 | 88.1 | 3700 | 0.1584 | 0.7822 | 0.7182 | 0.7488 | 0.9619 |
| 0.1461 | 90.48 | 3800 | 0.1583 | 0.7822 | 0.7182 | 0.7488 | 0.9619 |
| 0.1461 | 92.86 | 3900 | 0.1581 | 0.7822 | 0.7182 | 0.7488 | 0.9619 |
| 0.1438 | 95.24 | 4000 | 0.1581 | 0.7822 | 0.7182 | 0.7488 | 0.9619 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.13.3 |