--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_tobacco3482_kd_CEKD_t2.0_a0.5 results: [] --- # dit-tiny_tobacco3482_kd_CEKD_t2.0_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: 3.5976 - Accuracy: 0.18 - Brier Loss: 0.8781 - Nll: 6.8947 - F1 Micro: 0.18 - F1 Macro: 0.0306 - Ece: 0.2499 - Aurc: 0.8510 ## 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 | 3.8479 | 0.145 | 0.8999 | 10.1604 | 0.145 | 0.0253 | 0.2222 | 0.8467 | | No log | 1.96 | 6 | 3.8090 | 0.145 | 0.8946 | 10.5967 | 0.145 | 0.0253 | 0.2246 | 0.8470 | | No log | 2.96 | 9 | 3.7500 | 0.16 | 0.8866 | 8.6365 | 0.16 | 0.0406 | 0.2205 | 0.8486 | | No log | 3.96 | 12 | 3.7003 | 0.16 | 0.8805 | 6.5484 | 0.16 | 0.0327 | 0.2242 | 0.8816 | | No log | 4.96 | 15 | 3.6677 | 0.155 | 0.8776 | 6.7592 | 0.155 | 0.0271 | 0.2365 | 0.8919 | | No log | 5.96 | 18 | 3.6477 | 0.155 | 0.8770 | 7.2639 | 0.155 | 0.0278 | 0.2368 | 0.8961 | | No log | 6.96 | 21 | 3.6339 | 0.18 | 0.8774 | 7.3546 | 0.18 | 0.0313 | 0.2486 | 0.8556 | | No log | 7.96 | 24 | 3.6240 | 0.18 | 0.8781 | 7.0685 | 0.18 | 0.0308 | 0.2654 | 0.8528 | | No log | 8.96 | 27 | 3.6163 | 0.18 | 0.8784 | 7.0041 | 0.18 | 0.0306 | 0.2561 | 0.8532 | | No log | 9.96 | 30 | 3.6114 | 0.18 | 0.8787 | 6.9904 | 0.18 | 0.0306 | 0.2584 | 0.8537 | | No log | 10.96 | 33 | 3.6078 | 0.18 | 0.8788 | 6.9806 | 0.18 | 0.0306 | 0.2594 | 0.8538 | | No log | 11.96 | 36 | 3.6052 | 0.18 | 0.8789 | 6.9768 | 0.18 | 0.0306 | 0.2596 | 0.8537 | | No log | 12.96 | 39 | 3.6034 | 0.18 | 0.8788 | 6.9716 | 0.18 | 0.0306 | 0.2507 | 0.8532 | | No log | 13.96 | 42 | 3.6018 | 0.18 | 0.8786 | 6.9683 | 0.18 | 0.0306 | 0.2548 | 0.8527 | | No log | 14.96 | 45 | 3.6005 | 0.18 | 0.8786 | 6.9040 | 0.18 | 0.0306 | 0.2597 | 0.8524 | | No log | 15.96 | 48 | 3.5995 | 0.18 | 0.8784 | 6.8978 | 0.18 | 0.0306 | 0.2685 | 0.8518 | | No log | 16.96 | 51 | 3.5989 | 0.18 | 0.8784 | 6.8972 | 0.18 | 0.0306 | 0.2641 | 0.8515 | | No log | 17.96 | 54 | 3.5989 | 0.18 | 0.8784 | 6.8961 | 0.18 | 0.0306 | 0.2550 | 0.8513 | | No log | 18.96 | 57 | 3.5988 | 0.18 | 0.8784 | 6.8968 | 0.18 | 0.0306 | 0.2505 | 0.8510 | | No log | 19.96 | 60 | 3.5982 | 0.18 | 0.8782 | 6.8956 | 0.18 | 0.0306 | 0.2478 | 0.8511 | | No log | 20.96 | 63 | 3.5980 | 0.18 | 0.8782 | 6.8954 | 0.18 | 0.0306 | 0.2456 | 0.8507 | | No log | 21.96 | 66 | 3.5978 | 0.18 | 0.8782 | 6.8951 | 0.18 | 0.0306 | 0.2499 | 0.8511 | | No log | 22.96 | 69 | 3.5976 | 0.18 | 0.8781 | 6.8949 | 0.18 | 0.0306 | 0.2499 | 0.8510 | | No log | 23.96 | 72 | 3.5976 | 0.18 | 0.8781 | 6.8949 | 0.18 | 0.0306 | 0.2499 | 0.8510 | | No log | 24.96 | 75 | 3.5976 | 0.18 | 0.8781 | 6.8947 | 0.18 | 0.0306 | 0.2499 | 0.8510 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2