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# dit-tiny_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: 0.1502
- Accuracy: 0.0625
- Brier Loss: 0.9374
- Nll: 9.1398
- F1 Micro: 0.0625
- F1 Macro: 0.0074
- Ece: 0.1015
- Aurc: 0.9383

## 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   | 0.1540          | 0.0625   | 0.9376     | 8.5438 | 0.0625   | 0.0074   | 0.1043 | 0.9530 |
| No log        | 1.96  | 24   | 0.1519          | 0.0625   | 0.9376     | 8.2831 | 0.0625   | 0.0074   | 0.1008 | 0.9465 |
| No log        | 2.96  | 36   | 0.1512          | 0.0625   | 0.9375     | 8.4629 | 0.0625   | 0.0074   | 0.1028 | 0.9336 |
| No log        | 3.96  | 48   | 0.1510          | 0.0625   | 0.9375     | 8.6283 | 0.0625   | 0.0074   | 0.1027 | 0.9365 |
| No log        | 4.96  | 60   | 0.1509          | 0.0625   | 0.9375     | 8.5065 | 0.0625   | 0.0074   | 0.1030 | 0.9433 |
| No log        | 5.96  | 72   | 0.1508          | 0.0625   | 0.9375     | 8.4779 | 0.0625   | 0.0074   | 0.1017 | 0.9414 |
| No log        | 6.96  | 84   | 0.1507          | 0.0625   | 0.9375     | 8.5053 | 0.0625   | 0.0074   | 0.1045 | 0.9438 |
| No log        | 7.96  | 96   | 0.1507          | 0.0625   | 0.9375     | 8.7396 | 0.0625   | 0.0074   | 0.1032 | 0.9440 |
| No log        | 8.96  | 108  | 0.1506          | 0.0625   | 0.9375     | 8.6420 | 0.0625   | 0.0074   | 0.1031 | 0.9448 |
| No log        | 9.96  | 120  | 0.1506          | 0.0625   | 0.9375     | 8.8410 | 0.0625   | 0.0074   | 0.1045 | 0.9438 |
| No log        | 10.96 | 132  | 0.1506          | 0.0625   | 0.9374     | 8.9438 | 0.0625   | 0.0074   | 0.1042 | 0.9413 |
| No log        | 11.96 | 144  | 0.1505          | 0.0625   | 0.9374     | 8.9847 | 0.0625   | 0.0074   | 0.1032 | 0.9418 |
| No log        | 12.96 | 156  | 0.1505          | 0.0625   | 0.9374     | 9.0594 | 0.0625   | 0.0074   | 0.1031 | 0.9397 |
| No log        | 13.96 | 168  | 0.1504          | 0.0625   | 0.9374     | 9.0748 | 0.0625   | 0.0074   | 0.1045 | 0.9343 |
| No log        | 14.96 | 180  | 0.1504          | 0.0625   | 0.9374     | 9.0912 | 0.0625   | 0.0074   | 0.1018 | 0.9358 |
| No log        | 15.96 | 192  | 0.1504          | 0.0625   | 0.9374     | 9.0950 | 0.0625   | 0.0074   | 0.1032 | 0.9331 |
| No log        | 16.96 | 204  | 0.1503          | 0.0625   | 0.9374     | 9.2141 | 0.0625   | 0.0074   | 0.1015 | 0.9363 |
| No log        | 17.96 | 216  | 0.1503          | 0.0625   | 0.9374     | 9.0918 | 0.0625   | 0.0074   | 0.1046 | 0.9354 |
| No log        | 18.96 | 228  | 0.1503          | 0.0625   | 0.9374     | 9.1430 | 0.0625   | 0.0074   | 0.1018 | 0.9385 |
| No log        | 19.96 | 240  | 0.1503          | 0.0625   | 0.9374     | 9.2149 | 0.0625   | 0.0074   | 0.0991 | 0.9404 |
| No log        | 20.96 | 252  | 0.1503          | 0.0625   | 0.9374     | 9.0900 | 0.0625   | 0.0074   | 0.1043 | 0.9386 |
| No log        | 21.96 | 264  | 0.1503          | 0.0625   | 0.9374     | 9.1244 | 0.0625   | 0.0074   | 0.1060 | 0.9395 |
| No log        | 22.96 | 276  | 0.1503          | 0.0625   | 0.9374     | 9.1353 | 0.0625   | 0.0074   | 0.1005 | 0.9378 |
| No log        | 23.96 | 288  | 0.1502          | 0.0625   | 0.9374     | 9.2063 | 0.0625   | 0.0074   | 0.1032 | 0.9373 |
| No log        | 24.96 | 300  | 0.1502          | 0.0625   | 0.9374     | 9.1398 | 0.0625   | 0.0074   | 0.1015 | 0.9383 |


### Framework versions

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

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