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---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dit-tiny_tobacco3482_kd_CEKD_t1.5_a0.7
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_a0.7
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: 2.6280
- Accuracy: 0.18
- Brier Loss: 0.8747
- Nll: 6.7569
- F1 Micro: 0.18
- F1 Macro: 0.0306
- Ece: 0.2550
- Aurc: 0.8496
## 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 | 2.7961 | 0.145 | 0.8999 | 10.1560 | 0.145 | 0.0253 | 0.2221 | 0.8467 |
| No log | 1.96 | 6 | 2.7646 | 0.145 | 0.8946 | 10.5828 | 0.145 | 0.0253 | 0.2242 | 0.8475 |
| No log | 2.96 | 9 | 2.7185 | 0.155 | 0.8868 | 8.6137 | 0.155 | 0.0501 | 0.2145 | 0.8394 |
| No log | 3.96 | 12 | 2.6825 | 0.21 | 0.8808 | 6.5439 | 0.2100 | 0.0613 | 0.2567 | 0.8351 |
| No log | 4.96 | 15 | 2.6619 | 0.155 | 0.8778 | 6.7839 | 0.155 | 0.0274 | 0.2346 | 0.8880 |
| No log | 5.96 | 18 | 2.6517 | 0.18 | 0.8769 | 7.4578 | 0.18 | 0.0395 | 0.2461 | 0.8571 |
| No log | 6.96 | 21 | 2.6450 | 0.18 | 0.8767 | 7.1192 | 0.18 | 0.0308 | 0.2518 | 0.8516 |
| No log | 7.96 | 24 | 2.6400 | 0.18 | 0.8766 | 6.9539 | 0.18 | 0.0306 | 0.2472 | 0.8526 |
| No log | 8.96 | 27 | 2.6355 | 0.18 | 0.8762 | 6.9109 | 0.18 | 0.0306 | 0.2524 | 0.8527 |
| No log | 9.96 | 30 | 2.6332 | 0.18 | 0.8759 | 6.8997 | 0.18 | 0.0306 | 0.2491 | 0.8527 |
| No log | 10.96 | 33 | 2.6317 | 0.18 | 0.8757 | 6.8943 | 0.18 | 0.0306 | 0.2529 | 0.8524 |
| No log | 11.96 | 36 | 2.6309 | 0.18 | 0.8755 | 6.8287 | 0.18 | 0.0306 | 0.2442 | 0.8523 |
| No log | 12.96 | 39 | 2.6304 | 0.18 | 0.8753 | 6.7670 | 0.18 | 0.0306 | 0.2478 | 0.8521 |
| No log | 13.96 | 42 | 2.6298 | 0.18 | 0.8752 | 6.7597 | 0.18 | 0.0306 | 0.2433 | 0.8517 |
| No log | 14.96 | 45 | 2.6293 | 0.18 | 0.8751 | 6.7590 | 0.18 | 0.0306 | 0.2516 | 0.8513 |
| No log | 15.96 | 48 | 2.6290 | 0.18 | 0.8750 | 6.7556 | 0.18 | 0.0306 | 0.2555 | 0.8515 |
| No log | 16.96 | 51 | 2.6287 | 0.18 | 0.8750 | 6.7582 | 0.18 | 0.0306 | 0.2557 | 0.8514 |
| No log | 17.96 | 54 | 2.6289 | 0.18 | 0.8750 | 6.7556 | 0.18 | 0.0306 | 0.2476 | 0.8509 |
| No log | 18.96 | 57 | 2.6289 | 0.18 | 0.8750 | 6.7567 | 0.18 | 0.0306 | 0.2475 | 0.8505 |
| No log | 19.96 | 60 | 2.6285 | 0.18 | 0.8748 | 6.7567 | 0.18 | 0.0306 | 0.2433 | 0.8502 |
| No log | 20.96 | 63 | 2.6283 | 0.18 | 0.8748 | 6.7577 | 0.18 | 0.0306 | 0.2512 | 0.8500 |
| No log | 21.96 | 66 | 2.6281 | 0.18 | 0.8748 | 6.7586 | 0.18 | 0.0306 | 0.2551 | 0.8495 |
| No log | 22.96 | 69 | 2.6280 | 0.18 | 0.8747 | 6.7580 | 0.18 | 0.0306 | 0.2550 | 0.8496 |
| No log | 23.96 | 72 | 2.6280 | 0.18 | 0.8747 | 6.7573 | 0.18 | 0.0306 | 0.2550 | 0.8496 |
| No log | 24.96 | 75 | 2.6280 | 0.18 | 0.8747 | 6.7569 | 0.18 | 0.0306 | 0.2550 | 0.8496 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
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