--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_rvl_cdip_100_examples_per_class_simkd_CEKD_t1_aNone results: [] --- # 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