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# dit-small_rvl_cdip_100_examples_per_class_simkd_CEKD_t1_aNone

This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1481
- Accuracy: 0.08
- Brier Loss: 0.9369
- Nll: 9.2883
- F1 Micro: 0.08
- F1 Macro: 0.0357
- Ece: 0.1153
- Aurc: 0.8531

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll    | F1 Micro | F1 Macro | Ece    | Aurc   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log        | 0.96  | 12   | 0.1528          | 0.0625   | 0.9377     | 9.9656 | 0.0625   | 0.0074   | 0.1025 | 0.9319 |
| No log        | 1.96  | 24   | 0.1507          | 0.06     | 0.9377     | 9.9434 | 0.06     | 0.0074   | 0.1036 | 0.9537 |
| No log        | 2.96  | 36   | 0.1500          | 0.0625   | 0.9376     | 8.6216 | 0.0625   | 0.0074   | 0.1019 | 0.9383 |
| No log        | 3.96  | 48   | 0.1498          | 0.0625   | 0.9376     | 9.2776 | 0.0625   | 0.0074   | 0.1032 | 0.9438 |
| No log        | 4.96  | 60   | 0.1496          | 0.0625   | 0.9375     | 9.3105 | 0.0625   | 0.0074   | 0.1017 | 0.9421 |
| No log        | 5.96  | 72   | 0.1495          | 0.0625   | 0.9375     | 9.7276 | 0.0625   | 0.0074   | 0.1029 | 0.9380 |
| No log        | 6.96  | 84   | 0.1494          | 0.0625   | 0.9374     | 9.6348 | 0.0625   | 0.0074   | 0.1017 | 0.9347 |
| No log        | 7.96  | 96   | 0.1493          | 0.0625   | 0.9374     | 9.6145 | 0.0625   | 0.0074   | 0.1008 | 0.9359 |
| No log        | 8.96  | 108  | 0.1492          | 0.0625   | 0.9374     | 9.5748 | 0.0625   | 0.0074   | 0.1019 | 0.9371 |
| No log        | 9.96  | 120  | 0.1491          | 0.0625   | 0.9373     | 9.5551 | 0.0625   | 0.0074   | 0.1005 | 0.9372 |
| No log        | 10.96 | 132  | 0.1490          | 0.065    | 0.9373     | 9.5267 | 0.065    | 0.0122   | 0.1047 | 0.9315 |
| No log        | 11.96 | 144  | 0.1489          | 0.065    | 0.9373     | 9.5165 | 0.065    | 0.0122   | 0.1043 | 0.9284 |
| No log        | 12.96 | 156  | 0.1488          | 0.065    | 0.9372     | 9.5162 | 0.065    | 0.0123   | 0.1068 | 0.9302 |
| No log        | 13.96 | 168  | 0.1488          | 0.07     | 0.9372     | 9.5139 | 0.07     | 0.0213   | 0.1070 | 0.9275 |
| No log        | 14.96 | 180  | 0.1487          | 0.0725   | 0.9371     | 9.4579 | 0.0725   | 0.0253   | 0.1095 | 0.9174 |
| No log        | 15.96 | 192  | 0.1486          | 0.075    | 0.9371     | 9.3950 | 0.075    | 0.0286   | 0.1106 | 0.9161 |
| No log        | 16.96 | 204  | 0.1485          | 0.075    | 0.9371     | 9.3347 | 0.075    | 0.0280   | 0.1055 | 0.9014 |
| No log        | 17.96 | 216  | 0.1484          | 0.0775   | 0.9370     | 9.3157 | 0.0775   | 0.0315   | 0.1089 | 0.8695 |
| No log        | 18.96 | 228  | 0.1483          | 0.08     | 0.9370     | 9.3125 | 0.08     | 0.0362   | 0.1133 | 0.8526 |
| No log        | 19.96 | 240  | 0.1483          | 0.08     | 0.9370     | 9.2915 | 0.08     | 0.0360   | 0.1113 | 0.8554 |
| No log        | 20.96 | 252  | 0.1482          | 0.0775   | 0.9370     | 9.2937 | 0.0775   | 0.0374   | 0.1118 | 0.8475 |
| No log        | 21.96 | 264  | 0.1482          | 0.08     | 0.9369     | 9.2903 | 0.08     | 0.0357   | 0.1167 | 0.8526 |
| No log        | 22.96 | 276  | 0.1482          | 0.08     | 0.9369     | 9.2888 | 0.08     | 0.0357   | 0.1099 | 0.8540 |
| No log        | 23.96 | 288  | 0.1481          | 0.08     | 0.9369     | 9.2877 | 0.08     | 0.0357   | 0.1126 | 0.8531 |
| No log        | 24.96 | 300  | 0.1481          | 0.08     | 0.9369     | 9.2883 | 0.08     | 0.0357   | 0.1153 | 0.8531 |


### Framework versions

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

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