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# dit_base

This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the davanstrien/leicester_loaded_annotations dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4527
- Accuracy: 0.8190

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- 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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.89  | 6    | 1.7452          | 0.4095   |
| 1.8958        | 1.89  | 12   | 1.6185          | 0.4286   |
| 1.8958        | 2.89  | 18   | 1.4731          | 0.4857   |
| 1.8466        | 3.89  | 24   | 1.3459          | 0.5524   |
| 1.445         | 4.89  | 30   | 1.1766          | 0.5810   |
| 1.445         | 5.89  | 36   | 1.0902          | 0.6381   |
| 1.2077        | 6.89  | 42   | 0.9331          | 0.6762   |
| 1.2077        | 7.89  | 48   | 0.8431          | 0.6762   |
| 1.0254        | 8.89  | 54   | 0.8657          | 0.6857   |
| 0.8275        | 9.89  | 60   | 0.6801          | 0.7429   |
| 0.8275        | 10.89 | 66   | 0.6699          | 0.7810   |
| 0.8063        | 11.89 | 72   | 0.6296          | 0.7524   |
| 0.8063        | 12.89 | 78   | 0.5498          | 0.7905   |
| 0.7127        | 13.89 | 84   | 0.4974          | 0.8381   |
| 0.6356        | 14.89 | 90   | 0.6715          | 0.7619   |
| 0.6356        | 15.89 | 96   | 0.4602          | 0.8095   |
| 0.6438        | 16.89 | 102  | 0.4886          | 0.8095   |
| 0.6438        | 17.89 | 108  | 0.4332          | 0.8      |
| 0.5329        | 18.89 | 114  | 0.4197          | 0.8095   |
| 0.4932        | 19.89 | 120  | 0.4168          | 0.8190   |
| 0.4932        | 20.89 | 126  | 0.4691          | 0.8      |
| 0.4861        | 21.89 | 132  | 0.4263          | 0.8476   |
| 0.4861        | 22.89 | 138  | 0.4464          | 0.8190   |
| 0.4935        | 23.89 | 144  | 0.4857          | 0.7905   |
| 0.433         | 24.89 | 150  | 0.4873          | 0.7810   |
| 0.433         | 25.89 | 156  | 0.4641          | 0.8095   |
| 0.4289        | 26.89 | 162  | 0.5316          | 0.8      |
| 0.4289        | 27.89 | 168  | 0.3389          | 0.8571   |
| 0.4204        | 28.89 | 174  | 0.4272          | 0.8      |
| 0.3668        | 29.89 | 180  | 0.3493          | 0.8667   |
| 0.3668        | 30.89 | 186  | 0.3861          | 0.8571   |
| 0.4101        | 31.89 | 192  | 0.4216          | 0.8381   |
| 0.4101        | 32.89 | 198  | 0.4258          | 0.8190   |
| 0.3614        | 33.89 | 204  | 0.4409          | 0.8571   |
| 0.3267        | 34.89 | 210  | 0.4475          | 0.8190   |
| 0.3267        | 35.89 | 216  | 0.4316          | 0.8190   |
| 0.3423        | 36.89 | 222  | 0.4095          | 0.8381   |
| 0.3423        | 37.89 | 228  | 0.4671          | 0.8286   |
| 0.3325        | 38.89 | 234  | 0.3994          | 0.8286   |
| 0.3326        | 39.89 | 240  | 0.5004          | 0.8190   |
| 0.3326        | 40.89 | 246  | 0.4103          | 0.8381   |
| 0.2964        | 41.89 | 252  | 0.4469          | 0.8286   |
| 0.2964        | 42.89 | 258  | 0.4774          | 0.8286   |
| 0.3435        | 43.89 | 264  | 0.3843          | 0.8381   |
| 0.3146        | 44.89 | 270  | 0.3710          | 0.8667   |
| 0.3146        | 45.89 | 276  | 0.3392          | 0.8667   |
| 0.3168        | 46.89 | 282  | 0.3597          | 0.8667   |
| 0.3168        | 47.89 | 288  | 0.4143          | 0.8381   |
| 0.3081        | 48.89 | 294  | 0.3579          | 0.8571   |
| 0.3103        | 49.89 | 300  | 0.4527          | 0.8190   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.1

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