--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-small_rvl_cdip_100_examples_per_class_simkd_CEKD_t1_aNone results: [] --- # 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