--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_tobacco3482_kd_CEKD_t1.5_a0.9 results: [] --- # dit-tiny_tobacco3482_kd_CEKD_t1.5_a0.9 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: 2.3286 - Accuracy: 0.18 - Brier Loss: 0.8742 - Nll: 6.7213 - F1 Micro: 0.18 - F1 Macro: 0.0306 - Ece: 0.2558 - Aurc: 0.8491 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - 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 | 3 | 2.4683 | 0.145 | 0.8999 | 10.1538 | 0.145 | 0.0253 | 0.2220 | 0.8466 | | No log | 1.96 | 6 | 2.4396 | 0.145 | 0.8947 | 10.5704 | 0.145 | 0.0253 | 0.2237 | 0.8463 | | No log | 2.96 | 9 | 2.3985 | 0.145 | 0.8869 | 8.5511 | 0.145 | 0.0451 | 0.2116 | 0.8036 | | No log | 3.96 | 12 | 2.3677 | 0.21 | 0.8810 | 6.5446 | 0.2100 | 0.0611 | 0.2566 | 0.8335 | | No log | 4.96 | 15 | 2.3517 | 0.155 | 0.8780 | 6.8400 | 0.155 | 0.0279 | 0.2309 | 0.8894 | | No log | 5.96 | 18 | 2.3450 | 0.18 | 0.8771 | 8.1897 | 0.18 | 0.0313 | 0.2495 | 0.8531 | | No log | 6.96 | 21 | 2.3407 | 0.18 | 0.8767 | 7.3073 | 0.18 | 0.0306 | 0.2551 | 0.8513 | | No log | 7.96 | 24 | 2.3371 | 0.18 | 0.8763 | 6.9328 | 0.18 | 0.0306 | 0.2501 | 0.8520 | | No log | 8.96 | 27 | 2.3337 | 0.18 | 0.8757 | 6.8828 | 0.18 | 0.0306 | 0.2507 | 0.8525 | | No log | 9.96 | 30 | 2.3321 | 0.18 | 0.8753 | 6.8682 | 0.18 | 0.0306 | 0.2508 | 0.8524 | | No log | 10.96 | 33 | 2.3312 | 0.18 | 0.8751 | 6.7981 | 0.18 | 0.0306 | 0.2462 | 0.8521 | | No log | 11.96 | 36 | 2.3309 | 0.18 | 0.8749 | 6.7375 | 0.18 | 0.0306 | 0.2531 | 0.8520 | | No log | 12.96 | 39 | 2.3307 | 0.18 | 0.8748 | 6.7235 | 0.18 | 0.0306 | 0.2524 | 0.8518 | | No log | 13.96 | 42 | 2.3304 | 0.18 | 0.8747 | 6.7200 | 0.18 | 0.0306 | 0.2482 | 0.8514 | | No log | 14.96 | 45 | 2.3301 | 0.18 | 0.8746 | 6.7201 | 0.18 | 0.0306 | 0.2410 | 0.8509 | | No log | 15.96 | 48 | 2.3298 | 0.18 | 0.8746 | 6.7182 | 0.18 | 0.0306 | 0.2449 | 0.8505 | | No log | 16.96 | 51 | 2.3295 | 0.18 | 0.8745 | 6.7211 | 0.18 | 0.0306 | 0.2412 | 0.8500 | | No log | 17.96 | 54 | 2.3297 | 0.18 | 0.8745 | 6.7201 | 0.18 | 0.0306 | 0.2449 | 0.8496 | | No log | 18.96 | 57 | 2.3296 | 0.18 | 0.8745 | 6.7216 | 0.18 | 0.0306 | 0.2392 | 0.8494 | | No log | 19.96 | 60 | 2.3292 | 0.18 | 0.8744 | 6.7214 | 0.18 | 0.0306 | 0.2371 | 0.8494 | | No log | 20.96 | 63 | 2.3290 | 0.18 | 0.8744 | 6.7222 | 0.18 | 0.0306 | 0.2371 | 0.8493 | | No log | 21.96 | 66 | 2.3288 | 0.18 | 0.8743 | 6.7227 | 0.18 | 0.0306 | 0.2408 | 0.8494 | | No log | 22.96 | 69 | 2.3286 | 0.18 | 0.8743 | 6.7223 | 0.18 | 0.0306 | 0.2558 | 0.8490 | | No log | 23.96 | 72 | 2.3286 | 0.18 | 0.8743 | 6.7218 | 0.18 | 0.0306 | 0.2558 | 0.8491 | | No log | 24.96 | 75 | 2.3286 | 0.18 | 0.8742 | 6.7213 | 0.18 | 0.0306 | 0.2558 | 0.8491 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2