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# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_7_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: 0.9969
- Accuracy: 0.7375
- Exit 0 Accuracy: 0.0575
- Exit 1 Accuracy: 0.135

## 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    | 2.7554          | 0.09     | 0.04            | 0.07            |
| No log        | 1.96  | 8    | 2.7058          | 0.15     | 0.0475          | 0.0625          |
| No log        | 2.96  | 12   | 2.6226          | 0.1675   | 0.05            | 0.0625          |
| No log        | 3.96  | 16   | 2.5435          | 0.19     | 0.0525          | 0.0625          |
| No log        | 4.96  | 20   | 2.4855          | 0.2075   | 0.0525          | 0.0625          |
| No log        | 5.96  | 24   | 2.3833          | 0.2925   | 0.0525          | 0.065           |
| No log        | 6.96  | 28   | 2.2843          | 0.3375   | 0.0525          | 0.0725          |
| No log        | 7.96  | 32   | 2.1867          | 0.3525   | 0.0525          | 0.0875          |
| No log        | 8.96  | 36   | 2.0982          | 0.3775   | 0.0525          | 0.09            |
| No log        | 9.96  | 40   | 1.9870          | 0.4375   | 0.055           | 0.09            |
| No log        | 10.96 | 44   | 1.8947          | 0.4575   | 0.055           | 0.0725          |
| No log        | 11.96 | 48   | 1.7869          | 0.495    | 0.055           | 0.065           |
| No log        | 12.96 | 52   | 1.7034          | 0.54     | 0.0575          | 0.0575          |
| No log        | 13.96 | 56   | 1.6100          | 0.57     | 0.0575          | 0.055           |
| No log        | 14.96 | 60   | 1.5372          | 0.5675   | 0.0575          | 0.065           |
| No log        | 15.96 | 64   | 1.4592          | 0.61     | 0.0575          | 0.07            |
| No log        | 16.96 | 68   | 1.4024          | 0.6125   | 0.0575          | 0.08            |
| No log        | 17.96 | 72   | 1.3378          | 0.64     | 0.0575          | 0.075           |
| No log        | 18.96 | 76   | 1.2934          | 0.66     | 0.0575          | 0.085           |
| No log        | 19.96 | 80   | 1.2202          | 0.655    | 0.0575          | 0.09            |
| No log        | 20.96 | 84   | 1.1826          | 0.6725   | 0.055           | 0.085           |
| No log        | 21.96 | 88   | 1.1325          | 0.6925   | 0.055           | 0.09            |
| No log        | 22.96 | 92   | 1.0960          | 0.7275   | 0.055           | 0.0875          |
| No log        | 23.96 | 96   | 1.0852          | 0.69     | 0.055           | 0.0925          |
| No log        | 24.96 | 100  | 1.0503          | 0.7075   | 0.055           | 0.0875          |
| No log        | 25.96 | 104  | 1.0417          | 0.705    | 0.055           | 0.0975          |
| No log        | 26.96 | 108  | 1.0174          | 0.71     | 0.055           | 0.095           |
| No log        | 27.96 | 112  | 0.9902          | 0.715    | 0.055           | 0.1             |
| No log        | 28.96 | 116  | 1.0088          | 0.6975   | 0.055           | 0.1025          |
| No log        | 29.96 | 120  | 0.9992          | 0.7125   | 0.055           | 0.1075          |
| No log        | 30.96 | 124  | 0.9688          | 0.72     | 0.0575          | 0.1125          |
| No log        | 31.96 | 128  | 0.9745          | 0.7275   | 0.0575          | 0.11            |
| No log        | 32.96 | 132  | 0.9688          | 0.7175   | 0.055           | 0.1125          |
| No log        | 33.96 | 136  | 0.9720          | 0.7225   | 0.055           | 0.11            |
| No log        | 34.96 | 140  | 0.9515          | 0.7375   | 0.055           | 0.1125          |
| No log        | 35.96 | 144  | 0.9717          | 0.73     | 0.055           | 0.115           |
| No log        | 36.96 | 148  | 0.9686          | 0.7225   | 0.055           | 0.1125          |
| No log        | 37.96 | 152  | 0.9646          | 0.7275   | 0.055           | 0.115           |
| No log        | 38.96 | 156  | 0.9522          | 0.7375   | 0.055           | 0.1225          |
| No log        | 39.96 | 160  | 0.9844          | 0.735    | 0.055           | 0.12            |
| No log        | 40.96 | 164  | 0.9815          | 0.745    | 0.055           | 0.1075          |
| No log        | 41.96 | 168  | 0.9866          | 0.7225   | 0.055           | 0.115           |
| No log        | 42.96 | 172  | 0.9835          | 0.7325   | 0.055           | 0.115           |
| No log        | 43.96 | 176  | 0.9902          | 0.74     | 0.055           | 0.11            |
| No log        | 44.96 | 180  | 0.9843          | 0.745    | 0.055           | 0.115           |
| No log        | 45.96 | 184  | 1.0099          | 0.725    | 0.055           | 0.1225          |
| No log        | 46.96 | 188  | 0.9917          | 0.7325   | 0.0575          | 0.125           |
| No log        | 47.96 | 192  | 0.9817          | 0.735    | 0.0575          | 0.13            |
| No log        | 48.96 | 196  | 1.0048          | 0.73     | 0.055           | 0.13            |
| No log        | 49.96 | 200  | 0.9973          | 0.7375   | 0.0575          | 0.1275          |
| No log        | 50.96 | 204  | 0.9899          | 0.7375   | 0.0575          | 0.1275          |
| No log        | 51.96 | 208  | 0.9913          | 0.7375   | 0.0575          | 0.1275          |
| No log        | 52.96 | 212  | 0.9975          | 0.735    | 0.0575          | 0.13            |
| No log        | 53.96 | 216  | 1.0028          | 0.7325   | 0.0575          | 0.13            |
| No log        | 54.96 | 220  | 0.9928          | 0.735    | 0.0575          | 0.1275          |
| No log        | 55.96 | 224  | 0.9866          | 0.735    | 0.0575          | 0.1325          |
| No log        | 56.96 | 228  | 0.9884          | 0.7375   | 0.0575          | 0.1275          |
| No log        | 57.96 | 232  | 0.9943          | 0.7425   | 0.0575          | 0.13            |
| No log        | 58.96 | 236  | 0.9967          | 0.74     | 0.0575          | 0.13            |
| No log        | 59.96 | 240  | 0.9969          | 0.7375   | 0.0575          | 0.135           |


### 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_7_gate", "results": []}]}
transformers pytorch layoutlmv3 text-classification generated_from_trainer license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us