--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_tobacco3482_kd_CEKD_t2.5_a0.7 results: [] --- # dit-tiny_tobacco3482_kd_CEKD_t2.5_a0.7 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: 3.2510 - Accuracy: 0.18 - Brier Loss: 0.8767 - Nll: 6.8039 - F1 Micro: 0.18 - F1 Macro: 0.0306 - Ece: 0.2513 - Aurc: 0.8508 ## 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 | 3.4586 | 0.145 | 0.8999 | 10.1587 | 0.145 | 0.0253 | 0.2221 | 0.8467 | | No log | 1.96 | 6 | 3.4232 | 0.145 | 0.8946 | 10.5824 | 0.145 | 0.0253 | 0.2242 | 0.8475 | | No log | 2.96 | 9 | 3.3704 | 0.16 | 0.8867 | 8.6135 | 0.16 | 0.0503 | 0.2171 | 0.8440 | | No log | 3.96 | 12 | 3.3273 | 0.155 | 0.8807 | 6.5471 | 0.155 | 0.0274 | 0.2248 | 0.8831 | | No log | 4.96 | 15 | 3.3006 | 0.155 | 0.8779 | 6.8045 | 0.155 | 0.0271 | 0.2331 | 0.8918 | | No log | 5.96 | 18 | 3.2856 | 0.16 | 0.8773 | 8.2046 | 0.16 | 0.0329 | 0.2361 | 0.8956 | | No log | 6.96 | 21 | 3.2758 | 0.18 | 0.8774 | 8.0738 | 0.18 | 0.0308 | 0.2561 | 0.8544 | | No log | 7.96 | 24 | 3.2688 | 0.18 | 0.8778 | 7.1046 | 0.18 | 0.0308 | 0.2647 | 0.8524 | | No log | 8.96 | 27 | 3.2630 | 0.18 | 0.8778 | 6.9910 | 0.18 | 0.0306 | 0.2591 | 0.8530 | | No log | 9.96 | 30 | 3.2597 | 0.18 | 0.8778 | 6.9680 | 0.18 | 0.0306 | 0.2736 | 0.8538 | | No log | 10.96 | 33 | 3.2573 | 0.18 | 0.8776 | 6.9547 | 0.18 | 0.0306 | 0.2698 | 0.8536 | | No log | 11.96 | 36 | 3.2557 | 0.18 | 0.8775 | 6.9491 | 0.18 | 0.0306 | 0.2653 | 0.8533 | | No log | 12.96 | 39 | 3.2546 | 0.18 | 0.8773 | 6.8987 | 0.18 | 0.0306 | 0.2606 | 0.8526 | | No log | 13.96 | 42 | 3.2536 | 0.18 | 0.8771 | 6.8204 | 0.18 | 0.0306 | 0.2601 | 0.8523 | | No log | 14.96 | 45 | 3.2528 | 0.18 | 0.8771 | 6.8141 | 0.18 | 0.0306 | 0.2521 | 0.8519 | | No log | 15.96 | 48 | 3.2522 | 0.18 | 0.8769 | 6.8074 | 0.18 | 0.0306 | 0.2606 | 0.8517 | | No log | 16.96 | 51 | 3.2519 | 0.18 | 0.8769 | 6.8077 | 0.18 | 0.0306 | 0.2607 | 0.8515 | | No log | 17.96 | 54 | 3.2520 | 0.18 | 0.8769 | 6.8050 | 0.18 | 0.0306 | 0.2561 | 0.8510 | | No log | 18.96 | 57 | 3.2520 | 0.18 | 0.8769 | 6.8057 | 0.18 | 0.0306 | 0.2519 | 0.8509 | | No log | 19.96 | 60 | 3.2515 | 0.18 | 0.8768 | 6.8046 | 0.18 | 0.0306 | 0.2556 | 0.8507 | | No log | 20.96 | 63 | 3.2514 | 0.18 | 0.8768 | 6.8048 | 0.18 | 0.0306 | 0.2515 | 0.8506 | | No log | 21.96 | 66 | 3.2512 | 0.18 | 0.8767 | 6.8048 | 0.18 | 0.0306 | 0.2556 | 0.8508 | | No log | 22.96 | 69 | 3.2510 | 0.18 | 0.8767 | 6.8045 | 0.18 | 0.0306 | 0.2513 | 0.8509 | | No log | 23.96 | 72 | 3.2510 | 0.18 | 0.8767 | 6.8043 | 0.18 | 0.0306 | 0.2513 | 0.8508 | | No log | 24.96 | 75 | 3.2510 | 0.18 | 0.8767 | 6.8039 | 0.18 | 0.0306 | 0.2513 | 0.8508 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2