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# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_6_gate

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: 0.9813
- Accuracy: 0.7425
- Exit 0 Accuracy: 0.055
- Exit 1 Accuracy: 0.1075

## 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: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 192
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|
| No log        | 0.96  | 4    | 2.7555          | 0.09     | 0.0425          | 0.0675          |
| No log        | 1.96  | 8    | 2.7103          | 0.145    | 0.0425          | 0.06            |
| No log        | 2.96  | 12   | 2.6243          | 0.175    | 0.0475          | 0.0625          |
| No log        | 3.96  | 16   | 2.5457          | 0.19     | 0.0475          | 0.06            |
| No log        | 4.96  | 20   | 2.4802          | 0.22     | 0.055           | 0.0725          |
| No log        | 5.96  | 24   | 2.3688          | 0.29     | 0.0525          | 0.1125          |
| No log        | 6.96  | 28   | 2.2693          | 0.33     | 0.0525          | 0.105           |
| No log        | 7.96  | 32   | 2.1806          | 0.35     | 0.0525          | 0.1275          |
| No log        | 8.96  | 36   | 2.0652          | 0.395    | 0.0525          | 0.125           |
| No log        | 9.96  | 40   | 1.9632          | 0.4325   | 0.0525          | 0.1225          |
| No log        | 10.96 | 44   | 1.8904          | 0.4725   | 0.05            | 0.1175          |
| No log        | 11.96 | 48   | 1.7364          | 0.5575   | 0.05            | 0.1225          |
| No log        | 12.96 | 52   | 1.6698          | 0.5525   | 0.05            | 0.12            |
| No log        | 13.96 | 56   | 1.5785          | 0.59     | 0.0525          | 0.115           |
| No log        | 14.96 | 60   | 1.5102          | 0.5975   | 0.0575          | 0.12            |
| No log        | 15.96 | 64   | 1.3949          | 0.64     | 0.0575          | 0.12            |
| No log        | 16.96 | 68   | 1.3608          | 0.625    | 0.0575          | 0.125           |
| No log        | 17.96 | 72   | 1.3010          | 0.64     | 0.0575          | 0.125           |
| No log        | 18.96 | 76   | 1.2554          | 0.665    | 0.0575          | 0.125           |
| No log        | 19.96 | 80   | 1.1876          | 0.675    | 0.0575          | 0.1225          |
| No log        | 20.96 | 84   | 1.1572          | 0.705    | 0.0575          | 0.13            |
| No log        | 21.96 | 88   | 1.1204          | 0.6875   | 0.0575          | 0.1275          |
| No log        | 22.96 | 92   | 1.0787          | 0.71     | 0.0575          | 0.1375          |
| No log        | 23.96 | 96   | 1.0833          | 0.7075   | 0.0575          | 0.13            |
| No log        | 24.96 | 100  | 1.0345          | 0.725    | 0.0575          | 0.13            |
| No log        | 25.96 | 104  | 1.0832          | 0.7      | 0.0575          | 0.14            |
| No log        | 26.96 | 108  | 1.0061          | 0.7275   | 0.0575          | 0.1325          |
| No log        | 27.96 | 112  | 1.0357          | 0.695    | 0.0575          | 0.13            |
| No log        | 28.96 | 116  | 0.9696          | 0.7325   | 0.0575          | 0.1275          |
| No log        | 29.96 | 120  | 1.0087          | 0.6975   | 0.0575          | 0.1275          |
| No log        | 30.96 | 124  | 0.9800          | 0.72     | 0.0575          | 0.1175          |
| No log        | 31.96 | 128  | 0.9802          | 0.715    | 0.0575          | 0.125           |
| No log        | 32.96 | 132  | 0.9751          | 0.7375   | 0.0575          | 0.13            |
| No log        | 33.96 | 136  | 0.9578          | 0.725    | 0.0575          | 0.1275          |
| No log        | 34.96 | 140  | 0.9624          | 0.725    | 0.0575          | 0.1225          |
| No log        | 35.96 | 144  | 0.9676          | 0.72     | 0.0575          | 0.13            |
| No log        | 36.96 | 148  | 0.9572          | 0.73     | 0.0575          | 0.1175          |
| No log        | 37.96 | 152  | 1.0086          | 0.7175   | 0.0575          | 0.125           |
| No log        | 38.96 | 156  | 0.9555          | 0.735    | 0.055           | 0.11            |
| No log        | 39.96 | 160  | 0.9469          | 0.74     | 0.055           | 0.115           |
| No log        | 40.96 | 164  | 0.9835          | 0.7275   | 0.055           | 0.115           |
| No log        | 41.96 | 168  | 0.9364          | 0.745    | 0.055           | 0.1075          |
| No log        | 42.96 | 172  | 0.9590          | 0.74     | 0.055           | 0.105           |
| No log        | 43.96 | 176  | 0.9499          | 0.7425   | 0.055           | 0.1025          |
| No log        | 44.96 | 180  | 0.9731          | 0.7375   | 0.055           | 0.1             |
| No log        | 45.96 | 184  | 0.9719          | 0.725    | 0.055           | 0.1025          |
| No log        | 46.96 | 188  | 0.9669          | 0.7375   | 0.055           | 0.105           |
| No log        | 47.96 | 192  | 0.9713          | 0.7325   | 0.055           | 0.11            |
| No log        | 48.96 | 196  | 0.9738          | 0.7475   | 0.055           | 0.1075          |
| No log        | 49.96 | 200  | 0.9662          | 0.7425   | 0.055           | 0.1025          |
| No log        | 50.96 | 204  | 0.9848          | 0.73     | 0.055           | 0.1025          |
| No log        | 51.96 | 208  | 0.9689          | 0.7475   | 0.055           | 0.11            |
| No log        | 52.96 | 212  | 0.9690          | 0.7475   | 0.055           | 0.105           |
| No log        | 53.96 | 216  | 0.9783          | 0.7475   | 0.055           | 0.1025          |
| No log        | 54.96 | 220  | 0.9831          | 0.7425   | 0.055           | 0.1025          |
| No log        | 55.96 | 224  | 0.9778          | 0.745    | 0.055           | 0.1025          |
| No log        | 56.96 | 228  | 0.9740          | 0.7475   | 0.055           | 0.1025          |
| No log        | 57.96 | 232  | 0.9754          | 0.7475   | 0.055           | 0.1075          |
| No log        | 58.96 | 236  | 0.9793          | 0.7425   | 0.055           | 0.1075          |
| No log        | 59.96 | 240  | 0.9813          | 0.7425   | 0.055           | 0.1075          |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_6_gate", "results": []}]}
transformers pytorch layoutlmv3 text-classification generated_from_trainer license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us