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---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dit-tiny_tobacco3482_kd_CEKD_t1.5_a0.9
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-tiny_tobacco3482_kd_CEKD_t1.5_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: 2.3286
- Accuracy: 0.18
- Brier Loss: 0.8742
- Nll: 6.7213
- F1 Micro: 0.18
- F1 Macro: 0.0306
- Ece: 0.2558
- Aurc: 0.8491
## 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 | 2.4683 | 0.145 | 0.8999 | 10.1538 | 0.145 | 0.0253 | 0.2220 | 0.8466 |
| No log | 1.96 | 6 | 2.4396 | 0.145 | 0.8947 | 10.5704 | 0.145 | 0.0253 | 0.2237 | 0.8463 |
| No log | 2.96 | 9 | 2.3985 | 0.145 | 0.8869 | 8.5511 | 0.145 | 0.0451 | 0.2116 | 0.8036 |
| No log | 3.96 | 12 | 2.3677 | 0.21 | 0.8810 | 6.5446 | 0.2100 | 0.0611 | 0.2566 | 0.8335 |
| No log | 4.96 | 15 | 2.3517 | 0.155 | 0.8780 | 6.8400 | 0.155 | 0.0279 | 0.2309 | 0.8894 |
| No log | 5.96 | 18 | 2.3450 | 0.18 | 0.8771 | 8.1897 | 0.18 | 0.0313 | 0.2495 | 0.8531 |
| No log | 6.96 | 21 | 2.3407 | 0.18 | 0.8767 | 7.3073 | 0.18 | 0.0306 | 0.2551 | 0.8513 |
| No log | 7.96 | 24 | 2.3371 | 0.18 | 0.8763 | 6.9328 | 0.18 | 0.0306 | 0.2501 | 0.8520 |
| No log | 8.96 | 27 | 2.3337 | 0.18 | 0.8757 | 6.8828 | 0.18 | 0.0306 | 0.2507 | 0.8525 |
| No log | 9.96 | 30 | 2.3321 | 0.18 | 0.8753 | 6.8682 | 0.18 | 0.0306 | 0.2508 | 0.8524 |
| No log | 10.96 | 33 | 2.3312 | 0.18 | 0.8751 | 6.7981 | 0.18 | 0.0306 | 0.2462 | 0.8521 |
| No log | 11.96 | 36 | 2.3309 | 0.18 | 0.8749 | 6.7375 | 0.18 | 0.0306 | 0.2531 | 0.8520 |
| No log | 12.96 | 39 | 2.3307 | 0.18 | 0.8748 | 6.7235 | 0.18 | 0.0306 | 0.2524 | 0.8518 |
| No log | 13.96 | 42 | 2.3304 | 0.18 | 0.8747 | 6.7200 | 0.18 | 0.0306 | 0.2482 | 0.8514 |
| No log | 14.96 | 45 | 2.3301 | 0.18 | 0.8746 | 6.7201 | 0.18 | 0.0306 | 0.2410 | 0.8509 |
| No log | 15.96 | 48 | 2.3298 | 0.18 | 0.8746 | 6.7182 | 0.18 | 0.0306 | 0.2449 | 0.8505 |
| No log | 16.96 | 51 | 2.3295 | 0.18 | 0.8745 | 6.7211 | 0.18 | 0.0306 | 0.2412 | 0.8500 |
| No log | 17.96 | 54 | 2.3297 | 0.18 | 0.8745 | 6.7201 | 0.18 | 0.0306 | 0.2449 | 0.8496 |
| No log | 18.96 | 57 | 2.3296 | 0.18 | 0.8745 | 6.7216 | 0.18 | 0.0306 | 0.2392 | 0.8494 |
| No log | 19.96 | 60 | 2.3292 | 0.18 | 0.8744 | 6.7214 | 0.18 | 0.0306 | 0.2371 | 0.8494 |
| No log | 20.96 | 63 | 2.3290 | 0.18 | 0.8744 | 6.7222 | 0.18 | 0.0306 | 0.2371 | 0.8493 |
| No log | 21.96 | 66 | 2.3288 | 0.18 | 0.8743 | 6.7227 | 0.18 | 0.0306 | 0.2408 | 0.8494 |
| No log | 22.96 | 69 | 2.3286 | 0.18 | 0.8743 | 6.7223 | 0.18 | 0.0306 | 0.2558 | 0.8490 |
| No log | 23.96 | 72 | 2.3286 | 0.18 | 0.8743 | 6.7218 | 0.18 | 0.0306 | 0.2558 | 0.8491 |
| No log | 24.96 | 75 | 2.3286 | 0.18 | 0.8742 | 6.7213 | 0.18 | 0.0306 | 0.2558 | 0.8491 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
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