--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-small_tobacco3482_kd_CEKD_t5.0_a0.5 results: [] --- # dit-small_tobacco3482_kd_CEKD_t5.0_a0.5 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: 3.7912 - Accuracy: 0.185 - Brier Loss: 0.8688 - Nll: 5.6106 - F1 Micro: 0.185 - F1 Macro: 0.0488 - Ece: 0.2524 - Aurc: 0.7391 ## 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 | 4.0715 | 0.06 | 0.9043 | 8.8976 | 0.06 | 0.0114 | 0.1751 | 0.9034 | | No log | 1.96 | 6 | 3.9774 | 0.18 | 0.8893 | 8.0316 | 0.18 | 0.0305 | 0.2237 | 0.8040 | | No log | 2.96 | 9 | 3.8805 | 0.18 | 0.8782 | 8.6752 | 0.18 | 0.0305 | 0.2566 | 0.8189 | | No log | 3.96 | 12 | 3.8615 | 0.18 | 0.8836 | 8.9177 | 0.18 | 0.0305 | 0.2645 | 0.8205 | | No log | 4.96 | 15 | 3.8624 | 0.185 | 0.8844 | 6.3245 | 0.185 | 0.0488 | 0.2727 | 0.7889 | | No log | 5.96 | 18 | 3.8605 | 0.185 | 0.8813 | 5.1679 | 0.185 | 0.0488 | 0.2558 | 0.7797 | | No log | 6.96 | 21 | 3.8511 | 0.185 | 0.8774 | 5.1770 | 0.185 | 0.0488 | 0.2510 | 0.7741 | | No log | 7.96 | 24 | 3.8410 | 0.185 | 0.8751 | 5.6014 | 0.185 | 0.0488 | 0.2458 | 0.7699 | | No log | 8.96 | 27 | 3.8317 | 0.185 | 0.8733 | 5.9766 | 0.185 | 0.0488 | 0.2537 | 0.7681 | | No log | 9.96 | 30 | 3.8259 | 0.185 | 0.8724 | 6.0278 | 0.185 | 0.0488 | 0.2473 | 0.7689 | | No log | 10.96 | 33 | 3.8226 | 0.185 | 0.8724 | 6.8070 | 0.185 | 0.0488 | 0.2618 | 0.7671 | | No log | 11.96 | 36 | 3.8209 | 0.185 | 0.8730 | 7.6044 | 0.185 | 0.0488 | 0.2539 | 0.7643 | | No log | 12.96 | 39 | 3.8187 | 0.185 | 0.8730 | 8.1654 | 0.185 | 0.0488 | 0.2542 | 0.7612 | | No log | 13.96 | 42 | 3.8147 | 0.185 | 0.8725 | 7.1073 | 0.185 | 0.0488 | 0.2542 | 0.7566 | | No log | 14.96 | 45 | 3.8096 | 0.185 | 0.8720 | 6.3875 | 0.185 | 0.0488 | 0.2565 | 0.7566 | | No log | 15.96 | 48 | 3.8052 | 0.185 | 0.8712 | 6.0256 | 0.185 | 0.0488 | 0.2518 | 0.7524 | | No log | 16.96 | 51 | 3.8022 | 0.185 | 0.8707 | 5.7809 | 0.185 | 0.0488 | 0.2558 | 0.7485 | | No log | 17.96 | 54 | 3.8008 | 0.185 | 0.8701 | 5.6835 | 0.185 | 0.0488 | 0.2496 | 0.7442 | | No log | 18.96 | 57 | 3.7992 | 0.185 | 0.8700 | 5.3867 | 0.185 | 0.0488 | 0.2490 | 0.7421 | | No log | 19.96 | 60 | 3.7965 | 0.185 | 0.8694 | 5.4928 | 0.185 | 0.0488 | 0.2478 | 0.7406 | | No log | 20.96 | 63 | 3.7948 | 0.185 | 0.8693 | 5.5527 | 0.185 | 0.0488 | 0.2481 | 0.7405 | | No log | 21.96 | 66 | 3.7932 | 0.185 | 0.8691 | 5.5585 | 0.185 | 0.0488 | 0.2564 | 0.7396 | | No log | 22.96 | 69 | 3.7921 | 0.185 | 0.8689 | 5.5607 | 0.185 | 0.0488 | 0.2479 | 0.7391 | | No log | 23.96 | 72 | 3.7915 | 0.185 | 0.8688 | 5.6116 | 0.185 | 0.0488 | 0.2523 | 0.7390 | | No log | 24.96 | 75 | 3.7912 | 0.185 | 0.8688 | 5.6106 | 0.185 | 0.0488 | 0.2524 | 0.7391 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2