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---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dit-small_tobacco3482_kd_CEKD_t1.5_a0.5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dit-small_tobacco3482_kd_CEKD_t1.5_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: 2.8753
- Accuracy: 0.185
- Brier Loss: 0.8660
- Nll: 6.5533
- F1 Micro: 0.185
- F1 Macro: 0.0488
- Ece: 0.2451
- Aurc: 0.7363
## 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 | 3.1378 | 0.06 | 0.9042 | 9.2898 | 0.06 | 0.0114 | 0.1754 | 0.9032 |
| No log | 1.96 | 6 | 3.0447 | 0.18 | 0.8884 | 6.2145 | 0.18 | 0.0305 | 0.2294 | 0.8048 |
| No log | 2.96 | 9 | 2.9500 | 0.18 | 0.8761 | 6.9445 | 0.18 | 0.0305 | 0.2447 | 0.8193 |
| No log | 3.96 | 12 | 2.9328 | 0.18 | 0.8800 | 6.9512 | 0.18 | 0.0305 | 0.2565 | 0.8122 |
| No log | 4.96 | 15 | 2.9305 | 0.185 | 0.8793 | 6.9136 | 0.185 | 0.0488 | 0.2557 | 0.7823 |
| No log | 5.96 | 18 | 2.9286 | 0.185 | 0.8762 | 6.7762 | 0.185 | 0.0488 | 0.2533 | 0.7721 |
| No log | 6.96 | 21 | 2.9265 | 0.185 | 0.8731 | 5.9902 | 0.185 | 0.0488 | 0.2345 | 0.7682 |
| No log | 7.96 | 24 | 2.9240 | 0.185 | 0.8718 | 5.9696 | 0.185 | 0.0488 | 0.2625 | 0.7621 |
| No log | 8.96 | 27 | 2.9177 | 0.185 | 0.8707 | 5.9711 | 0.185 | 0.0488 | 0.2463 | 0.7578 |
| No log | 9.96 | 30 | 2.9129 | 0.185 | 0.8702 | 6.6932 | 0.185 | 0.0488 | 0.2485 | 0.7574 |
| No log | 10.96 | 33 | 2.9082 | 0.185 | 0.8704 | 6.7772 | 0.185 | 0.0488 | 0.2500 | 0.7560 |
| No log | 11.96 | 36 | 2.9039 | 0.185 | 0.8707 | 6.8060 | 0.185 | 0.0488 | 0.2464 | 0.7537 |
| No log | 12.96 | 39 | 2.8990 | 0.185 | 0.8704 | 6.7988 | 0.185 | 0.0488 | 0.2466 | 0.7515 |
| No log | 13.96 | 42 | 2.8933 | 0.185 | 0.8696 | 6.7771 | 0.185 | 0.0488 | 0.2505 | 0.7479 |
| No log | 14.96 | 45 | 2.8879 | 0.185 | 0.8688 | 6.7597 | 0.185 | 0.0488 | 0.2523 | 0.7482 |
| No log | 15.96 | 48 | 2.8840 | 0.185 | 0.8679 | 6.6825 | 0.185 | 0.0488 | 0.2648 | 0.7454 |
| No log | 16.96 | 51 | 2.8822 | 0.185 | 0.8676 | 6.6742 | 0.185 | 0.0488 | 0.2473 | 0.7425 |
| No log | 17.96 | 54 | 2.8819 | 0.185 | 0.8672 | 6.5521 | 0.185 | 0.0488 | 0.2479 | 0.7405 |
| No log | 18.96 | 57 | 2.8817 | 0.185 | 0.8671 | 6.5498 | 0.185 | 0.0488 | 0.2536 | 0.7385 |
| No log | 19.96 | 60 | 2.8797 | 0.185 | 0.8667 | 6.5563 | 0.185 | 0.0488 | 0.2442 | 0.7371 |
| No log | 20.96 | 63 | 2.8784 | 0.185 | 0.8666 | 6.6145 | 0.185 | 0.0488 | 0.2528 | 0.7374 |
| No log | 21.96 | 66 | 2.8770 | 0.185 | 0.8663 | 6.6084 | 0.185 | 0.0488 | 0.2489 | 0.7366 |
| No log | 22.96 | 69 | 2.8760 | 0.185 | 0.8662 | 6.5683 | 0.185 | 0.0488 | 0.2448 | 0.7360 |
| No log | 23.96 | 72 | 2.8756 | 0.185 | 0.8661 | 6.5544 | 0.185 | 0.0488 | 0.2450 | 0.7363 |
| No log | 24.96 | 75 | 2.8753 | 0.185 | 0.8660 | 6.5533 | 0.185 | 0.0488 | 0.2451 | 0.7363 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
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