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# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_8_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: 1.0199
- Accuracy: 0.7575
- Exit 0 Accuracy: 0.06
- Exit 1 Accuracy: 0.12

## 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    | 2.7540          | 0.09     | 0.0425          | 0.0625          |
| No log        | 1.96  | 8    | 2.7063          | 0.1525   | 0.045           | 0.0625          |
| No log        | 2.96  | 12   | 2.6279          | 0.1775   | 0.05            | 0.0625          |
| No log        | 3.96  | 16   | 2.5448          | 0.2025   | 0.0525          | 0.0625          |
| No log        | 4.96  | 20   | 2.4829          | 0.225    | 0.05            | 0.0625          |
| No log        | 5.96  | 24   | 2.3776          | 0.285    | 0.0525          | 0.0625          |
| No log        | 6.96  | 28   | 2.2785          | 0.33     | 0.055           | 0.0625          |
| No log        | 7.96  | 32   | 2.1809          | 0.3575   | 0.055           | 0.0625          |
| No log        | 8.96  | 36   | 2.0830          | 0.3875   | 0.055           | 0.085           |
| No log        | 9.96  | 40   | 1.9854          | 0.4275   | 0.055           | 0.07            |
| No log        | 10.96 | 44   | 1.8839          | 0.485    | 0.055           | 0.07            |
| No log        | 11.96 | 48   | 1.7375          | 0.545    | 0.055           | 0.0775          |
| No log        | 12.96 | 52   | 1.6524          | 0.5725   | 0.0575          | 0.095           |
| No log        | 13.96 | 56   | 1.5703          | 0.565    | 0.0575          | 0.1175          |
| No log        | 14.96 | 60   | 1.4606          | 0.6175   | 0.0575          | 0.1175          |
| No log        | 15.96 | 64   | 1.3988          | 0.6525   | 0.055           | 0.1175          |
| No log        | 16.96 | 68   | 1.3036          | 0.645    | 0.055           | 0.1175          |
| No log        | 17.96 | 72   | 1.2528          | 0.6875   | 0.0575          | 0.12            |
| No log        | 18.96 | 76   | 1.2113          | 0.69     | 0.0575          | 0.12            |
| No log        | 19.96 | 80   | 1.1515          | 0.6975   | 0.0575          | 0.1175          |
| No log        | 20.96 | 84   | 1.1117          | 0.71     | 0.0575          | 0.1175          |
| No log        | 21.96 | 88   | 1.0811          | 0.7075   | 0.0575          | 0.1175          |
| No log        | 22.96 | 92   | 1.0649          | 0.7075   | 0.0575          | 0.1175          |
| No log        | 23.96 | 96   | 1.0342          | 0.7275   | 0.0575          | 0.1175          |
| No log        | 24.96 | 100  | 0.9994          | 0.7275   | 0.0575          | 0.1175          |
| No log        | 25.96 | 104  | 1.0332          | 0.7275   | 0.0575          | 0.1175          |
| No log        | 26.96 | 108  | 0.9696          | 0.7325   | 0.06            | 0.1175          |
| No log        | 27.96 | 112  | 0.9957          | 0.7325   | 0.06            | 0.1175          |
| No log        | 28.96 | 116  | 0.9794          | 0.7325   | 0.06            | 0.1175          |
| No log        | 29.96 | 120  | 0.9602          | 0.735    | 0.06            | 0.1175          |
| No log        | 30.96 | 124  | 0.9900          | 0.75     | 0.06            | 0.12            |
| No log        | 31.96 | 128  | 0.9649          | 0.74     | 0.06            | 0.1175          |
| No log        | 32.96 | 132  | 0.9935          | 0.7325   | 0.06            | 0.12            |
| No log        | 33.96 | 136  | 0.9637          | 0.7475   | 0.06            | 0.12            |
| No log        | 34.96 | 140  | 0.9993          | 0.7325   | 0.06            | 0.12            |
| No log        | 35.96 | 144  | 0.9841          | 0.7375   | 0.06            | 0.1225          |
| No log        | 36.96 | 148  | 0.9719          | 0.7475   | 0.06            | 0.1225          |
| No log        | 37.96 | 152  | 0.9901          | 0.745    | 0.06            | 0.125           |
| No log        | 38.96 | 156  | 0.9692          | 0.7475   | 0.06            | 0.115           |
| No log        | 39.96 | 160  | 0.9850          | 0.7425   | 0.06            | 0.1025          |
| No log        | 40.96 | 164  | 0.9876          | 0.75     | 0.06            | 0.1             |
| No log        | 41.96 | 168  | 0.9867          | 0.7525   | 0.06            | 0.095           |
| No log        | 42.96 | 172  | 1.0042          | 0.7475   | 0.06            | 0.085           |
| No log        | 43.96 | 176  | 1.0099          | 0.735    | 0.06            | 0.1025          |
| No log        | 44.96 | 180  | 0.9988          | 0.755    | 0.06            | 0.1025          |
| No log        | 45.96 | 184  | 1.0033          | 0.7475   | 0.06            | 0.1025          |
| No log        | 46.96 | 188  | 1.0105          | 0.7475   | 0.06            | 0.105           |
| No log        | 47.96 | 192  | 1.0103          | 0.75     | 0.06            | 0.1025          |
| No log        | 48.96 | 196  | 1.0097          | 0.7575   | 0.06            | 0.11            |
| No log        | 49.96 | 200  | 0.9975          | 0.755    | 0.06            | 0.11            |
| No log        | 50.96 | 204  | 1.0095          | 0.76     | 0.06            | 0.115           |
| No log        | 51.96 | 208  | 1.0203          | 0.7575   | 0.06            | 0.1275          |
| No log        | 52.96 | 212  | 1.0304          | 0.745    | 0.06            | 0.1225          |
| No log        | 53.96 | 216  | 1.0286          | 0.75     | 0.06            | 0.125           |
| No log        | 54.96 | 220  | 1.0193          | 0.7575   | 0.06            | 0.1275          |
| No log        | 55.96 | 224  | 1.0138          | 0.7575   | 0.06            | 0.13            |
| No log        | 56.96 | 228  | 1.0168          | 0.76     | 0.06            | 0.1225          |
| No log        | 57.96 | 232  | 1.0196          | 0.76     | 0.06            | 0.12            |
| No log        | 58.96 | 236  | 1.0203          | 0.7575   | 0.06            | 0.12            |
| No log        | 59.96 | 240  | 1.0199          | 0.7575   | 0.06            | 0.12            |


### 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_8_gate", "results": []}]}
transformers pytorch layoutlmv3 text-classification generated_from_trainer license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us