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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-10
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. -->
# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-10
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: 10.1413
- Accuracy: 0.7325
- Exit 0 Accuracy: 0.1725
- Exit 1 Accuracy: 0.2175
- Exit 2 Accuracy: 0.6075
- Exit 3 Accuracy: 0.715
- Exit 4 Accuracy: 0.735
## 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: 2e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:|
| No log | 0.96 | 16 | 16.4817 | 0.17 | 0.0825 | 0.045 | 0.105 | 0.0625 | 0.0625 |
| No log | 1.98 | 33 | 15.9950 | 0.2675 | 0.1 | 0.1325 | 0.195 | 0.1775 | 0.2425 |
| No log | 3.0 | 50 | 14.9811 | 0.475 | 0.1025 | 0.1475 | 0.24 | 0.29 | 0.4425 |
| No log | 3.96 | 66 | 14.0127 | 0.5675 | 0.105 | 0.1425 | 0.27 | 0.3975 | 0.505 |
| No log | 4.98 | 83 | 13.3047 | 0.6075 | 0.125 | 0.1425 | 0.3175 | 0.43 | 0.595 |
| No log | 6.0 | 100 | 12.7573 | 0.6125 | 0.125 | 0.1475 | 0.325 | 0.495 | 0.615 |
| No log | 6.96 | 116 | 12.3656 | 0.645 | 0.1175 | 0.155 | 0.33 | 0.5175 | 0.6375 |
| No log | 7.98 | 133 | 11.9582 | 0.6625 | 0.115 | 0.16 | 0.3525 | 0.5725 | 0.67 |
| No log | 9.0 | 150 | 11.6533 | 0.6825 | 0.1225 | 0.16 | 0.375 | 0.6 | 0.7075 |
| No log | 9.96 | 166 | 11.5143 | 0.685 | 0.1525 | 0.1625 | 0.38 | 0.6 | 0.675 |
| No log | 10.98 | 183 | 11.3152 | 0.6625 | 0.115 | 0.1625 | 0.41 | 0.6225 | 0.6725 |
| No log | 12.0 | 200 | 11.0708 | 0.695 | 0.11 | 0.1625 | 0.425 | 0.6225 | 0.7075 |
| No log | 12.96 | 216 | 11.0412 | 0.6975 | 0.1125 | 0.1575 | 0.4 | 0.645 | 0.685 |
| No log | 13.98 | 233 | 10.8782 | 0.7125 | 0.1425 | 0.165 | 0.4275 | 0.6325 | 0.7075 |
| No log | 15.0 | 250 | 10.7282 | 0.7075 | 0.115 | 0.165 | 0.4225 | 0.65 | 0.7175 |
| No log | 15.96 | 266 | 10.7039 | 0.695 | 0.15 | 0.16 | 0.4375 | 0.6375 | 0.69 |
| No log | 16.98 | 283 | 10.5455 | 0.7125 | 0.13 | 0.165 | 0.4375 | 0.6675 | 0.715 |
| No log | 18.0 | 300 | 10.5214 | 0.7075 | 0.1275 | 0.17 | 0.45 | 0.6825 | 0.7075 |
| No log | 18.96 | 316 | 10.4995 | 0.715 | 0.155 | 0.1725 | 0.4525 | 0.68 | 0.7125 |
| No log | 19.98 | 333 | 10.3224 | 0.725 | 0.1475 | 0.1825 | 0.46 | 0.68 | 0.7225 |
| No log | 21.0 | 350 | 10.4247 | 0.71 | 0.1425 | 0.1825 | 0.4625 | 0.68 | 0.71 |
| No log | 21.96 | 366 | 10.3881 | 0.705 | 0.1375 | 0.1825 | 0.46 | 0.66 | 0.7125 |
| No log | 22.98 | 383 | 10.3065 | 0.715 | 0.1375 | 0.1875 | 0.465 | 0.6925 | 0.7225 |
| No log | 24.0 | 400 | 10.1955 | 0.72 | 0.145 | 0.1875 | 0.4725 | 0.695 | 0.7225 |
| No log | 24.96 | 416 | 10.1607 | 0.72 | 0.165 | 0.19 | 0.4925 | 0.7075 | 0.7175 |
| No log | 25.98 | 433 | 10.2416 | 0.72 | 0.14 | 0.195 | 0.48 | 0.7025 | 0.7275 |
| No log | 27.0 | 450 | 10.1321 | 0.715 | 0.145 | 0.1875 | 0.4925 | 0.7125 | 0.72 |
