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# EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-12-04_txt_vis_concat_enc_1_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: 0.9706
- Accuracy: 0.75
- Exit 0 Accuracy: 0.0575
- Exit 1 Accuracy: 0.055

## 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     | 0.0475          | 0.0625          |
| No log        | 1.96  | 8    | 2.7048          | 0.1525   | 0.055           | 0.0625          |
| No log        | 2.96  | 12   | 2.6225          | 0.18     | 0.0525          | 0.0625          |
| No log        | 3.96  | 16   | 2.5506          | 0.1925   | 0.055           | 0.0675          |
| No log        | 4.96  | 20   | 2.5032          | 0.22     | 0.05            | 0.0575          |
| No log        | 5.96  | 24   | 2.4566          | 0.235    | 0.05            | 0.055           |
| No log        | 6.96  | 28   | 2.3653          | 0.2975   | 0.05            | 0.0475          |
| No log        | 7.96  | 32   | 2.2942          | 0.3375   | 0.0525          | 0.0525          |
| No log        | 8.96  | 36   | 2.2127          | 0.35     | 0.0525          | 0.0425          |
| No log        | 9.96  | 40   | 2.1131          | 0.3825   | 0.0525          | 0.0475          |
| No log        | 10.96 | 44   | 2.0597          | 0.3875   | 0.0525          | 0.045           |
| No log        | 11.96 | 48   | 1.9519          | 0.4325   | 0.0525          | 0.0425          |
| No log        | 12.96 | 52   | 1.9216          | 0.4475   | 0.0525          | 0.045           |
| No log        | 13.96 | 56   | 1.7836          | 0.4975   | 0.0525          | 0.0425          |
| No log        | 14.96 | 60   | 1.7549          | 0.4975   | 0.055           | 0.0375          |
| No log        | 15.96 | 64   | 1.6272          | 0.545    | 0.055           | 0.04            |
| No log        | 16.96 | 68   | 1.5730          | 0.54     | 0.055           | 0.0425          |
| No log        | 17.96 | 72   | 1.5355          | 0.565    | 0.055           | 0.04            |
| No log        | 18.96 | 76   | 1.4481          | 0.5875   | 0.055           | 0.0375          |
| No log        | 19.96 | 80   | 1.3854          | 0.6125   | 0.055           | 0.04            |
| No log        | 20.96 | 84   | 1.3449          | 0.6375   | 0.055           | 0.0325          |
| No log        | 21.96 | 88   | 1.2869          | 0.6425   | 0.055           | 0.0275          |
| No log        | 22.96 | 92   | 1.2292          | 0.6575   | 0.0575          | 0.0325          |
| No log        | 23.96 | 96   | 1.2049          | 0.6625   | 0.0575          | 0.025           |
| No log        | 24.96 | 100  | 1.1601          | 0.6725   | 0.0575          | 0.035           |
| No log        | 25.96 | 104  | 1.1601          | 0.7025   | 0.0575          | 0.0425          |
| No log        | 26.96 | 108  | 1.1110          | 0.7      | 0.0575          | 0.0375          |
| No log        | 27.96 | 112  | 1.0922          | 0.715    | 0.06            | 0.0425          |
| No log        | 28.96 | 116  | 1.0862          | 0.7175   | 0.06            | 0.045           |
| No log        | 29.96 | 120  | 1.0855          | 0.7025   | 0.06            | 0.045           |
| No log        | 30.96 | 124  | 1.0540          | 0.715    | 0.06            | 0.0375          |
| No log        | 31.96 | 128  | 1.0366          | 0.7175   | 0.06            | 0.045           |
| No log        | 32.96 | 132  | 1.0407          | 0.72     | 0.06            | 0.045           |
| No log        | 33.96 | 136  | 1.0148          | 0.73     | 0.06            | 0.0425          |
| No log        | 34.96 | 140  | 1.0144          | 0.7275   | 0.06            | 0.05            |
| No log        | 35.96 | 144  | 1.0054          | 0.7275   | 0.0575          | 0.0525          |
| No log        | 36.96 | 148  | 1.0055          | 0.735    | 0.0575          | 0.055           |
| No log        | 37.96 | 152  | 1.0171          | 0.7375   | 0.0575          | 0.06            |
| No log        | 38.96 | 156  | 0.9861          | 0.745    | 0.0575          | 0.06            |
| No log        | 39.96 | 160  | 0.9886          | 0.74     | 0.0575          | 0.06            |
| No log        | 40.96 | 164  | 0.9734          | 0.74     | 0.0575          | 0.06            |
| No log        | 41.96 | 168  | 0.9816          | 0.745    | 0.0575          | 0.0575          |
| No log        | 42.96 | 172  | 0.9672          | 0.735    | 0.0575          | 0.055           |
| No log        | 43.96 | 176  | 0.9909          | 0.7375   | 0.0575          | 0.0575          |
| No log        | 44.96 | 180  | 0.9831          | 0.75     | 0.0575          | 0.055           |
| No log        | 45.96 | 184  | 0.9673          | 0.7375   | 0.0575          | 0.055           |
| No log        | 46.96 | 188  | 0.9762          | 0.75     | 0.0575          | 0.0525          |
| No log        | 47.96 | 192  | 0.9666          | 0.745    | 0.0575          | 0.0525          |
| No log        | 48.96 | 196  | 0.9714          | 0.7525   | 0.0575          | 0.0575          |
| No log        | 49.96 | 200  | 0.9661          | 0.7475   | 0.0575          | 0.0575          |
| No log        | 50.96 | 204  | 0.9707          | 0.745    | 0.0575          | 0.0575          |
| No log        | 51.96 | 208  | 0.9830          | 0.7475   | 0.0575          | 0.0575          |
| No log        | 52.96 | 212  | 0.9802          | 0.75     | 0.0575          | 0.05            |
| No log        | 53.96 | 216  | 0.9683          | 0.75     | 0.0575          | 0.055           |
| No log        | 54.96 | 220  | 0.9797          | 0.7475   | 0.0575          | 0.055           |
| No log        | 55.96 | 224  | 0.9874          | 0.7525   | 0.0575          | 0.055           |
| No log        | 56.96 | 228  | 0.9824          | 0.75     | 0.0575          | 0.055           |
| No log        | 57.96 | 232  | 0.9755          | 0.745    | 0.0575          | 0.055           |
| No log        | 58.96 | 236  | 0.9719          | 0.7475   | 0.0575          | 0.055           |
| No log        | 59.96 | 240  | 0.9706          | 0.75     | 0.0575          | 0.055           |


### 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_1_gate", "results": []}]}
transformers pytorch layoutlmv3 text-classification generated_from_trainer license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us