--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: dit-tiny_tobacco3482_kd_CEKD_t2.5_a0.5 results: [] --- # dit-tiny_tobacco3482_kd_CEKD_t2.5_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: 3.9560 - Accuracy: 0.18 - Brier Loss: 0.8800 - Nll: 6.8606 - F1 Micro: 0.18 - F1 Macro: 0.0306 - Ece: 0.2612 - Aurc: 0.8512 ## 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 | 4.2281 | 0.145 | 0.8999 | 10.1620 | 0.145 | 0.0253 | 0.2222 | 0.8467 | | No log | 1.96 | 6 | 4.1872 | 0.145 | 0.8946 | 10.5915 | 0.145 | 0.0253 | 0.2275 | 0.8468 | | No log | 2.96 | 9 | 4.1248 | 0.155 | 0.8866 | 8.6280 | 0.155 | 0.0360 | 0.2179 | 0.8487 | | No log | 3.96 | 12 | 4.0716 | 0.155 | 0.8806 | 6.5480 | 0.155 | 0.0272 | 0.2254 | 0.8851 | | No log | 4.96 | 15 | 4.0359 | 0.155 | 0.8778 | 6.7781 | 0.155 | 0.0271 | 0.2310 | 0.8931 | | No log | 5.96 | 18 | 4.0135 | 0.155 | 0.8774 | 7.8547 | 0.155 | 0.0271 | 0.2345 | 0.8965 | | No log | 6.96 | 21 | 3.9978 | 0.185 | 0.8779 | 8.3528 | 0.185 | 0.0468 | 0.2615 | 0.8612 | | No log | 7.96 | 24 | 3.9867 | 0.18 | 0.8789 | 7.6001 | 0.18 | 0.0308 | 0.2618 | 0.8546 | | No log | 8.96 | 27 | 3.9782 | 0.18 | 0.8796 | 7.0871 | 0.18 | 0.0306 | 0.2613 | 0.8538 | | No log | 9.96 | 30 | 3.9726 | 0.18 | 0.8800 | 7.0519 | 0.18 | 0.0306 | 0.2687 | 0.8545 | | No log | 10.96 | 33 | 3.9684 | 0.18 | 0.8803 | 7.0277 | 0.18 | 0.0306 | 0.2656 | 0.8537 | | No log | 11.96 | 36 | 3.9654 | 0.18 | 0.8805 | 7.0162 | 0.18 | 0.0306 | 0.2708 | 0.8536 | | No log | 12.96 | 39 | 3.9633 | 0.18 | 0.8805 | 7.0056 | 0.18 | 0.0306 | 0.2619 | 0.8535 | | No log | 13.96 | 42 | 3.9614 | 0.18 | 0.8804 | 6.9981 | 0.18 | 0.0306 | 0.2617 | 0.8532 | | No log | 14.96 | 45 | 3.9598 | 0.18 | 0.8804 | 6.9923 | 0.18 | 0.0306 | 0.2669 | 0.8531 | | No log | 15.96 | 48 | 3.9586 | 0.18 | 0.8803 | 6.9334 | 0.18 | 0.0306 | 0.2669 | 0.8529 | | No log | 16.96 | 51 | 3.9578 | 0.18 | 0.8802 | 6.9237 | 0.18 | 0.0306 | 0.2716 | 0.8522 | | No log | 17.96 | 54 | 3.9576 | 0.18 | 0.8802 | 6.8704 | 0.18 | 0.0306 | 0.2666 | 0.8521 | | No log | 18.96 | 57 | 3.9574 | 0.18 | 0.8802 | 6.8662 | 0.18 | 0.0306 | 0.2664 | 0.8523 | | No log | 19.96 | 60 | 3.9568 | 0.18 | 0.8801 | 6.8641 | 0.18 | 0.0306 | 0.2614 | 0.8518 | | No log | 20.96 | 63 | 3.9566 | 0.18 | 0.8801 | 6.8634 | 0.18 | 0.0306 | 0.2659 | 0.8516 | | No log | 21.96 | 66 | 3.9563 | 0.18 | 0.8800 | 6.8632 | 0.18 | 0.0306 | 0.2612 | 0.8516 | | No log | 22.96 | 69 | 3.9561 | 0.18 | 0.8800 | 6.8620 | 0.18 | 0.0306 | 0.2612 | 0.8513 | | No log | 23.96 | 72 | 3.9561 | 0.18 | 0.8800 | 6.8611 | 0.18 | 0.0306 | 0.2612 | 0.8513 | | No log | 24.96 | 75 | 3.9560 | 0.18 | 0.8800 | 6.8606 | 0.18 | 0.0306 | 0.2612 | 0.8512 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2