|
--- |
|
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 |
