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# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_5_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: 0.9668
- Accuracy: 0.7475
- Exit 0 Accuracy: 0.0575
- Exit 1 Accuracy: 0.0625

## 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     | 0.04            | 0.0625          |
| No log        | 1.96  | 8    | 2.7056          | 0.14     | 0.0475          | 0.065           |
| No log        | 2.96  | 12   | 2.6221          | 0.165    | 0.045           | 0.0575          |
| No log        | 3.96  | 16   | 2.5429          | 0.1925   | 0.045           | 0.0625          |
| No log        | 4.96  | 20   | 2.4773          | 0.225    | 0.0525          | 0.0625          |
| No log        | 5.96  | 24   | 2.3640          | 0.295    | 0.0525          | 0.0625          |
| No log        | 6.96  | 28   | 2.2786          | 0.3325   | 0.05            | 0.0625          |
| No log        | 7.96  | 32   | 2.1765          | 0.3675   | 0.0525          | 0.0625          |
| No log        | 8.96  | 36   | 2.0950          | 0.39     | 0.0525          | 0.0675          |
| No log        | 9.96  | 40   | 1.9789          | 0.4475   | 0.055           | 0.07            |
| No log        | 10.96 | 44   | 1.9028          | 0.455    | 0.055           | 0.07            |
| No log        | 11.96 | 48   | 1.7707          | 0.505    | 0.055           | 0.0725          |
| No log        | 12.96 | 52   | 1.7087          | 0.525    | 0.055           | 0.075           |
| No log        | 13.96 | 56   | 1.6296          | 0.555    | 0.055           | 0.0525          |
| No log        | 14.96 | 60   | 1.5540          | 0.575    | 0.0575          | 0.0575          |
| No log        | 15.96 | 64   | 1.4520          | 0.6275   | 0.055           | 0.0475          |
| No log        | 16.96 | 68   | 1.3974          | 0.63     | 0.055           | 0.0425          |
| No log        | 17.96 | 72   | 1.3386          | 0.655    | 0.055           | 0.0425          |
| No log        | 18.96 | 76   | 1.2821          | 0.665    | 0.055           | 0.04            |
| No log        | 19.96 | 80   | 1.2171          | 0.68     | 0.055           | 0.0425          |
| No log        | 20.96 | 84   | 1.1603          | 0.695    | 0.0525          | 0.05            |
| No log        | 21.96 | 88   | 1.1320          | 0.7025   | 0.0525          | 0.05            |
| No log        | 22.96 | 92   | 1.0933          | 0.7      | 0.0525          | 0.0425          |
| No log        | 23.96 | 96   | 1.0627          | 0.715    | 0.0525          | 0.0425          |
| No log        | 24.96 | 100  | 1.0525          | 0.72     | 0.0525          | 0.0575          |
| No log        | 25.96 | 104  | 1.0423          | 0.715    | 0.0525          | 0.06            |
| No log        | 26.96 | 108  | 1.0229          | 0.715    | 0.0525          | 0.06            |
| No log        | 27.96 | 112  | 0.9955          | 0.73     | 0.0525          | 0.0575          |
| No log        | 28.96 | 116  | 0.9720          | 0.73     | 0.055           | 0.06            |
| No log        | 29.96 | 120  | 0.9958          | 0.715    | 0.055           | 0.07            |
| No log        | 30.96 | 124  | 0.9662          | 0.74     | 0.055           | 0.07            |
| No log        | 31.96 | 128  | 0.9828          | 0.73     | 0.055           | 0.065           |
| No log        | 32.96 | 132  | 0.9726          | 0.73     | 0.055           | 0.065           |
| No log        | 33.96 | 136  | 0.9644          | 0.73     | 0.055           | 0.0625          |
| No log        | 34.96 | 140  | 0.9596          | 0.7375   | 0.055           | 0.0625          |
| No log        | 35.96 | 144  | 0.9471          | 0.7375   | 0.055           | 0.0675          |
| No log        | 36.96 | 148  | 0.9542          | 0.7375   | 0.055           | 0.0575          |
| No log        | 37.96 | 152  | 0.9740          | 0.72     | 0.0525          | 0.0625          |
| No log        | 38.96 | 156  | 0.9314          | 0.7525   | 0.0525          | 0.0625          |
| No log        | 39.96 | 160  | 0.9308          | 0.75     | 0.055           | 0.0625          |
| No log        | 40.96 | 164  | 0.9463          | 0.7475   | 0.0575          | 0.06            |
| No log        | 41.96 | 168  | 0.9369          | 0.75     | 0.0575          | 0.06            |
| No log        | 42.96 | 172  | 0.9585          | 0.735    | 0.0575          | 0.0525          |
| No log        | 43.96 | 176  | 0.9400          | 0.75     | 0.0575          | 0.06            |
| No log        | 44.96 | 180  | 0.9233          | 0.7625   | 0.0575          | 0.065           |
| No log        | 45.96 | 184  | 0.9533          | 0.75     | 0.0575          | 0.0725          |
| No log        | 46.96 | 188  | 0.9584          | 0.745    | 0.0575          | 0.065           |
| No log        | 47.96 | 192  | 0.9659          | 0.7375   | 0.0575          | 0.06            |
| No log        | 48.96 | 196  | 0.9480          | 0.7575   | 0.0575          | 0.0625          |
| No log        | 49.96 | 200  | 0.9712          | 0.745    | 0.0575          | 0.065           |
| No log        | 50.96 | 204  | 0.9800          | 0.7375   | 0.0575          | 0.0675          |
| No log        | 51.96 | 208  | 0.9495          | 0.75     | 0.0575          | 0.0675          |
| No log        | 52.96 | 212  | 0.9579          | 0.74     | 0.0575          | 0.0575          |
| No log        | 53.96 | 216  | 0.9777          | 0.7475   | 0.0575          | 0.0525          |
| No log        | 54.96 | 220  | 0.9731          | 0.7475   | 0.0575          | 0.05            |
| No log        | 55.96 | 224  | 0.9647          | 0.7475   | 0.0575          | 0.0525          |
| No log        | 56.96 | 228  | 0.9649          | 0.745    | 0.0575          | 0.0575          |
| No log        | 57.96 | 232  | 0.9666          | 0.7475   | 0.0575          | 0.065           |
| No log        | 58.96 | 236  | 0.9670          | 0.7475   | 0.0575          | 0.065           |
| No log        | 59.96 | 240  | 0.9668          | 0.7475   | 0.0575          | 0.0625          |


### 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_5_gate", "results": []}]}
transformers pytorch layoutlmv3 text-classification generated_from_trainer license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us