| No log | 27.96 | 466 | 10.1982 | 0.7275 | 0.145 | 0.1875 | 0.4875 | 0.7075 | 0.73 |
| No log | 28.98 | 483 | 10.2237 | 0.72 | 0.1575 | 0.19 | 0.515 | 0.7 | 0.7225 |
| 10.0174 | 30.0 | 500 | 10.1426 | 0.7175 | 0.1675 | 0.1975 | 0.5275 | 0.7125 | 0.7225 |
| 10.0174 | 30.96 | 516 | 10.1056 | 0.7325 | 0.14 | 0.1975 | 0.515 | 0.715 | 0.7325 |
| 10.0174 | 31.98 | 533 | 10.1616 | 0.7225 | 0.1525 | 0.195 | 0.5275 | 0.7175 | 0.72 |
| 10.0174 | 33.0 | 550 | 10.1053 | 0.7325 | 0.1425 | 0.195 | 0.525 | 0.7125 | 0.7275 |
| 10.0174 | 33.96 | 566 | 10.1581 | 0.7175 | 0.165 | 0.2 | 0.5375 | 0.71 | 0.71 |
| 10.0174 | 34.98 | 583 | 10.0835 | 0.7225 | 0.15 | 0.2025 | 0.5375 | 0.715 | 0.7225 |
| 10.0174 | 36.0 | 600 | 10.1349 | 0.725 | 0.1425 | 0.2 | 0.5375 | 0.7025 | 0.725 |
| 10.0174 | 36.96 | 616 | 10.0424 | 0.7325 | 0.1625 | 0.1975 | 0.545 | 0.7225 | 0.735 |
| 10.0174 | 37.98 | 633 | 10.0692 | 0.73 | 0.155 | 0.195 | 0.5525 | 0.7225 | 0.74 |
| 10.0174 | 39.0 | 650 | 10.0838 | 0.7325 | 0.1625 | 0.1975 | 0.56 | 0.7225 | 0.7375 |
| 10.0174 | 39.96 | 666 | 10.1160 | 0.7275 | 0.1675 | 0.1975 | 0.5575 | 0.7225 | 0.725 |
| 10.0174 | 40.98 | 683 | 10.0971 | 0.735 | 0.1675 | 0.1975 | 0.5625 | 0.7175 | 0.73 |
| 10.0174 | 42.0 | 700 | 10.1207 | 0.73 | 0.165 | 0.2 | 0.5775 | 0.715 | 0.7275 |
| 10.0174 | 42.96 | 716 | 10.1448 | 0.7325 | 0.175 | 0.205 | 0.5775 | 0.7175 | 0.73 |
| 10.0174 | 43.98 | 733 | 10.0945 | 0.735 | 0.1675 | 0.21 | 0.5775 | 0.7175 | 0.735 |
| 10.0174 | 45.0 | 750 | 10.1789 | 0.73 | 0.17 | 0.2175 | 0.5775 | 0.7125 | 0.7275 |
| 10.0174 | 45.96 | 766 | 10.1274 | 0.735 | 0.175 | 0.215 | 0.5875 | 0.7075 | 0.735 |
| 10.0174 | 46.98 | 783 | 10.1656 | 0.735 | 0.155 | 0.2125 | 0.5875 | 0.7125 | 0.7375 |
| 10.0174 | 48.0 | 800 | 10.1557 | 0.7275 | 0.16 | 0.215 | 0.6025 | 0.715 | 0.7325 |
| 10.0174 | 48.96 | 816 | 10.1436 | 0.74 | 0.165 | 0.215 | 0.6025 | 0.7175 | 0.735 |
| 10.0174 | 49.98 | 833 | 10.1474 | 0.7325 | 0.1625 | 0.215 | 0.6 | 0.715 | 0.735 |
| 10.0174 | 51.0 | 850 | 10.1647 | 0.7275 | 0.1725 | 0.2175 | 0.605 | 0.7175 | 0.7325 |
| 10.0174 | 51.96 | 866 | 10.1375 | 0.73 | 0.1775 | 0.215 | 0.6025 | 0.7125 | 0.7375 |
| 10.0174 | 52.98 | 883 | 10.1458 | 0.7325 | 0.1675 | 0.2175 | 0.605 | 0.7125 | 0.7375 |
| 10.0174 | 54.0 | 900 | 10.1527 | 0.7275 | 0.175 | 0.22 | 0.6025 | 0.715 | 0.73 |
| 10.0174 | 54.96 | 916 | 10.1349 | 0.7325 | 0.175 | 0.2175 | 0.6025 | 0.72 | 0.735 |
| 10.0174 | 55.98 | 933 | 10.1376 | 0.7325 | 0.175 | 0.22 | 0.6025 | 0.72 | 0.7325 |
| 10.0174 | 57.0 | 950 | 10.1413 | 0.7325 | 0.1725 | 0.2175 | 0.6075 | 0.715 | 0.7325 |
| 10.0174 | 57.6 | 960 | 10.1413 | 0.7325 | 0.1725 | 0.2175 | 0.6075 | 0.715 | 0.735 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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