--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_tobacco3482_kd_CEKD_t5.0_a0.9 results: [] --- # dit-tiny_tobacco3482_kd_CEKD_t5.0_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: 2.5147 - Accuracy: 0.18 - Brier Loss: 0.8746 - Nll: 6.7241 - F1 Micro: 0.18 - F1 Macro: 0.0306 - Ece: 0.2451 - Aurc: 0.8494 ## 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 | 2.6571 | 0.145 | 0.8999 | 10.1542 | 0.145 | 0.0253 | 0.2220 | 0.8466 | | No log | 1.96 | 6 | 2.6281 | 0.145 | 0.8947 | 10.5635 | 0.145 | 0.0253 | 0.2236 | 0.8461 | | No log | 2.96 | 9 | 2.5865 | 0.14 | 0.8870 | 8.5822 | 0.14 | 0.0433 | 0.2063 | 0.8040 | | No log | 3.96 | 12 | 2.5552 | 0.19 | 0.8811 | 6.5445 | 0.19 | 0.0552 | 0.2421 | 0.8576 | | No log | 4.96 | 15 | 2.5387 | 0.155 | 0.8782 | 7.1184 | 0.155 | 0.0277 | 0.2280 | 0.8892 | | No log | 5.96 | 18 | 2.5317 | 0.18 | 0.8774 | 8.7285 | 0.18 | 0.0319 | 0.2392 | 0.8538 | | No log | 6.96 | 21 | 2.5274 | 0.18 | 0.8770 | 8.2533 | 0.18 | 0.0306 | 0.2476 | 0.8514 | | No log | 7.96 | 24 | 2.5238 | 0.18 | 0.8767 | 6.9903 | 0.18 | 0.0306 | 0.2465 | 0.8523 | | No log | 8.96 | 27 | 2.5205 | 0.18 | 0.8762 | 6.9049 | 0.18 | 0.0306 | 0.2473 | 0.8528 | | No log | 9.96 | 30 | 2.5189 | 0.18 | 0.8758 | 6.8830 | 0.18 | 0.0306 | 0.2515 | 0.8526 | | No log | 10.96 | 33 | 2.5180 | 0.18 | 0.8756 | 6.8133 | 0.18 | 0.0306 | 0.2469 | 0.8522 | | No log | 11.96 | 36 | 2.5175 | 0.18 | 0.8754 | 6.7422 | 0.18 | 0.0306 | 0.2500 | 0.8519 | | No log | 12.96 | 39 | 2.5173 | 0.18 | 0.8753 | 6.5762 | 0.18 | 0.0306 | 0.2533 | 0.8515 | | No log | 13.96 | 42 | 2.5168 | 0.18 | 0.8751 | 6.5666 | 0.18 | 0.0306 | 0.2528 | 0.8516 | | No log | 14.96 | 45 | 2.5164 | 0.18 | 0.8750 | 6.7246 | 0.18 | 0.0306 | 0.2532 | 0.8512 | | No log | 15.96 | 48 | 2.5160 | 0.18 | 0.8750 | 6.7221 | 0.18 | 0.0306 | 0.2456 | 0.8507 | | No log | 16.96 | 51 | 2.5157 | 0.18 | 0.8749 | 6.7242 | 0.18 | 0.0306 | 0.2457 | 0.8507 | | No log | 17.96 | 54 | 2.5158 | 0.18 | 0.8749 | 6.7241 | 0.18 | 0.0306 | 0.2417 | 0.8503 | | No log | 18.96 | 57 | 2.5157 | 0.18 | 0.8749 | 6.7259 | 0.18 | 0.0306 | 0.2455 | 0.8503 | | No log | 19.96 | 60 | 2.5153 | 0.18 | 0.8748 | 6.7248 | 0.18 | 0.0306 | 0.2452 | 0.8495 | | No log | 20.96 | 63 | 2.5151 | 0.18 | 0.8748 | 6.7250 | 0.18 | 0.0306 | 0.2414 | 0.8494 | | No log | 21.96 | 66 | 2.5149 | 0.18 | 0.8747 | 6.7250 | 0.18 | 0.0306 | 0.2452 | 0.8495 | | No log | 22.96 | 69 | 2.5147 | 0.18 | 0.8747 | 6.7247 | 0.18 | 0.0306 | 0.2451 | 0.8495 | | No log | 23.96 | 72 | 2.5147 | 0.18 | 0.8747 | 6.7246 | 0.18 | 0.0306 | 0.2451 | 0.8495 | | No log | 24.96 | 75 | 2.5147 | 0.18 | 0.8746 | 6.7241 | 0.18 | 0.0306 | 0.2451 | 0.8494 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2